Complete Data Science Training with Python for Data Analysis

Complete Data Science Training with Python for Data Analysis

Applied Statistical Modeling for Data Analysis in R

Applied Statistical Modeling for Data Analysis in R

Project Based Python Programming For Kids & Beginners

Project Based Python Programming For Kids & Beginners

ArcGIS Desktop For Spatial Analysis: Go From Basic To Pro

ArcGIS Desktop For Spatial Analysis: Go From Basic To Pro

Clustering & Classification With Machine Learning In R

Clustering & Classification With Machine Learning In R

Tensorflow and Keras For Neural Networks and Deep Learning

Tensorflow and Keras For Neural Networks and Deep Learning

Complete Deep Learning In R With Keras & Others

Complete Deep Learning In R With Keras & Others

Complete Data Wrangling & Data Visualisation In R

Complete Data Wrangling & Data Visualisation In R

The Fun and Easy Guide to Machine Learning using Keras

The Fun and Easy Guide to Machine Learning using Keras

Harness the Power of Machine Learning in R for Data/Text Mining, & Natural Language Processing with Practical Examples

MASTER DATA SCIENCE, TEXT MINING AND NATURAL LANGUAGE PROCESSING IN R:

Learn to carry out pre-processing, visualization and machine learning tasks such as: clustering, classification and regression in R. You will be able to mine insights from text data and Twitter to give yourself & your company a competitive edge.    

                              LEARN FROM AN EXPERT DATA SCIENTIST  WITH +5 YEARS OF EXPERIENCE:

My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation).
I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning.
This gives students an incomplete knowledge of the subject. Unlike other courses out there, we are not going to stop at machine learning. We will also cover data mining, web-scraping, text mining and natural language processing along with mining social media sites like Twitter and Facebook for text data.

                                 NO PRIOR R OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED:

You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R.
My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life. After taking this course, you’ll easily use packages like caret, dplyr to work with real data in R. You will also learn to use the common NLP packages to extract insights from text data.  
I will even introduce you to some very important practical case studies - such as detecting loan repayment and tumor detection using machine learning. You will also extract tweets pertaining to trending topics and analyze their underlying sentiments and identify topics with Latent Dirichlet allocation. With this Powerful All-In-One R Data Science course, you’ll know it all: visualization, stats, machine learning, data mining, and neural networks!  

The underlying motivation for the course is to ensure you can apply R based data science on real data into practice today. Start analyzing data for your own projects, whatever your skill level and Impress your potential employers with actual examples of your data science projects.

HERE IS WHAT YOU WILL GET:

(a) This course will take you from a basic level to performing some of the most common advanced data science techniques using the powerful R based tools.  

(b) Equip you to use R to perform the different exploratory and visualization tasks for data modelling.  

© Introduce you to some of the most important machine learning concepts in a practical manner such that you can apply these concepts for practical data analysis and interpretation.   (d) You will get a strong understanding of some of the most important data mining, text mining and natural language processing techniques.  

(e) & You will be able to decide which data science techniques are best suited to answer your research questions and applicable to your data and interpret the results.

More Specifically, here’s what’s covered in the course:

Getting started with R, R Studio and Rattle for implementing different data science techniques

Data Structures and Reading in Pandas, including CSV, Excel, JSON, HTML data.

How to Pre-Process and “Wrangle” your R data by removing NAs/No data, handling conditional data, grouping by attributes..etc

Creating data visualizations like histograms, boxplots, scatterplots, barplots, pie/line charts, and MORE

Statistical analysis, statistical inference, and the relationships between variables.

Machine Learning, Supervised Learning, & Unsupervised Learning in R

Neural Networks for Classification and Regression

Web-Scraping using R

Extracting text data from Twitter and Facebook using APIs

Text mining

Common Natural Language Processing techniques such as sentiment analysis and topic modelling

We will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different techniques on real data and interpret the results.

After each video you will learn a new concept or technique which you may apply to your own projects.

All the data and code used in the course has been made available free of charge and you can use it as you like. You will also have access to additional lectures that are added in the future for FREE.

JOIN THE COURSE NOW!

Who this course is for:

• Students wishing to learn practical data science and machine learning in R
• Students wishing to learn the underlying theory and application of data mining in R
• Students interested in obtaining/mining data from sources such as Twiter
• Students interested in pre-processing and visualizing real life data
• Students wishing to analyze and derive insights from text data
• Students interested in learning basic text mining and Natural Language Processing (NLP) in R

94% off !!! #udemy #course for
#Data #Science :Data Mining & Natural Language Processing in R
Harness the Power of Machine Learning in R for Data/Text Mining, & Natural Language Processing with Practical Examples
#coupon #deal
https://www.udemy.com/data-science-datamining-natural-language-processing-in-r/?couponCode=DATAMINE1

Complete Guide to Practical Data Science with Python: Learn Statistics, Visualization, Machine Learning & More

THIS IS A COMPLETE DATA SCIENCE TRAINING WITH PYTHON FOR DATA ANALYSIS:

It’s A Full 12-Hour Python Data Science BootCamp To Help You Learn Statistical Modelling, Data Visualization, Machine Learning & Basic Deep Learning In Python!

HERE IS WHY YOU SHOULD TAKE THIS COURSE:

First of all, this course a complete guide to practical data science using Python…

That means, this course covers ALL the aspects of practical data science and if you take this course alone, you can do away with taking other courses or buying books on Python based data science.  

In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal. By storing, filtering, managing, and manipulating data in Python, you can give your company a competitive edge & boost your career to the next level!

THIS IS MY PROMISE TO YOU:

COMPLETE THIS ONE COURSE & BECOME A PRO IN PRACTICAL PYTHON BASED DATA SCIENCE!

But, first things first, My name is MINERVA SINGH and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation).

I have several years of experience in analyzing real life data from different sources  using data science related techniques and producing publications for international peer reviewed journals.

Over the course of my research I realized almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning…

This gives student an incomplete knowledge of the subject. This course will give you a robust grounding in all aspects of data science, from statistical modeling to visualization to machine learning.

Unlike other Python instructors, I dig deep into the statistical modeling features of Python and gives you a one-of-a-kind grounding in Python Data Science!

You will go all the way from carrying out simple visualizations and data explorations to statistical analysis to machine learning to finally implementing simple deep learning based models using Python

DISCOVER 12 COMPLETE SECTIONS ADDRESSING EVERY ASPECT OF PYTHON DATA SCIENCE (INCLUDING):

• A full introduction to Python Data Science and powerful Python driven framework for data science, Anaconda
• Getting started with Jupyter notebooks for implementing data science techniques in Python
• A comprehensive presentation about basic analytical tools- Numpy Arrays, Operations, Arithmetic, Equation-solving, Matrices, Vectors, Broadcasting, etc.
• Data Structures and Reading in Pandas, including CSV, Excel, JSON, HTML data
• How to Pre-Process and “Wrangle” your Python data by removing NAs/No data, handling conditional data, grouping by attributes, etc.
• Creating data visualizations like histograms, boxplots, scatterplots, barplots, pie/line charts, and more!
• Statistical analysis, statistical inference, and the relationships between variables
• Machine Learning, Supervised Learning, Unsupervised Learning in Python
• You’ll even discover how to create artificial neural networks and deep learning structures…& MUCH MORE!

With this course, you’ll have the keys to the entire Python Data Science kingdom!

NO PRIOR PYTHON OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED:

You’ll start by absorbing the most valuable Python Data Science basics and techniques…

I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in Python.

My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement Python based data science in real life.

After taking this course, you’ll easily use packages like Numpy, Pandas, and Matplotlib to work with real data in Python.

You’ll even understand deep concepts like statistical modeling in Python’s Statsmodels package and the difference between statistics and machine learning (including hands-on techniques).

