Python Regression Analysis: Statistics & Machine Learning

Python Regression Analysis: Statistics & Machine Learning, available at $64.99, has an average rating of 4.68, with 55 lectures, based on 247 reviews, and has 2254 subscribers.

You will learn about 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 This course is ideal for individuals who are Students Who Had Prior exposure to Python programming (Not Essential) or Students Wanting To Master The Anaconda iPython Environment For Data Science & Scientific Computations or Students Wishing To Learn The Implementation Of Supervised Learning (Regression) On Real Data Using Python or Students Looking To Get Started With Artificial Neural Networks & Deep Learning It is particularly useful for Students Who Had Prior exposure to Python programming (Not Essential) or Students Wanting To Master The Anaconda iPython Environment For Data Science & Scientific Computations or Students Wishing To Learn The Implementation Of Supervised Learning (Regression) On Real Data Using Python or Students Looking To Get Started With Artificial Neural Networks & Deep Learning.

Enroll now: Python Regression Analysis: Statistics & Machine Learning

Summary

Title: Python Regression Analysis: Statistics & Machine Learning

Price: $64.99

Average Rating: 4.68

Number of Lectures: 55

Number of Published Lectures: 55

Number of Curriculum Items: 55

Number of Published Curriculum Objects: 55

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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 Python
Carry out data cleaning Python
Implement ordinary least square (OLS) regression in Python and learn how to interpret the results.
Evaluate regression model accuracy
Implement generalized linear models (GLMs) such as logistic regression using Python
Use machine learning based regression techniques for predictive modelling�
Work with tree-based machine learning models
Implement machine learning methods such as random forest regression and gradient boosting machine regression for improved regression prediction accuracy.
& Carry out model selection

THIS 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!

Course Curriculum

Chapter 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

Chapter 2: Read in Data From Different Sources With Pandas

Lecture 1: What are Pandas?

Lecture 2: Read in Data from CSV

Lecture 3: Read in Excel Data

Lecture 4: Read in HTML Data

Chapter 3: Data Cleaning & Munging

Lecture 1: Remove Missing Values

Lecture 2: Conditional Data Selection

Lecture 3: Data Grouping

Lecture 4: Data Subsetting

Lecture 5: Ranking & Sorting

Lecture 6: Concatenate

Lecture 7: Merging & Joining Data Frames

Chapter 4: Statistical Data Analysis-Basic

Lecture 1: What is Statistical Data Analysis?

Lecture 2: Some Pointers on Collecting Data for Statistical Studies

Lecture 3: Some Pointers on Exploring Quantitative Data

Lecture 4: Explore the Quantitative Data: Descriptive Statistics

Lecture 5: Grouping & Summarizing Data by Categories

Lecture 6: Visualize Descriptive Statistics-Boxplots

Lecture 7: Common Terms Relating to Descriptive Statistics

Lecture 8: Data Distribution- Normal Distribution

Lecture 9: Check for Normal Distribution

Lecture 10: Standard Normal Distribution and Z-scores

Lecture 11: Confidence Interval-Theory

Lecture 12: Confidence Interval-Calculation

Chapter 5: Regression Modelling for Defining Relationship bw Variables

Lecture 1: Explore the Relationship Between Two Quantitative Variables

Lecture 2: Correlation Analysis

Lecture 3: Linear Regression-Theory

Lecture 4: Linear Regression-Implementation in Python

Lecture 5: Conditions of Linear Regression

Lecture 6: Conditions of Linear Regression-Check in Python

Lecture 7: Polynomial Regression

Lecture 8: GLM: Generalized Linear Model

Lecture 9: Logistic Regression

Chapter 6: Machine Learning for Data Science

Lecture 1: How is Machine Learning Different from Statistical Data Analysis?

Lecture 2: What is Machine Learning (ML) About? Some Theoretical Pointers

Chapter 7: Machine Learning Based Regression Modelling

Lecture 1: What Is This Section About?

Lecture 2: Data Preparation for Supervised Learning

Lecture 3: Pointers on Evaluating the Accuracy of Classification and Regression Modelling

Lecture 4: RF-Regression

Lecture 5: Support Vector Regression

Lecture 6: knn-Regression

Lecture 7: Gradient Boosting-regression

Lecture 8: Theory Behind ANN and DNN

Lecture 9: Regression with MLP

Chapter 8: Miscallaneous Information

Lecture 1: Using Colabs for Online Data Science

Lecture 2: Colab GPU

Lecture 3: Github

Lecture 4: What is Machine Learning?

Lecture 5: POSIT

Instructors

Minerva Singh
Bestselling Instructor & Data Scientist(Cambridge Uni)

Rating Distribution

1 stars: 6 votes
2 stars: 13 votes
3 stars: 33 votes
4 stars: 41 votes
5 stars: 154 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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