Complete Linear Regression Analysis in Python
Free Download Complete Linear Regression Analysis in Python
Last updated 11/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.50 GB | Duration: 7h 33m
Linear Regression in Python| Simple Regression, Multiple Regression, Ridge Regression, Lasso and subset selection also
What you'll learn
Learn how to solve real life problem using the Linear Regression technique
Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression
Predict future outcomes basis past data by implementing Simplest Machine Learning algorithm
Understand how to interpret the result of Linear Regression model and translate them into actionable insight
Understanding of basics of statistics and concepts of Machine Learning
Indepth knowledge of data collection and data preprocessing for Machine Learning Linear Regression problem
Learn advanced variations of OLS method of Linear Regression
Course contains a end-to-end DIY project to implement your learnings from the lectures
How to convert business problem into a Machine learning Linear Regression problem
Basic statistics using Numpy library in Python
Data representation using Seaborn library in Python
Linear Regression technique of Machine Learning using Scikit Learn and Statsmodel libraries of Python
Requirements
Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same
Description
You're looking for a complete Linear Regression course that teaches you everything you need to create a Linear Regression model in Python, right?You've found the right Linear Regression course!After completing this course you will be able to:Identify the business problem which can be solved using linear regression technique of Machine Learning.Create a linear regression model in Python and analyze its result.Confidently practice, discuss and understand Machine Learning conceptsA Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.How this course will help you?If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you the most popular technique of machine learning, which is Linear RegressionWhy should you choose this course?This course covers all the steps that one should take while solving a business problem through linear regression.Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones:This is very good, i love the fact the all explanation given can be understood by a layman - JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. - DaisyOur PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Quizzes, and complete AssignmentsWith each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning. What is covered in this course? This course teaches you all the steps of creating a Linear Regression model, which is the most popular Machine Learning model, to solve business problems.Below are the course contents of this course on Linear Regression:Section 1 - Basics of StatisticsThis section is divided into five different lectures starting from types of data then types of statisticsthen graphical representations to describe the data and then a lecture on measures of center like meanmedian and mode and lastly measures of dispersion like range and standard deviationSection 2 - Python basicThis section gets you started with Python.This section will help you set up the python and Jupyter environment on your system and it'll teachyou how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.Section 3 - Introduction to Machine LearningIn this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.Section 4 - Data PreprocessingIn this section you will learn what actions you need to take a step by step to get the data and then prepare it for the analysis these steps are very important.We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation and correlation.Section 5 - Regression ModelThis section starts with simple linear regression and then covers multiple linear regression.We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don't understand it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results, what are other variations to the ordinary least squared method and how do we finally interpret the result to find out the answer to a business problem.By the end of this course, your confidence in creating a regression model in Python will soar. You'll have a thorough understanding of how to use regression modelling to create predictive models and solve business problems.Go ahead and click the enroll button, and I'll see you in lesson 1!CheersStart-Tech Academy------------Below is a list of popular FAQs of students who want to start their Machine learning journey-What is Machine Learning?Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.What is the Linear regression technique of Machine learning?Linear Regression is a simple machine learning model for regression problems, i.e., when the target variable is a real value.Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, that y can be calculated from a linear combination of the input variables (x).When there is a single input variable (x), the method is referred to as simple linear regression.When there are multiple input variables, the method is known as multiple linear regression.Why learn Linear regression technique of Machine learning?There are four reasons to learn Linear regression technique of Machine learning:1. Linear Regression is the most popular machine learning technique2. Linear Regression has fairly good prediction accuracy3. Linear Regression is simple to implement and easy to interpret4. It gives you a firm base to start learning other advanced techniques of Machine LearningHow much time does it take to learn Linear regression technique of machine learning?Linear Regression is easy but no one can determine the learning time it takes. It totally depends on you. The method we adopted to help you learn Linear regression starts from the basics and takes you to advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it. Practice is the only way to remember whatever you have learnt. Therefore, we have also provided you with another data set to work on as a separate project of Linear regression.What are the steps I should follow to be able to build a Machine Learning model?You can divide your learning process into 4 parts:Statistics and Probability - Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part.Understanding of Machine learning - Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning modelProgramming Experience - A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in PythonUnderstanding of Linear Regression modelling - Having a good knowledge of Linear Regression gives you a solid understanding of how machine learning works. Even though Linear regression is the simplest technique of Machine learning, it is still the most popular one with fairly good prediction ability. Fifth and sixth section cover Linear regression topic end-to-end and with each theory lecture comes a corresponding practical lecture where we actually run each query with you.Why use Python for data Machine Learning?Understanding Python is one of the valuable skills needed for a career in Machine Learning.Though it hasn't always been, Python is the programming language of choice for data science. Here's a brief history: In 2016, it overtook R on Kaggle, the premier platform for data science competitions. In 2017, it overtook R on KDNuggets's annual poll of data scientists' most used tools. In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.Machine Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it's nice to know that employment opportunities are abundant (and growing) as well.What is the difference between Data Mining, Machine Learning, and Deep Learning?Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions.Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.