I will even introduce you to deep learning and neural networks using the powerful H2o framework!

With this Powerful All-In-One Python Data Science course, you’ll know it all: visualization, stats, machine learning, data mining, and deep learning!

The underlying motivation for the course is to ensure you can apply Python based data science on real data and put into practice today. Start analyzing  data for your own projects, whatever your skill level and IMPRESS your potential employers with actual examples of your  data science abilities.

HERE IS WHAT THIS COURSE WILL DO FOR YOU:

This course is your one shot way of acquiring the knowledge of statistical data analysis skills that I acquired from the rigorous training received at two of the best universities in the world, perusal of numerous books and publishing statistically rich papers in renowned international journal like PLOS One.

This course will:

  (a) Take students without a prior Python and/or statistics background background from a basic level to performing some of the most common advanced data science techniques using the powerful Python based Jupyter notebooks.

  (b) Equip students to use Python for performing different statistical data analysis and visualization tasks for data modelling.

  © Introduce some of the most important statistical and machine learning concepts to students in a practical manner such that students can apply these concepts for practical data analysis and interpretation.

  (d) Students will get a strong background in some of the most important data science techniques.

  (e) Students will be able to decide which data science techniques are best suited to answer their research questions and applicable to their data and interpret the results.

It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different  techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects.

JOIN THE COURSE NOW!

Who this course is for:

• Anyone Who Wishes To Learn Practical Data Science Using Python
• Anyone Interested In Learning How To Implement Machine Learning Algorithms Using Python
• People Looking To Get Started In Deep Learning Using Python
• People Looking To Work With Real Life Data In Python
• Anyone With A Prior Knowledge Of Python Looking To Branch Out Into Data Analysis
• Anyone Looking To Become Proficient In Exploratory Data Analysis, Statistical Modelling & Visualizations Using iPython

Complete Data #Science Training with #Python for #Data Analysis Online #course now 95% discounted check #coupon
https://www.udemy.com/complete-data-science-training-with-python-for-data-analysis/?couponCode=PYTHON_DS10

Harness The Power Of Machine Learning For Unsupervised & Supervised Learning In R -- With Practical Examples

HERE IS WHY YOU SHOULD TAKE THIS COURSE:

This course your complete guide to both supervised & unsupervised learning using R…

That means, this course covers all the main aspects of practical data science and if you take this course, you can do away with taking other courses or buying books on R based data science.

In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in unsupervised & supervised learning in R, you can give your company a competitive edge and boost your career to the next level.

LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE:

My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University.

I have +5 years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals.

Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic…

This course will give you a robust grounding in the main aspects of machine learning- clustering & classification.

Unlike other R instructors, I dig deep into the machine learning features of R and gives you a one-of-a-kind grounding in  Data Science!

You will go all the way from carrying out data reading & cleaning  to machine learning to finally implementing powerful machine learning algorithms and evaluating their performance using R.

THIS COURSE HAS 8 SECTIONS COVERING EVERY ASPECT OF R MACHINE LEARNING:

• A full introduction to the R Framework for data science

• Data Structures and Reading in R, including CSV, Excel and HTML data

• How to Pre-Process and “Clean” data by removing NAs/No data,visualization

• Machine Learning, Supervised Learning, Unsupervised Learning in R

• Model building and selection…& MUCH MORE!

By the end of the course, you’ll have the keys to the entire R Machine Learning Kingdom!

NO PRIOR R OR STATISTICS/MACHINE LEARNING KNOWLEDGE REQUIRED:

You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R.

My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life.

After taking this course, you’ll easily use data science packages like caret to work with real data in R…

You’ll even understand concepts like unsupervised learning, dimension reduction and supervised learning. Again, we’ll work with real data and you will have access to all the code and data used in the course.

JOIN MY COURSE NOW!

Who this course is for:

• Students Interested In Getting Started With Data Science Applications In The R & R Studio Environment
• Students Wishing To Learn The Implementation Of Unsupervised Learning On Real Data
• Students Wishing To Learn The Implementation Of Supervised Learning (Classification) On Real Data Using R

92% discount #coupon #udemy #course for
#Clustering & #Classification With #Machine #Learning In R
#couponcode
https://www.udemy.com/clustering-classification-with-machine-learning-in-r/

Harness The Power Of Machine Learning For Unsupervised & Supervised Learning In R -- With Practical Examples

HERE IS WHY YOU SHOULD TAKE THIS COURSE:

This course your complete guide to both supervised & unsupervised learning using R…

That means, this course covers all the main aspects of practical data science and if you take this course, you can do away with taking other courses or buying books on R based data science.

In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in unsupervised & supervised learning in R, you can give your company a competitive edge and boost your career to the next level.

LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE:

My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University.

I have +5 years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals.

Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic…

This course will give you a robust grounding in the main aspects of machine learning- clustering & classification.

Unlike other R instructors, I dig deep into the machine learning features of R and gives you a one-of-a-kind grounding in  Data Science!

You will go all the way from carrying out data reading & cleaning  to machine learning to finally implementing powerful machine learning algorithms and evaluating their performance using R.

THIS COURSE HAS 8 SECTIONS COVERING EVERY ASPECT OF R MACHINE LEARNING:

• A full introduction to the R Framework for data science

• Data Structures and Reading in R, including CSV, Excel and HTML data

• How to Pre-Process and “Clean” data by removing NAs/No data,visualization

• Machine Learning, Supervised Learning, Unsupervised Learning in R

• Model building and selection…& MUCH MORE!

By the end of the course, you’ll have the keys to the entire R Machine Learning Kingdom!

NO PRIOR R OR STATISTICS/MACHINE LEARNING KNOWLEDGE REQUIRED:

You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R.

My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life.

After taking this course, you’ll easily use data science packages like caret to work with real data in R…

You’ll even understand concepts like unsupervised learning, dimension reduction and supervised learning. Again, we’ll work with real data and you will have access to all the code and data used in the course.

JOIN MY COURSE NOW!

Who this course is for:

• Students Interested In Getting Started With Data Science Applications In The R & R Studio Environment
• Students Wishing To Learn The Implementation Of Unsupervised Learning On Real Data
• Students Wishing To Learn The Implementation Of Supervised Learning (Classification) On Real Data Using R

92% discount #coupon #udemy #course for
#Clustering & #Classification With #Machine #Learning In R
#couponcode
https://www.udemy.com/clustering-classification-with-machine-learning-in-r/

Master the Most Important Deep Learning Frameworks (Tensorflow & Keras) for Python Data Science

THIS IS A COMPLETE NEURAL NETWORKS & DEEP LEARNING TRAINING WITH TENSORFLOW & KERAS IN PYTHON!

It is a full 7-Hour Python Tensorflow & Keras Neural Network & Deep Learning Boot Camp that will help you learn basic machine learning, neural networks and deep learning  using two of the most important Deep Learning frameworks- Tensorflow and Keras.                        

HERE IS WHY YOU SHOULD ENROLL IN THIS COURSE:

This course is your complete guide to practical machine & deep learning using the Tensorflow & Keras framework in Python..

This means, this course covers the important aspects of Keras and Tensorflow (Google’s powerful Deep Learning framework) and if you take this course, you can do away with taking other courses or buying books on Python Tensorflow and Keras based data science.  

In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal and advent of Tensorflow and Keras is revolutionizing Deep Learning…

By gaining proficiency in Keras and and Tensorflow, you can give your company a competitive edge and boost your career to the next level.

THIS IS MY PROMISE TO YOU: COMPLETE THIS ONE COURSE & BECOME A PRO IN PRACTICAL KERAS & TENSORFLOW BASED DATA SCIENCE!

But first things first. My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation).

I have several years of experience in analyzing real life data from different sources  using data science related techniques and producing publications for international peer reviewed journals.

Over the course of my research I realized almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning..

