Python Regression Analysis: Statistics & Machine Learning

Learn Complete Hands-On Regression Analysis for Practical Statistical Modelling and Machine Learning in Python

What you'll learn
Harness The Power Of Anaconda/iPython For Practical Data Science
Read In Data Into The Python Environment From Different Sources
Implement Classical Statistical Regression Modelling Techniques Such As Linear Regression In Python
Implement Machine Learning Based Regression Modelling Techniques Such As Random Forests & kNN For Predictive Modelling
Neural Network & Deep Learning Based Regression

Requirements
Be Able To Operate & Install Software On A Computer
Have Prior Exposure To Common Machine Learning Terms Such As Regression Modelling & Supervised Learning

Description
HERE IS WHY YOU SHOULD ENROLL IN THIS COURSE:Regression analysis is one of the central aspects of both statistical and machine learning based analysis. This course will teach you regression analysis for both statistical data analysis and machine learning in Python in a practical hands-on manner. It explores the relevant concepts in a practical manner from basic to expert level. This course can help you achieve better grades, give you new analysis tools for your academic career, implement your knowledge in a work setting & make business forecasting related decisions...All of this while exploring the wisdom of an Oxford and Cambridge educated researcher.Most statistics and machine learning courses and books only touch upon the basic aspects of regression analysis. This does not teach the students about all the different regression analysis techniques they can apply to their own data in both academic and business setting, resulting in inaccurate modelling. My course is Different; It will help you go all the way from implementing and inferring simple OLS (ordinary least square) regression models to dealing with issues of multicollinearity in regression to machine learning based regression models. LEARN FROM AN EXPERT DATA SCIENTIST: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 (Tropical Ecology and Conservation). 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. This course is based on my years of regression modelling experience and implementing different regression models on real life data.
THIS COURSE WILL HELP YOU BECOME A REGRESSION ANALYSIS EXPERT:Here is what we'll be covering inside the course:Get started with Python and Anaconda. Install these on your system, learn to load packages and read in different types of data in PythonCarry out data cleaning PythonImplement ordinary least square (OLS) regression in Python and learn how to interpret the results.Evaluate regression model accuracyImplement generalized linear models (GLMs) such as logistic regression using PythonUse machine learning based regression techniques for predictive modelling Work with tree-based machine learning modelsImplement machine learning methods such as random forest regression and gradient boosting machine regression for improved regression prediction accuracy.& Carry out model selectionTHIS IS A PRACTICAL GUIDE TO REGRESSION ANALYSIS WITH REAL LIFE DATA:This course is your one shot way of acquiring the knowledge of statistical and machine learning analysis 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. Specifically the course will: (a) Take you from a basic level of statistical knowledge to performing some of the most common advanced regression analysis based techniques. (b) Equip you to use Python for performing the different statistical and machine learning data analysis tasks. (c) Introduce some of the most important statistical and machine learning concepts to you in a practical manner so you can apply these concepts for practical data analysis and interpretation. (d) You will get a strong background in some of the most important statistical and machine learning concepts for regression analysis. (e) You will be able to decide which regression analysis techniques are best suited to answer your research questions and applicable to your 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 both statistical and machine learning regression analysis... 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!

Overview
Section 1: INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools

Lecture 1 Welcome to the Course

Lecture 2 Data and Scripts For the Course

Lecture 3 Python Data Science Environment

Lecture 4 For Mac Users

Lecture 5 Introduction to IPython

Lecture 6 IPython in Browser

Lecture 7 Python Data Science Packages To Be Used

Section 2: Read in Data From Different Sources With Pandas

Lecture 8 What are Pandas?

Lecture 9 Read in Data from CSV

Lecture 10 Read in Excel Data

Lecture 11 Read in HTML Data

Section 3: Data Cleaning & Munging

Lecture 12 Remove Missing Values

Lecture 13 Conditional Data Selection

Lecture 14 Data Grouping

Lecture 15 Data Subsetting

Lecture 16 Ranking & Sorting

Lecture 17 Concatenate

Lecture 18 Merging & Joining Data Frames

Section 4: Statistical Data Analysis-Basic

Lecture 19 What is Statistical Data Analysis?

Lecture 20 Some Pointers on Collecting Data for Statistical Studies

Lecture 21 Some Pointers on Exploring Quantitative Data

Lecture 22 Explore the Quantitative Data: Descriptive Statistics

Lecture 23 Grouping & Summarizing Data by Categories

Lecture 24 Visualize Descriptive Statistics-Boxplots

Lecture 25 Common Terms Relating to Descriptive Statistics

Lecture 26 Data Distribution- Normal Distribution

Lecture 27 Check for Normal Distribution

Lecture 28 Standard Normal Distribution and Z-scores

Lecture 29 Confidence Interval-Theory

Lecture 30 Confidence Interval-Calculation

Section 5: Regression Modelling for Defining Relationship bw Variables

Lecture 31 Explore the Relationship Between Two Quantitative Variables

Lecture 32 Correlation Analysis

Lecture 33 Linear Regression-Theory

Lecture 34 Linear Regression-Implementation in Python

Lecture 35 Conditions of Linear Regression

Lecture 36 Conditions of Linear Regression-Check in Python

Lecture 37 Polynomial Regression

Lecture 38 GLM: Generalized Linear Model

Lecture 39 Logistic Regression

Section 6: Machine Learning for Data Science

Lecture 40 How is Machine Learning Different from Statistical Data Analysis?

Lecture 41 What is Machine Learning (ML) About? Some Theoretical Pointers

Section 7: Machine Learning Based Regression Modelling

Lecture 42 What Is This Section About?

Lecture 43 Data Preparation for Supervised Learning

Lecture 44 Pointers on Evaluating the Accuracy of Classification and Regression Modelling

Lecture 45 RF-Regression

Lecture 46 Support Vector Regression

Lecture 47 knn-Regression

Lecture 48 Gradient Boosting-regression

Lecture 49 Theory Behind ANN and DNN

Lecture 50 Regression with MLP

Section 8: Miscallaneous Information

Lecture 51 Using Colabs for Online Data Science

Lecture 52 Colab GPU

Lecture 53 Github

Lecture 54 What is Machine Learning?

Students Who Had Prior exposure to Python programming (Not Essential),Students Wanting To Master The Anaconda iPython Environment For Data Science & Scientific Computations,Students Wishing To Learn The Implementation Of Supervised Learning (Regression) On Real Data Using Python,Students Looking To Get Started With Artificial Neural Networks & Deep Learning

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