Machine Learning 101 With Scikit-Learn And Statsmodels

New to machine learning? This is the place to start: Linear regression, Logistic regression & Cluster Analysis

What you'll learn

You will gain confidence when working with 2 of the leading ML packages - statsmodels and sklearn

You will learn how to perform a linear regression

You will become familiar with the ins and outs of a logistic regression

You will excel at carrying out cluster analysis (both flat and hierarchical)

You will learn how to apply your skills to real-life business cases

You will be able to comprehend the underlying ideas behind ML models

Requirements

Basic coding skills in Python

Description

Are you an aspiring data scientist determined to achieve professional success?Are you ready and willing to master the most valuable skills that will skyrocket your data science career?Great! You’ve come to the right place.This course will provide you with the solid Machine Learning knowledge that will help you reach your dream job destination.That’s right. Machine Learning is one of the fundamental skills you need to become a data scientist. It is the stepping stone that will help you understand deep learning and modern data analysis techniques.In this course, we will explore the three most fundamental machine learning topics: Linear regression Logistic regression Cluster analysisSurprised? Even neural networks geeks (like us) can’t help, but admit that it’s these 3 simple methods - linear regression, logistic regression and clustering that data science actually revolves around.So, in this course, we will make an otherwise complex subject matter easy to understand and apply in practice.Of course, there is only one way to teach these skills in the context of data science - to accompany statistics theory with practical application of these quantitative methods in Python.And that’s precisely what we are after. Theory and practice go hand in hand here.We have developed this course with not one but two machine learning libraries – StatsModels and sklearn. As our practical experience showed us, they have different use cases and should be used together rather than independently.Yet another advantage of taking this course? We are very conscious that data science theory is often overlooked.You can’t teach someone to run before they know how to walk. That’s why we will start slowly and continue by building complex ML models.But don’t assume you’ll be bored by theory.On the contrary! We have prepared a course that will get you results and will foster your interest in the subject matter, as it will show you that machine learning is something you can do, too (with the right teacher by your side).Well, we hope you are as excited as we are, as this course is the door that can open countless opportunities in the data science world for you. This is a course you’ll be actually eager to complete.On top of that we are happy to offer a 30-day money back guarantee. No risk for you. The content of the course is so outstanding , that this is a no-brainer for us We are 100% certain you will love it.Why wait any longer? Every day is a missed opportunity.Click the “Buy Now” button and let’s start (machine) learning together!

Overview

Section 1: Introduction

Lecture 1 What Does the Course Cover?

Section 2: Setting Up The Working Environment

Lecture 2 Setting Up the Environment - An Introduction (Do Not Skip, Please)!

Lecture 3 Why Python and Why Jupyter?

Lecture 4 Installing Anaconda

Lecture 5 The Jupyter Dashboard - Part 1

Lecture 6 The Jupyter Dashboard - Part 2

Lecture 7 Jupyter Shortcuts

Lecture 8 Installing sklearn

Lecture 9 Installing Packages - Exercise

Lecture 10 Installing Packages - Solution

Section 3: Linear Regression with StatsModels

Lecture 11 Introduction to Regression Analysis

Lecture 12 The Linear Regression Model

Lecture 13 Correlation vs Regression

Lecture 14 Geometrical Representation

Lecture 15 Python Packages Installation

Lecture 16 Simple Linear Regression in Python

Lecture 17 Simple Linear Regression in Python - Exercise

Lecture 18 What is Seaborn?

Lecture 19 What Does the StatsModels Summary Regression Table Tell us?

Lecture 20 SST, SSR, and SSE

Lecture 21 The Ordinary Least Squares (OLS)

Lecture 22 Goodness of Fit: The R-Squared

Lecture 23 The Multiple Linear Regression Model

Lecture 24 Adjusted R-Squared

Lecture 25 Multiple Linear Regression - Exercise

Lecture 26 F-Statistic and F-Test for a Linear Regression

Lecture 27 Assumptions of the OLS Framework

Lecture 28 A1: Linearity

Lecture 29 A2: No Endogeneity

Lecture 30 A3: Normality and Homoscedasticity

Lecture 31 A4: No Autocorrelation

Lecture 32 A5: No Multicollinearity

Lecture 33 Dealing with Categorical Data

Lecture 34 Dealing with Categorical Data - Exercise

Lecture 35 Making Predictions

Section 4: Linear Regression with Sklearn

Lecture 36 What is sklearn?