This gives students an incomplete knowledge of the subject. My course, on the other hand, will give you a robust grounding in all aspects of data science within the Tensorflow framework.

Unlike other Python courses, we dig deep into the statistical modeling features of Tensorflow & Keras and give you a one-of-a-kind grounding in these frameworks!

DISCOVER 8 COMPLETE SECTIONS ADDRESSING EVERY ASPECT OF PYTHON BASED TENSORFLOW DATA SCIENCE:

• A full introduction to Python Data Science and powerful Python driven framework for data science, Anaconda
• Getting started with Jupyter notebooks for implementing data science techniques in Python
• A comprehensive presentation about Tensorflow & Keras installation and a brief introduction to the other Python data science packages
• Brief introduction to the working of Pandas and Numpy
• The basics of the Tensorflow syntax and graphing environment
• The basics of the Keras syntax
• Machine Learning, Supervised Learning, Unsupervised Learning in the Tensorflow & Keras frameworks
• You’ll even discover how to create artificial neural networks and deep learning structures with Tensorflow & Keras

BUT,  WAIT! THIS ISN’T JUST ANY OTHER DATA SCIENCE COURSE:

You’ll start by absorbing the most valuable Python Tensorflow and Keras basics and techniques.

I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts.

My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement Python based data science in real -life.

After taking this course, you’ll easily use packages like Numpy, Pandas, and Matplotlib to work with real data in Python along with gaining fluency in Tensorflow and Keras. I will even introduce you to deep learning models such as Convolution Neural network (CNN) !!

The underlying motivation for the course is to ensure you can apply Python based data science on real data into practice today, start analyzing  data for your own projects whatever your skill level, and impress your potential employers with actual examples of your data science abilities.

This course will take students without a prior Python and/or statistics background background from a basic level to performing some of the most common advanced data science techniques using the powerful Python based Jupyter notebooks

It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different  techniques on real data and interpret the results..

After each video you will learn a new concept or technique which you may apply to your own projects!

JOIN THE COURSE NOW!

Who this course is for:

• People Interested In Learning Python Based Tensorflow and Keras For Data Science Applications
• People With Prior Exposure To Python Programming &/Or Data Science Concepts
• People Interested In Implementing Neural Networks & Deep Learning Models With Tensorflow
• People Interested In Implementing Neural Networks & Deep Learning Models With Keras.

92% discount #coupon #udemy #course for
#Tensorflow and Keras For Neural Networks and #Deep #Learning
#couponcode
https://www.udemy.com/tensorflow-and-keras-for-neural-networks-and-deep-learning/

Deep Learning: Master Powerful Deep Learning Tools in R Like Keras, Mxnet, H2O and Others

YOUR COMPLETE GUIDE TO ARTIFICIAL NEURAL NETWORKS & DEEP LEARNING IN R:      

This course covers the main aspects of neural networks and deep learning. If you take this course, you can do away with taking other courses or buying books on R based data science.

In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in neural networks and deep learning in R, you can give your company a competitive edge and boost your career to the next level!

LEARN FROM AN EXPERT DATA SCIENTIST:

My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University.

I have +5 years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals.

Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic .

This course will give you a robust grounding in the main aspects of practical neural networks and deep learning.

Unlike other R instructors, I dig deep into the data science features of R and give you a one-of-a-kind grounding in data science…

You will go all the way from carrying out data reading & cleaning  to to finally implementing powerful neural networks and deep learning algorithms and evaluating their performance using R.

Among other things:

You will be introduced to powerful R-based deep learning packages such as h2o and MXNET.

You will be introduced to deep neural networks (DNN), convolution neural networks (CNN) and unsupervised methods.

You will learn how to implement convolutional neural networks (CNN)s on imagery data using the Keras framework

You will learn to apply these frameworks to real life data including credit card fraud data, tumor data, images among others for classification and regression applications.  

With this course, you’ll have the keys to the entire R Neural Networks and Deep Learning Kingdom!

NO PRIOR R OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED:

You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R.

My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real-life.

After taking this course, you’ll easily use data science packages like caret, h2o, mxnet, keras to implement novel deep learning techniques in R. You will get your hands dirty with real life data, including real-life imagery data which you will learn to pre-process and model

You’ll even understand the underlying concepts to understand what algorithms and methods are best suited for your data.

We will also work with real data and you will have access to all the code and data used in the course.

JOIN MY COURSE NOW!

Who this course is for:

• People Wanting To Master The R & R Studio Environment For Data Science
• Anyone With Prior Exposure To Common Machine Learning Concepts Such As Supervised Learning
• Students Wishing To Learn The Implementation Of Neural Networks On Real Data In R
• Students Wishing To Learn The Implementation Of Basic Deep Learning Concepts In R

92% discount #coupon #udemy #course for
Complete #Deep #Learning In R With Keras & Others
#couponcode
https://www.udemy.com/complete-deep-learning-in-r-with-keras-others/

Learn Data Preprocessing, Data Wrangling and Data Visualisation For Practical Data Science Applications in R

THIS IS YOUR ROADMAP TO LEARNING & BECOMING HIGHLY PROFICIENT IN DATA PREPROCESSING, DATA WRANGLING, & DATA VISUALIZATION IN R!

Hello, My name is Minerva Singh. I am an Oxford University MPhil graduate in Geography & Environment & I  finished a PhD at Cambridge University in Tropical Ecology & Conservation.

I have +5 of experience in analyzing real-life data from different sources using statistical modelling and producing publications for international peer-reviewed journals. If you find statistics books & manuals too vague, expensive & not practical, then you’re going to love this course!

I created this course to take you by hand and teach you all the concepts, and tackle the most fundamental building block on practical data science - data wrangling and visualisation.

THIS COURSE WILL TEACH YOU ALL YOU NEED AND PUT YOUR KNOWLEDGE TO PRACTICE NOW!

This course is your sure-fire way of acquiring the knowledge and statistical data analysis wrangling and visualisation skills that I acquired from the rigorous training I received at 2 of the best universities in the world, the perusal of numerous books and publishing statistically rich papers in the renowned international journal like PLOS One.

HERE IS WHAT THIS COURSE WILL DO FOR YOU:

It will take you (even if you have no prior statistical modelling/analysis background) from a basic level of performing some of the most common data wrangling tasks in R.

It will equip you to use some of the most important R data wrangling and visualisation packages such as dplyr and ggplot2.

It will Introduce some of the most important data visualisation concepts to you in a practical manner such that you can apply these concepts for practical data analysis and interpretation.

You will also be able to decide which wrangling and visualisation techniques are best suited to answer your research questions and applicable to your data and interpret the results..

The course will mostly focus on helping you implement different techniques on real-life data such as Olympic and Nobel Prize winners

After each video, you will learn a new concept or technique which you may apply to your own projects immediately! Reinforce your knowledge through practical quizzes and assignments.

ON TOP OF THE COURSE, I’M ALSO OFFERING YOU:

Practice Activities To Reinforce Your Learning

My Continuous Support To Make Sure You Gain Complete Understanding & Proficiency

Access To Future Course Updates Free Of Charge

I’ll Even Go The Extra Mile & Cover Any Topics That Are Related To The Subject That You Need Help With (This is something you can’t get anywhere else).

& Access To A Community Of 25,000 Data Scientists (& growing) All Learning Together & Helping Each Other!

Now, go ahead & enrol in the course. I’m certain you’ll love it, but in case you don’t, you can always request a refund within 30 days. No hard feelings whatsoever. I look forward to seeing you inside!