Overview
Section 1: Introduction
Lecture 1 Welcome to the course!
Lecture 2 Course contents
Lecture 3 Course Resources
Lecture 4 This is a milestone!
Section 2: Setting up Python
Lecture 5 Installing Python and Anaconda
Lecture 6 Opening Jupyter Notebook
Lecture 7 Introduction to Jupyter Notebook - Part 1
Lecture 8 Introduction to Jupyter Notebook - Part 2
Section 3: Python crash course - Working with different data types
Lecture 9 Arithmetic operators in Python
Lecture 10 Strings in Python - Part 1
Lecture 11 Strings in Python - Part 2
Lecture 12 Lists - Part 1
Lecture 13 Lists - Part 2
Lecture 14 Tuples and Directories
Section 4: Important Python Libraries
Lecture 15 Working with Numpy Library of Python
Lecture 16 Working with Pandas Library of Python
Lecture 17 Working with Seaborn Library of Python
Lecture 18 Python file for additional practice
Section 5: Integrating ChatGPT with Python
Lecture 19 Integrating ChatGPT with Jupyter notebook
Section 6: Basics of Statistics
Lecture 20 Types of Data
Lecture 21 Types of Statistics
Lecture 22 Describing data Graphically
Lecture 23 Measures of Centers
Lecture 24 Practice Exercise 1
Lecture 25 Measures of Dispersion
Lecture 26 Practice Exercise 2
Section 7: Introduction to Machine Learning
Lecture 27 Introduction to Machine Learning
Lecture 28 Building a Machine Learning Model
Section 8: Data Preprocessing
Lecture 29 Gathering Business Knowledge
Lecture 30 Data Exploration
Lecture 31 The Dataset and the Data Dictionary
Lecture 32 Importing Data in Python
Lecture 33 Project exercise 1
Lecture 34 Univariate analysis and EDD
Lecture 35 EDD in Python
Lecture 36 Project Exercise 2
Lecture 37 What is outlier treatment?
Lecture 38 Outlier Treatment in Python
Lecture 39 Project Exercise 3
Lecture 40 Missing Value Imputation
Lecture 41 Missing Value Imputation in Python
Lecture 42 Project Exercise 4
Lecture 43 Seasonality in Data
Lecture 44 Bi-variate analysis and Variable transformation
Lecture 45 Variable transformation and deletion in Python
Lecture 46 Project Exercise 5
Lecture 47 Non-usable variables
Lecture 48 Handling qualitative data by using dummy variables
Lecture 49 Dummy variable creation in Python
Lecture 50 Project Exercise 6
Lecture 51 Correlation Analysis
Lecture 52 Correlation Analysis in Python
Lecture 53 Project Exercise 7
Section 9: Linear Regression
Lecture 54 The Problem Statement
Lecture 55 Basic Equations and Ordinary Least Squares (OLS) method
Lecture 56 Assessing accuracy of predicted coefficients
Lecture 57 Assessing Model Accuracy: RSE and R squared
Lecture 58 Simple Linear Regression in Python
Lecture 59 Project Exercise 8
Lecture 60 Multiple Linear Regression
Lecture 61 The F - statistic
Lecture 62 Interpreting results of Categorical variables
Lecture 63 Multiple Linear Regression in Python
Lecture 64 Project Exercise 9
Lecture 65 Test-train split
Lecture 66 Bias Variance trade-off
Lecture 67 More about test-train split
Lecture 68 Test train split in Python
Lecture 69 Linear models other than OLS
Lecture 70 Subset selection techniques
Lecture 71 Shrinkage methods: Ridge and Lasso
Lecture 72 Ridge regression and Lasso in Python
Lecture 73 Heteroscedasticity
Lecture 74 Project Exercise 10
Lecture 75 Final Project Exercise
Lecture 76 Comprehensive Interview Preparation Questions
Section 10: Congratulations & About your certificate
Lecture 77 The final milestone!
Lecture 78 About your certificate
Lecture 79 Bonus lecture
People pursuing a career in data science,Working Professionals beginning their Data journey,Statisticians needing more practical experience,Anyone curious to master Linear Regression from beginner to Advanced in short span of time
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