Lecture 37 Game Plan for sklearn

Lecture 38 Simple Linear Regression with sklearn

Lecture 39 Simple Linear Regression with sklearn - Summary Table

Lecture 40 A Note on Normalization

Lecture 41 Simple Linear Regression with sklearn - Exercise

Lecture 42 Multiple Linear Regression with sklearn

Lecture 43 Adjusted R-Squared

Lecture 44 Adjusted R-Squared - Exercise

Lecture 45 Feature Selection through p-values (F-regression)

Lecture 46 A Note on Calculation of P-values with sklearn

Lecture 47 Creating a Summary Table with the p-values

Lecture 48 Multiple Linear Regression - Exercise

Lecture 49 Feature Scaling

Lecture 50 Feature Selection through Standardization

Lecture 51 Making Predictions with Standardized Coefficients

Lecture 52 Feature Scaling - Exercise

Lecture 53 Underfitting and Overfitting

Lecture 54 Training and Testing

Section 5: Linear Regression - Practical Example

Lecture 55 Practical Example (Part 1)

Lecture 56 Practical Example (Part 2)

Lecture 57 A Note on Multicollinearity

Lecture 58 Practical Example (Part 3)

Lecture 59 Dummies and VIF - Exercise

Lecture 60 Practical Example (Part 4)

Lecture 61 Dummy Variables Interpretation - Exercise

Lecture 62 Practical Example (Part 5)

Lecture 63 Linear Regression - Exercise

Section 6: Logistic Regression

Lecture 64 Introduction to Logistic Regression

Lecture 65 A Simple Example of a Logistic Regression in Python

Lecture 66 What is the Difference Between a Logistic and a Logit Function?

Lecture 67 Your First Logistic Regression

Lecture 68 Your First Logistic Regression - Exercise

Lecture 69 A Coding Tip (optional)

Lecture 70 Going through the Regression Summary Table

Lecture 71 Going through the Regression Summary Table - Exercise

Lecture 72 Interpreting the Odds Ratio

Lecture 73 Dummies in a Logistic Regression

Lecture 74 Dummies in a Logistic Regression - Exercise

Lecture 75 Assessing the Accuracy of a Classification Model

Lecture 76 Assessing the Accuracy of a Classification Model - Exercise

Lecture 77 Underfitting and Overfitting

Lecture 78 Testing our Model and Bulding a Confusion Matrix

Lecture 79 Testing our Model and Bulding a Confusion Matrix - Exercise

Section 7: Cluster Analysis

Lecture 80 Introduction to Cluster Analysis

Lecture 81 Examples of Clustering

Lecture 82 Classification vs Clustering

Lecture 83 Math Concepts Needed to Proceed

Lecture 84 K-Means Clustering

Lecture 85 A Hands on Example of K-Means

Lecture 86 A Hands on Example of K-Means - Exercise

Lecture 87 Categorical Data in Cluster Analysis

Lecture 88 Categorical Data in Cluster Analysis - Exercise

Lecture 89 The Elbow Method or How to Choose the Number of Clusters

Lecture 90 The Elbow Method or How to Choose the Number of Clusters - Exercise

Lecture 91 Pros and Cons of K-Means

Lecture 92 Standardization of Features when Clustering

Lecture 93 Cluster Analysis and Regression Analysis

Lecture 94 Practical Example: Market Segmentation (Part 1)

Lecture 95 Practical Example: Market Segmentation (Part 2)

Lecture 96 What Can be Done with Cluster Analysis?

Lecture 97 EXERCISE: Species Segmentation with Cluster Analysis (Part 1)

Lecture 98 EXERCISE: Species Segmentation with Cluster Analysis (Part 2)

Section 8: Cluster Analysis: Additional Topics

Lecture 99 Other Types of Clustering

Lecture 100 The Dendrogram

Lecture 101 Heatmaps

Lecture 102 Completing 100%

This course is for you, if you want to become a successful data scientist,This course is great if you want to get acquainted with the fundamental machine learning methods,This course is ideal for you, if you are a just getting started and want to gradually build up valuable skills in machine learning and data science