Who this course is for:

• Students Interested In Getting Started With Data Science Applications In The R & R Studio Environment
• Students Interested in Learning About the Common Pre-processing Data Tasks
• Students Interested in Gaining Exposure to Common R Packages Such As ggplot2
• Those Interested in Learning About Different Kinds of Data Visualisations
• Those Interested in Learning to Create Publication Quality Visualisations

92% discount #coupon #udemy #course for
Complete Data #Wrangling & #Data #Visualisation In R
#couponcode
https://www.udemy.com/complete-data-wrangling-data-visualization-with-r/

Learn 16 Machine Learning Algorithms in a Fun and Easy along with Practical Python Labs using Keras

Welcome to the Fun and Easy Machine learning Course in Python and Keras.
 

Are you Intrigued by the field of Machine Learning? Then this course is for you! We will take you on an adventure into the amazing of field Machine Learning. Each section consists of fun and intriguing white board explanations with regards to important concepts in Machine learning as well as practical python labs which you will enhance your comprehension of this vast yet lucrative sub-field of Data Science.

So Many Machine Learning Courses Out There, Why This One?

This is a valid question and the answer is simple. This is the ONLY course on Udemy which will get you implementing some of the most common machine learning algorithms on real data in Python. Plus, you will gain exposure to neural networks (using the H2o framework) and some of the most common deep learning algorithms with the Keras package.

We designed this course for anyone who wants to learn the state of the art in Machine learning in a simple and fun way without learning complex math or boring explanations.  Each theoretically lecture is uniquely designed using whiteboard animations which can maximize engagement in the lectures and improves knowledge retention. This ensures that you absorb more content than you would traditionally would watching other theoretical videos and or books on this subject.

What you will Learn in this Course

This is how the course is structured:

Regression – Linear Regression, Decision Trees, Random Forest Regression,

Classification – Logistic Regression, K Nearest Neighbors (KNN), Support Vector Machine (SVM) and Naive Bayes,

Clustering - K-Means, Hierarchical Clustering,

Association Rule Learning - Apriori, Eclat,

Dimensionality Reduction - Principle Component Analysis, Linear Discriminant  Analysis,

Neural Networks - Artificial Neural Networks, Convolution Neural Networks, Recurrent Neural Networks.

Practical Lab Structure
You DO NOT need any prior Python or Statistics/Machine Learning Knowledge to get Started. The course will start by introducing students to one of the most fundamental statistical data analysis models and its practical implementation in Python- ordinary least squares (OLS) regression. Subsequently some of the most common machine learning regression and classification techniques such as random forests, decision trees and linear discriminant analysis will be covered. In addition to providing a theoretical foundation for these, hands-on practical labs will demonstrate how to implement these in Python. Students will also be introduced to the practical applications of common data mining techniques in Python and gain proficiency in using a powerful Python based framework for machine learning which is Anaconda (Python Distribution). Finally you will get a solid grounding in both Artificial Neural Networks (ANN) and the Keras package for implementing deep learning algorithms such as the Convolution Neural Network (CNN). Deep Learning is an in-demand topic and a knowledge of this will make you more attractive to employers.

Excited Yet?

So as you can see you are going to be learning to build a lot of impressive Machine Learning apps in this 3 hour course. The underlying motivation for the course is to ensure you can apply Python based data science on real data into practice today. Start analyzing  data for your own projects, whatever your skill level and IMPRESS your potential employers with an actual examples of your  machine learning abilities.

It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different  techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects.

TAKE ACTION TODAY! We will personally support you and ensure your experience with this course is a success. And for any reason you are unhappy with this course, Udemy has a 30 day Money Back Refund Policy, So no questions asked, no quibble and no Risk to you. You got nothing to lose. Click that enroll button and we’ll see you in side the course.

Who this course is for:

• Student who starting out or interested in Machine Learning or Deep Learning.
• Students with Prior Python Programming Exposure Who Want to Use it for Machine Learning
• Students interested in gaining exposure to the Keras library for Deep Learning.
• Data analysts who want to expand into Machine Learning.
• College students who want to start a career in Data Science.

92% discount #coupon #udemy #course for
The Fun and Easy Guide to #Machine #Learning using #Keras
#couponcode
https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/

Clustering & Classification With Machine Learning In Python

HERE IS WHY YOU SHOULD TAKE THIS COURSE:

This course your complete guide to both supervised & unsupervised learning using Python. This means, this course covers all the main aspects of practical data science and if you take this course, you can do away with taking other courses or buying books on Python based data science.

In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal..

By becoming proficient in unsupervised & supervised learning in Python, you can give your company a competitive edge and boost your career to the next level.

LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE:

My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I also just recently finished a PhD at Cambridge University.

I have several years of experience in analyzing real life data from different sources  using data science techniques and producing publications for international peer reviewed journals.

Over the course of my research I realized almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic .

This course will give you a robust grounding in the main aspects of machine learning- clustering & classification.

Unlike other Python instructors, I dig deep into the machine learning features of Python and gives you a one-of-a-kind grounding in Python Data Science!

You will go all the way from carrying out data reading & cleaning  to machine learning to finally implementing simple deep learning based models using Python

THE COURSE COMPOSES OF 7 SECTIONS TO HELP YOU MASTER PYTHON MACHINE LEARNING:

• A full introduction to Python Data Science and powerful Python driven framework for data science, Anaconda • Getting started with Jupyter notebooks for implementing data science techniques in Python  • Data Structures and Reading in Pandas, including CSV, Excel and HTML data • How to Pre-Process and “Wrangle” your Python data by removing NAs/No data, handling conditional data, grouping by attributes, etc.

• Machine Learning, Supervised Learning, Unsupervised Learning in Python

• Artificial neural networks (ANN) and Deep Learning. You’ll even discover how to use artificial neural networks and deep learning structures for classification!

With such a rigorous grounding in so many topics, you will be an unbeatable data scientist by the end of the course.

NO PRIOR PYTHON OR STATISTICS OR MACHINE LEARNING KNOWLEDGE IS REQUIRED:

You’ll start by absorbing the most valuable Python Data Science basics and techniques.

I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in Python.

My course will help you implement the methods using real data obtained from different sources.

After taking this course, you’ll easily use packages like Numpy, Pandas, and Matplotlib to work with real data in Python..

You’ll even understand concepts like unsupervised learning, dimension reduction and supervised learning.. I will even introduce you to deep learning and neural networks using the powerful H2o framework!

Most importantly, you will learn to implement these techniques practically using Python. You will have access to all the data and scripts used in this course. Remember, I am always around to support my students!

JOIN MY COURSE NOW!

Who this course is for:

• Students Interested In Getting Started With Data Science Applications In The Python Environment
• People Wanting To Master The Anaconda iPython Environment For Data Science & Scientific Computations
• Students Wishing To Learn The Implementation Of Unsupervised Learning On Real Data Using Python
• Students Wishing To Learn The Implementation Of Supervised Learning (Classification) On Real Data Using Python
• Students Looking To Get Started With Artificial Neural Networks & Deep Learning

92% discount #coupon #udemy #course for
#Clustering & #Classification With #Machine Learning In #Python
#couponcode
https://www.udemy.com/clustering-classification-with-machine-learning-in-python/

Your Complete Guide to Statistical Data Analysis and Visualization For Practical Applications in R

APPLIED STATISTICAL MODELING FOR DATA ANALYSIS IN R

COMPLETE GUIDE TO STATISTICAL DATA ANALYSIS & VISUALIZATION FOR PRACTICAL APPLICATIONS IN R

            Confounded by Confidence Intervals? Pondering Over p-values? Hankering Over Hypothesis Testing?

Hello, My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation).

I have several years of experience in analyzing real life data from different sources using statistical modeling and producing publications for international peer reviewed journals. If you find statistics books & manuals too vague, expensive & not practical, then you’re going to love this course!

I created this course to take you by hand and teach you all the concepts, and take your statistical modeling from basic to an advanced level for practical data analysis.

With this course, I want to help you save time and learn what the arcane statistical concepts have to do with the actual analysis of data and the interpretation of the bespoke results. Frankly, this is the only one course you need to complete  in order to get a head start in practical statistical modeling for data analysis using R.

My course has 9.5 hours of lectures and provides a robust foundation to carry out PRACTICAL, real-life statistical data analysis tasks in R, one of the most popular and FREE data analysis frameworks.

GET ACCESS TO A COURSE THAT IS JAM PACKED WITH TONS OF APPLICABLE INFORMATION! AND GET A FREE VIDEO COURSE IN MACHINE LEARNING AS WELL!

This course is your sure-fire way of acquiring the knowledge and statistical data analysis skills that I acquired from the rigorous training I received at 2 of the best universities in the world, perusal of numerous books and publishing statistically rich papers in renowned international journal like PLOS One.

To be more specific, here’s what the course will do for you:

 (a) It will take you (even if you have no prior statistical modelling/analysis background) from a basic level to performing some of the most common advanced statistical data analysis tasks in R.

 (b) It will equip you to use R for performing the different statistical data analysis and visualization tasks for data modelling.

 © It will Introduce some of the most important statistical concepts to you in a practical manner such that you can apply these concepts for practical data analysis and interpretation.

 (d) You will learn some of the most important statistical modelling concepts from probability distributions to hypothesis testing to regression modelling and multivariate analysis.

 (e) You will also be able to decide which statistical modelling techniques are best suited to answer your research questions and applicable to your data and interpret the results.

The course will mostly focus on helping you implement different statistical analysis techniques on your data and interpret the results.

After each video you will learn a new concept or technique which you may apply to your own projects immediately!

TAKE ACTION NOW :) You’ll also have my continuous support when you take this course just to make sure you’re successful with it.  If my GUARANTEE is not enough for you, you can ask for a refund within 30 days of your purchase in case you’re not completely satisfied with the course.

TAKE ACTION TODAY! I will personally support you and ensure your experience with this course is a success.

Who this course is for:

• People working in any numerate field which requires data analysis
• Students of Environmental Science, Ecology, Biology,Conservation and Other Natural Sciences
• People with some prior knowledge of the R interface- (a) installing packages (b) reading in csv files
• People carrying out observational or experimental studies

#Applied #Statistical Modeling for #Data #Analysis in R !!! 85% off!! #umedy #course #couponcode
https://www.udemy.com/applied-statistical-modeling-for-data-analysis-in-r/?couponCode=STATSINTRO_30

Satellite Remote Sensing Data Bootcamp With Opensource Tools

ENROLL IN MY LATEST COURSE ON HOW TO LEARN ALL ABOUT BASIC SATELLITE REMOTE SENSING.

Are you currently enrolled in either of my Core or Intermediate Spatial Data Analysis Courses?

Or perhaps you have prior experience in GIS or tools like R and QGIS?

You don’t want to spend 100s and 1000s of dollars on buying commercial software for imagery analysis?

The next step for you is to gain profIciency in satellite remote sensing data analysis.

MY COURSE IS A HANDS ON TRAINING WITH REAL REMOTE SENSING DATA WITH OPEN SOURCE TOOLS!

My course provides a foundation to carry out PRACTICAL, real-life remote sensing analysis tasks in popular and FREE software frameworks with REAL spatial data. By taking this course, you are taking an important step forward in your GIS journey to become an expert in geospatial analysis.

Why Should You Take My Course?

I am an Oxford University MPhil (Geography and Environment) graduate. I also completed a PhD at Cambridge University (Tropical Ecology and Conservation).

I have several years of experience in analyzing real life spatial remote sensing data from different sources and producing publications for international peer reviewed journals.

In this course, actual satellite remote sensing data such as Landsat from USGS and radar data from JAXA  will be used to give a practical hands-on experience of working with remote sensing and understanding what kind of questions remote  sensing can help us answer.

This course will ensure you learn & put remote sensing data analysis into practice today and increase your proficiency in geospatial analysis.

Remote sensing software tools are very expensive and their cost can run into thousands of dollars. Instead of shelling out so much money or procuring pirated copies (which puts you at a risk of prosecution), you will learn to carry out some of the most important and common remote sensing analysis tasks using a number of popular, open source GIS tools such as R, QGIS, GRASS and ESA-SNAP.  All of which are in great demand in the geospatial sector and improving your skills in these is a plus for you.

This is an introductory course, i.e. we will focus on learning the most important and widely encountered remote sensing data processing and analyzing tasks in R, QGIS, GRASS and ESA-SNAP

You will also learn about the different sources of remote sensing data there are and how to obtain these FREE OF CHARGE and process them using FREE SOFTWARE.

In addition to all the above, you’ll have MY CONTINUOUS SUPPORT to make sure you get the most value out of your investment!

ENROLL NOW :)

Who this course is for:

• People with prior expereince of working spatial data
• GIS analysts
• Ecologists
• Forestry and Conservation Practioners
• Geographers
• Geologists

92% off #udemy course #couponcode
#Satellite #Remote Sensing Data #Bootcamp With #Opensource Tools
https://www.udemy.com/satellite-remote-sensing-data-bootcamp-with-opensource-tools/?couponCode=REMOTESENSING_15

Spatial Data Analysis With ArcGIS Desktop: Master GIS Techniques and Open Doors to Amazing Geospatial Careers

I WANNA LEARN SPATIAL DATA ANALYSIS, BUT…

I found most spatial data books & manuals vague

There are no courses that actually teach me how it’s actually done

Available resources are expensive & I can’t afford them

I want to get a job in the field of GIS and geospatial analysis

I work in the field of ecology or quantitative social sciences or hydrology or civil engineering or geography

If you found yourself in one of these situations, then..

I’VE GOT GREAT NEWS FOR YOU!

Over the past few months, I have published multiple courses on Udemy around this topic which will be tremendously helpful to you & most important of all AFFORDABLE!

Today, I’ve created yet another powerful resource for you!

In this course, over 50+ hands-on and practical lecture, I will help you master the most common and important geo-processing tasks that can be performed with ArcGIS Desktop, one of THE MOST important GIS software tools available.

I will also show you the kind of questions answered through Spatial Analysis & data used.

THE STEPS I’LL WALK YOU THROUGH:

First of all, we’ll start some basic GIS tasks like “Zooming”.

Then, we’ll move into more complex processing tasks like “Geo-Statistics”.

We’ll also deal with some theoretical concepts related to Spatial Data Analysis, and then we’ll focus on implementing some of the most common GIS techniques (all the way showing you how to execute these tasks in ArcGIS Desktop).

The stuff you’ll learn from this course will be extremely useful in terms of you being able to implement it on future Spatial Data projects you’ll be working on (in a variety of disciplines from ecology to engineering).

LEARN FROM AN ACTUAL EXPERT IN THIS AREA:

My name is MINERVA SINGH.

I am an Oxford University MPhil (Geography and Environment) graduate.

I recently finished my PhD at Cambridge University (Tropical Ecology and Conservation).

I have SEVERAL YEARS OF EXPERIENCE in analyzing REAL LIFE DATA from different sources in ArcGIS Desktop.

I’ve also published my work in many international peer reviewed journals.

HERE IS HOW MY COURSE IS UNIQUE..

My course is a HANDS ON TRAINING with REAL data.

It’s a step by step course covering both the THEORY & APPLICATION of Spatial Data Analysis.

I teach Practical Stuff that you can learn quickly and start implementing NOW.

This is one of the most comprehensive courses on this topic.

I advise you to take advantage of it & enroll in the course TODAY!

TAKE ACTION TODAY! I will personally support you and ensure your experience with this course is a success. I answer all questions put to me and guide my students when they get stuck.

Please make sure you have access to ArcGIS before enrolling

Who this course is for:

• Academics and Researchers
• Conservation managers, field ecologists and social scientists
• People looking to get started in the field of GIS Analysis
• Students of Geography, Environmental Sciences, Geology, Hydrology, Engineering, Earth Sciences and Ecology
• People looking to use ArcGIS Desktop in academic or professional settings

#ArcGIS #Desktop For Spatial #Analysis: Go From Basic To #Pro 90% off !!! #coupon #offer #discount #deals #couponcode
https://www.udemy.com/arcgis-desktop-for-spatial-analysis-go-from-basic-to-pro/?couponCode=ARCGIS_PROMO_20

Core Spatial Data Analysis: Introductory GIS with R and QGIS

MASTER SPATIAL DATA ANALYSIS IN R & QGIS: HANDS ON TRAINING WITH A REAL SPATIAL DATA PROJECT!

Do you find GIS & Spatial Data books & manuals too vague, expensive & not practical and looking for a course that takes you by hand, teaches you all the concepts, and get you started on a real life project?

Or perhaps you want to save time and learn how to automate some of the most common GIS tasks?

I’m very excited you found my spatial data analysis course. My course provides a foundation to carry out PRACTICAL, real-life spatial data analysis tasks in popular and FREE software frameworks.

My name is MINERVA SINGH and i am an Oxford University MPhil (Geography and Environment) graduate. I am currently pursuing a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life spatial data from different sources and producing publications for international peer reviewed journals.

In this course, actual spatial data from the Tam Dao National Park in Vietnam will be used to give a practical hands-on experience of working with real life spatial data and understanding what kind of questions spatial data can help us answer. The underlying motivation for the course is to ensure you can put spatial data analysis into practice today. Start analyzing spatial data for your own projects, whatever your skill level and IMPRESS your potential employers with an actual example of your spatial data analysis abilities.

This is a core course in spatial data analysis, i.e. we will focus on learning the most important and widely encountered spatial data analysis tasks in both R and QGIS

It is a practical, hands-on course, i.e. we will spend a tiny amount of time dealing with some of the theoretical concepts related to spatial data analysis. However, majority of the course will focus on working with the spatial data from the Tam Dao National Park, Vietnam. After each video you will learn a new concept or technique which you may apply to your own projects.

TAKE ACTION TODAY! I will personally support you and ensure your experience with this course is a success.

Who this course is for:

• Academics
• Researchers
• Conservation managers
• Anybody who works/will work with spatial data.

#Core #Spatial #Data #Analysis: Introductory GIS with R and QGIS 93% off #couponcode for #udemy #course
https://www.udemy.com/core-spatial-data-analysis-with-r-and-qgis/?couponCode=EARLYBIRD_COREGIS

[Intermediate] Spatial Data Analysis with R, QGIS & More

PRACTICAL TRAINING WITH REAL SPATIAL DATA FROM DIFFERENT SOURCES.

—————————————————————————————————————-

DEVELOP MAD GIS SKILLS AND PERFORM SPATIAL DATA ANALYSIS USING FREE KICKASS TOOLS SUCH AS QGIS, R, GRASS AND GOOGLE EARTH.

—————————————————————————————————————-

This course is designed to take users who use R and QGIS for basic spatial data/GIS analysis to perform more advanced GIS tasks (including automated workflows and geo-referencing) using a variety of different data. In addition to making you proficient in R and QGIS for spatial data analysis, you will be introduced to another powerful free GIS software.. GRASS.

This course takes a completely practical approach to spatial data analysis and mapping- Each lecture will teach you a practical application/processing technique which you can apply easily.

The course is taught by Minerva Singh, A PhD graduate from Cambridge University, UK, who has several years of research experience in Quantitative Ecology and an MPhil in Geography and Environment from Oxford University. Minerva has published papers in international peer reviewed journals and given talks at international conferences.  

The underlying motivation for the course is to ensure you can put spatial data analysis into practice today and develop sound GIS analysis skills. You’ll be able to start analyzing spatial data for your own projects, and IMPRESS YOUR FUTURE EMPLOYERS with examples of your PRACTICAL spatial data analysis abilities. This course is different from other training resources. Each lecture seeks to enhance your GIS skills in a demonstrable and tangible manner and provide you with practically implementable GIS solutions.

This is an intermediate course in spatial data analysis, i.e. we will build on on basic spatial data analysis tasks (such as those covered in the beginner version course: Core Spatial Data Analysis: Introductory GIS with R and QGIS) and teach users how to practically implement more complex GIS tasks such as interpolation, mapping spatial data, geo-referencing and detailed vector processing. Additionally you will be introduced to preliminary geo-statistics and mapping/visualizing spatial data.

This course covers complex GIS techniques, and by completing this course, you will be implementing these PRACTICALLY in freely-available software, thus making you MORE ATTRACTIVE TO EMPLOYERS.  

It is a practical, hands-on course, i.e. we will spend a tiny amount of time dealing with some of the theoretical concepts pertaining to the different spatial data analysis techniques demonstrated in the course. However, majority of the course will focus on working with real spatial data from different sources. After each video you will learn how to practically implement a new concept or technique in the different softwares used for the course.

During the course of my research I have discovered that R is a powerful tool for collating and analyzing spatial data acquired from different sources.  Proficiency in spatial data analysis in R and QGIS has helped me publish more peer reviewed papers faster. Feel free to check out my profile on ResearchGate.

FREE BONUS: You will have access to all the data used in the course, along with the R code files. You will also have access to future lectures, resources and R code files. Enroll in the course today & take advantage of this special bonus!

I don’t have to remind you that we have a RISK-FREE GUARANTEE in the case of you not being satisfied with the course. Take action now!

Who this course is for:

• People who have a basic understanding of spatial data analysis and want to learn more
• Students interested in building up on skills acquired through my previous course Core Spatial Data Analysis: Introductory GIS with R and QGIS
• Academics
• Conservation managers
• GIS Technicians

95% Discount #coupon code for Spatial #Data #Analysis with R, QGIS & More #udemy #course
https://www.udemy.com/intermediate-spatial-data-analysis-with-r-qgis-more/?couponCode=10PROMO

Learn 16 Machine Learning Algorithms in a Fun and Easy along with Practical Python Labs using Keras

Welcome to the Fun and Easy Machine learning Course in Python and Keras.
 

Are you Intrigued by the field of Machine Learning? Then this course is for you! We will take you on an adventure into the amazing of field Machine Learning. Each section consists of fun and intriguing white board explanations with regards to important concepts in Machine learning as well as practical python labs which you will enhance your comprehension of this vast yet lucrative sub-field of Data Science.

So Many Machine Learning Courses Out There, Why This One?

This is a valid question and the answer is simple. This is the ONLY course on Udemy which will get you implementing some of the most common machine learning algorithms on real data in Python. Plus, you will gain exposure to neural networks (using the H2o framework) and some of the most common deep learning algorithms with the Keras package.

We designed this course for anyone who wants to learn the state of the art in Machine learning in a simple and fun way without learning complex math or boring explanations.  Each theoretically lecture is uniquely designed using whiteboard animations which can maximize engagement in the lectures and improves knowledge retention. This ensures that you absorb more content than you would traditionally would watching other theoretical videos and or books on this subject.

What you will Learn in this Course

This is how the course is structured:

Regression – Linear Regression, Decision Trees, Random Forest Regression,

Classification – Logistic Regression, K Nearest Neighbors (KNN), Support Vector Machine (SVM) and Naive Bayes,

Clustering - K-Means, Hierarchical Clustering,

Association Rule Learning - Apriori, Eclat,

Dimensionality Reduction - Principle Component Analysis, Linear Discriminant  Analysis,

Neural Networks - Artificial Neural Networks, Convolution Neural Networks, Recurrent Neural Networks.

Practical Lab Structure
You DO NOT need any prior Python or Statistics/Machine Learning Knowledge to get Started. The course will start by introducing students to one of the most fundamental statistical data analysis models and its practical implementation in Python- ordinary least squares (OLS) regression. Subsequently some of the most common machine learning regression and classification techniques such as random forests, decision trees and linear discriminant analysis will be covered. In addition to providing a theoretical foundation for these, hands-on practical labs will demonstrate how to implement these in Python. Students will also be introduced to the practical applications of common data mining techniques in Python and gain proficiency in using a powerful Python based framework for machine learning which is Anaconda (Python Distribution). Finally you will get a solid grounding in both Artificial Neural Networks (ANN) and the Keras package for implementing deep learning algorithms such as the Convolution Neural Network (CNN). Deep Learning is an in-demand topic and a knowledge of this will make you more attractive to employers.

Excited Yet?

So as you can see you are going to be learning to build a lot of impressive Machine Learning apps in this 3 hour course. The underlying motivation for the course is to ensure you can apply Python based data science on real data into practice today. Start analyzing  data for your own projects, whatever your skill level and IMPRESS your potential employers with an actual examples of your  machine learning abilities.

It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different  techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects.

TAKE ACTION TODAY! We will personally support you and ensure your experience with this course is a success. And for any reason you are unhappy with this course, Udemy has a 30 day Money Back Refund Policy, So no questions asked, no quibble and no Risk to you. You got nothing to lose. Click that enroll button and we’ll see you in side the course.

Who this course is for:

• Student who starting out or interested in Machine Learning or Deep Learning.
• Students with Prior Python Programming Exposure Who Want to Use it for Machine Learning
• Students interested in gaining exposure to the Keras library for Deep Learning.
• Data analysts who want to expand into Machine Learning.
• College students who want to start a career in Data Science.

92% discount #coupon #udemy #course for
The Fun and Easy Guide to #Machine #Learning using #Keras
#couponcode
https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/

Harness The Power Of Machine Learning For Unsupervised & Supervised Learning In R -- With Practical Examples

HERE IS WHY YOU SHOULD TAKE THIS COURSE:

This course your complete guide to both supervised & unsupervised learning using R…

That means, this course covers all the main aspects of practical data science and if you take this course, you can do away with taking other courses or buying books on R based data science.

In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in unsupervised & supervised learning in R, you can give your company a competitive edge and boost your career to the next level.

LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE:

My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University.

I have +5 years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals.

Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic…

This course will give you a robust grounding in the main aspects of machine learning- clustering & classification.

Unlike other R instructors, I dig deep into the machine learning features of R and gives you a one-of-a-kind grounding in  Data Science!

You will go all the way from carrying out data reading & cleaning  to machine learning to finally implementing powerful machine learning algorithms and evaluating their performance using R.

THIS COURSE HAS 8 SECTIONS COVERING EVERY ASPECT OF R MACHINE LEARNING:

• A full introduction to the R Framework for data science

• Data Structures and Reading in R, including CSV, Excel and HTML data

• How to Pre-Process and “Clean” data by removing NAs/No data,visualization

• Machine Learning, Supervised Learning, Unsupervised Learning in R

• Model building and selection…& MUCH MORE!

By the end of the course, you’ll have the keys to the entire R Machine Learning Kingdom!

NO PRIOR R OR STATISTICS/MACHINE LEARNING KNOWLEDGE REQUIRED:

You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R.

My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life.

After taking this course, you’ll easily use data science packages like caret to work with real data in R…

You’ll even understand concepts like unsupervised learning, dimension reduction and supervised learning. Again, we’ll work with real data and you will have access to all the code and data used in the course.

JOIN MY COURSE NOW!

Who this course is for:

• Students Interested In Getting Started With Data Science Applications In The R & R Studio Environment
• Students Wishing To Learn The Implementation Of Unsupervised Learning On Real Data
• Students Wishing To Learn The Implementation Of Supervised Learning (Classification) On Real Data

92% discount #coupon #udemy #course for
Complete #Deep #Learning In R With Keras & Others
#couponcode
https://www.udemy.com/complete-deep-learning-in-r-with-keras-others/

Spatial Data Analysis With ArcGIS Desktop: Master GIS Techniques and Open Doors to Amazing Geospatial Careers

I WANNA LEARN SPATIAL DATA ANALYSIS, BUT…

I found most spatial data books & manuals vague

There are no courses that actually teach me how it’s actually done

Available resources are expensive & I can’t afford them

I want to get a job in the field of GIS and geospatial analysis

I work in the field of ecology or quantitative social sciences or hydrology or civil engineering or geography

If you found yourself in one of these situations, then..

I’VE GOT GREAT NEWS FOR YOU!

Over the past few months, I have published multiple courses on Udemy around this topic which will be tremendously helpful to you & most important of all AFFORDABLE!

Today, I’ve created yet another powerful resource for you!

In this course, over 50+ hands-on and practical lecture, I will help you master the most common and important geo-processing tasks that can be performed with ArcGIS Desktop, one of THE MOST important GIS software tools available.

I will also show you the kind of questions answered through Spatial Analysis & data used.

THE STEPS I’LL WALK YOU THROUGH:

First of all, we’ll start some basic GIS tasks like “Zooming”.

Then, we’ll move into more complex processing tasks like “Geo-Statistics”.

We’ll also deal with some theoretical concepts related to Spatial Data Analysis, and then we’ll focus on implementing some of the most common GIS techniques (all the way showing you how to execute these tasks in ArcGIS Desktop).

The stuff you’ll learn from this course will be extremely useful in terms of you being able to implement it on future Spatial Data projects you’ll be working on (in a variety of disciplines from ecology to engineering).

LEARN FROM AN ACTUAL EXPERT IN THIS AREA:

My name is MINERVA SINGH.

I am an Oxford University MPhil (Geography and Environment) graduate.

I recently finished my PhD at Cambridge University (Tropical Ecology and Conservation).

I have SEVERAL YEARS OF EXPERIENCE in analyzing REAL LIFE DATA from different sources in ArcGIS Desktop.

I’ve also published my work in many international peer reviewed journals.

HERE IS HOW MY COURSE IS UNIQUE..

My course is a HANDS ON TRAINING with REAL data.

It’s a step by step course covering both the THEORY & APPLICATION of Spatial Data Analysis.

I teach Practical Stuff that you can learn quickly and start implementing NOW.

This is one of the most comprehensive courses on this topic.

I advise you to take advantage of it & enroll in the course TODAY!

TAKE ACTION TODAY! I will personally support you and ensure your experience with this course is a success. I answer all questions put to me and guide my students when they get stuck.

Please make sure you have access to ArcGIS before enrolling

Who this course is for:

• Academics and Researchers
• Conservation managers, field ecologists and social scientists
• People looking to get started in the field of GIS Analysis
• Students of Geography, Environmental Sciences, Geology, Hydrology, Engineering, Earth Sciences and Ecology
• People looking to use ArcGIS Desktop in academic or professional settings

#ArcGIS Desktop For Spatial Analysis: Go From Basic To Pro #udemy #course 95% off !!! #couponcode
https://www.udemy.com/arcgis-desktop-for-spatial-analysis-go-from-basic-to-pro/?couponCode=ARCGIS10A

Complete Guide to Practical Data Science with Python: Learn Statistics, Visualization, Machine Learning & More

THIS IS A COMPLETE DATA SCIENCE TRAINING WITH PYTHON FOR DATA ANALYSIS:

It’s A Full 12-Hour Python Data Science BootCamp To Help You Learn Statistical Modelling, Data Visualization, Machine Learning & Basic Deep Learning In Python!

HERE IS WHY YOU SHOULD TAKE THIS COURSE:

First of all, this course a complete guide to practical data science using Python…

That means, this course covers ALL the aspects of practical data science and if you take this course alone, you can do away with taking other courses or buying books on Python based data science.  

In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal. By storing, filtering, managing, and manipulating data in Python, you can give your company a competitive edge & boost your career to the next level!

THIS IS MY PROMISE TO YOU:

COMPLETE THIS ONE COURSE & BECOME A PRO IN PRACTICAL PYTHON BASED DATA SCIENCE!

But, first things first, My name is MINERVA SINGH and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation).

I have several years of experience in analyzing real life data from different sources  using data science related techniques and producing publications for international peer reviewed journals.

Over the course of my research I realized almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning…

This gives student an incomplete knowledge of the subject. This course will give you a robust grounding in all aspects of data science, from statistical modeling to visualization to machine learning.

Unlike other Python instructors, I dig deep into the statistical modeling features of Python and gives you a one-of-a-kind grounding in Python Data Science!

You will go all the way from carrying out simple visualizations and data explorations to statistical analysis to machine learning to finally implementing simple deep learning based models using Python

DISCOVER 12 COMPLETE SECTIONS ADDRESSING EVERY ASPECT OF PYTHON DATA SCIENCE (INCLUDING):

• A full introduction to Python Data Science and powerful Python driven framework for data science, Anaconda
• Getting started with Jupyter notebooks for implementing data science techniques in Python
• A comprehensive presentation about basic analytical tools- Numpy Arrays, Operations, Arithmetic, Equation-solving, Matrices, Vectors, Broadcasting, etc.
• Data Structures and Reading in Pandas, including CSV, Excel, JSON, HTML data
• How to Pre-Process and “Wrangle” your Python data by removing NAs/No data, handling conditional data, grouping by attributes, etc.
• Creating data visualizations like histograms, boxplots, scatterplots, barplots, pie/line charts, and more!
• Statistical analysis, statistical inference, and the relationships between variables
• Machine Learning, Supervised Learning, Unsupervised Learning in Python
• You’ll even discover how to create artificial neural networks and deep learning structures…& MUCH MORE!

With this course, you’ll have the keys to the entire Python Data Science kingdom!

NO PRIOR PYTHON OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED:

You’ll start by absorbing the most valuable Python Data Science basics and techniques…

I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in Python.

My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement Python based data science in real life.

After taking this course, you’ll easily use packages like Numpy, Pandas, and Matplotlib to work with real data in Python.

You’ll even understand deep concepts like statistical modeling in Python’s Statsmodels package and the difference between statistics and machine learning (including hands-on techniques).

I will even introduce you to deep learning and neural networks using the powerful H2o framework!

With this Powerful All-In-One Python Data Science course, you’ll know it all: visualization, stats, machine learning, data mining, and deep learning!

The underlying motivation for the course is to ensure you can apply Python based data science on real data and put into practice today. Start analyzing  data for your own projects, whatever your skill level and IMPRESS your potential employers with actual examples of your  data science abilities.

HERE IS WHAT THIS COURSE WILL DO FOR YOU:

This course is your one shot way of acquiring the knowledge of statistical data analysis skills that I acquired from the rigorous training received at two of the best universities in the world, perusal of numerous books and publishing statistically rich papers in renowned international journal like PLOS One.

This course will:

  (a) Take students without a prior Python and/or statistics background background from a basic level to performing some of the most common advanced data science techniques using the powerful Python based Jupyter notebooks.

  (b) Equip students to use Python for performing different statistical data analysis and visualization tasks for data modelling.

  © Introduce some of the most important statistical and machine learning concepts to students in a practical manner such that students can apply these concepts for practical data analysis and interpretation.

  (d) Students will get a strong background in some of the most important data science techniques.

  (e) Students will be able to decide which data science techniques are best suited to answer their research questions and applicable to their data and interpret the results.

It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different  techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects.

JOIN THE COURSE NOW!

Who this course is for:

• Anyone Who Wishes To Learn Practical Data Science Using Python
• Anyone Interested In Learning How To Implement Machine Learning Algorithms Using Python
• People Looking To Get Started In Deep Learning Using Python
• People Looking To Work With Real Life Data In Python
• Anyone With A Prior Knowledge Of Python Looking To Branch Out Into Data Analysis
• Anyone Looking To Become Proficient In Exploratory Data Analysis, Statistical Modelling & Visualizations Using iPython.

Complete Data #Science Training with #Python for #Data Analysis Online #course now 95% discounted check #coupon
https://www.udemy.com/complete-data-science-training-with-python-for-data-analysis/?couponCode=PYTHON_DS10

Your Complete Guide to Statistical Data Analysis and Visualization For Practical Applications in R

APPLIED STATISTICAL MODELING FOR DATA ANALYSIS IN R

COMPLETE GUIDE TO STATISTICAL DATA ANALYSIS & VISUALIZATION FOR PRACTICAL APPLICATIONS IN R

            Confounded by Confidence Intervals? Pondering Over p-values? Hankering Over Hypothesis Testing?

Hello, My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation).

I have several years of experience in analyzing real life data from different sources using statistical modeling and producing publications for international peer reviewed journals. If you find statistics books & manuals too vague, expensive & not practical, then you’re going to love this course!

I created this course to take you by hand and teach you all the concepts, and take your statistical modeling from basic to an advanced level for practical data analysis.

With this course, I want to help you save time and learn what the arcane statistical concepts have to do with the actual analysis of data and the interpretation of the bespoke results. Frankly, this is the only one course you need to complete  in order to get a head start in practical statistical modeling for data analysis using R.

My course has 9.5 hours of lectures and provides a robust foundation to carry out PRACTICAL, real-life statistical data analysis tasks in R, one of the most popular and FREE data analysis frameworks.

GET ACCESS TO A COURSE THAT IS JAM PACKED WITH TONS OF APPLICABLE INFORMATION! AND GET A FREE VIDEO COURSE IN MACHINE LEARNING AS WELL!

This course is your sure-fire way of acquiring the knowledge and statistical data analysis skills that I acquired from the rigorous training I received at 2 of the best universities in the world, perusal of numerous books and publishing statistically rich papers in renowned international journal like PLOS One.

To be more specific, here’s what the course will do for you:

 (a) It will take you (even if you have no prior statistical modelling/analysis background) from a basic level to performing some of the most common advanced statistical data analysis tasks in R.

 (b) It will equip you to use R for performing the different statistical data analysis and visualization tasks for data modelling.

 © It will Introduce some of the most important statistical concepts to you in a practical manner such that you can apply these concepts for practical data analysis and interpretation.

 (d) You will learn some of the most important statistical modelling concepts from probability distributions to hypothesis testing to regression modelling and multivariate analysis.

 (e) You will also be able to decide which statistical modelling techniques are best suited to answer your research questions and applicable to your data and interpret the results.

The course will mostly focus on helping you implement different statistical analysis techniques on your data and interpret the results.

After each video you will learn a new concept or technique which you may apply to your own projects immediately!

TAKE ACTION NOW :) You’ll also have my continuous support when you take this course just to make sure you’re successful with it.  If my GUARANTEE is not enough for you, you can ask for a refund within 30 days of your purchase in case you’re not completely satisfied with the course.

TAKE ACTION TODAY! I will personally support you and ensure your experience with this course is a success.

Who this course is for:

• People working in any numerate field which requires data analysis
• Students of Environmental Science, Ecology, Biology,Conservation and Other Natural Sciences
• People with some prior knowledge of the R interface- (a) installing packages (b) reading in csv files
• People carrying out observational or experimental studies.

90% OFF #discount #course ON #udemy #couponcode
Text #Mining and Natural #Language Processing in R
https://www.udemy.com/text-mining-and-natural-language-processing-in-r/learn/v4/?couponCode=TEXTMINE_FREE1

System4 Facility Services on #Google https://t.co/dkKMqIU1iS

System4 Facility Services on #Google https://t.co/dkKMqIU1iS

— Joe Ziolkowski (@ziolkowski_joe) June 19, 2019

from Twitter https://twitter.com/ziolkowski_joe