The Machine Learning Series In Python: Level 1

Build a solid foundation in Machine Learning: Linear Regression, Logistic Regression and K-Means Clustering in Python

What you'll learn
Machine Learning
The Machine Learning Process
Regression
Ordinary Least Squares
Simple Linear Regression
Multiple Linear Regression
R-Squared
Adjusted R-Squared
Classification
Maximum Likelihood
Feature Scaling
Confusion Matrix
Accuracy
Clustering
K-Means Clustering
The Elbow Method
K-Means++
Build Machine Learning models in Python
Make Predictions
Requirements
Every single line of code will be fully explained so there are no prerequisites for coding skills
This is a foundational course, so no prior knowledge of Data Science is required
Some high-school level mathematics knowledge is recommended but not required
We use Google Colab for coding in Python which is very intuitive, but you can also use Jupyter or another IDE
Description
In this course you will master the foundations of Machine Learning and practice building ML models with real-world case studies. We will start from scratch and explain:What Machine Learning isThe Machine Learning Process of how to build a ML modelRegression: Predict a continuous numberSimple Linear RegressionOrdinary Least SquaresMultiple Linear RegressionR-SquaredAdjusted R-SquaredClassification: Predict a Category / ClassLogistic RegressionMaximum LikelihoodFeature ScalingConfusion MatrixAccuracyClustering: Predict / Identify a PatternK-Means ClusteringThe Elbow Method We will also do the following the three following practical activities:Real-World Case Study: Build a Multiple Linear Regression modelReal-World Case Study: Build a Logistic Regression modelReal-World Case Study: Build a K-Means Clustering modelThe Course Objectives are the following:- Get the right basics of how machine learning works and how models are built.- Understand what is regression.- Understand the theory behind the linear regression model.- Know how to build, train and evaluate a linear regression model for a real-world case study.- Understand what is classification.- Understand the theory behind the logistic regression model.- Understand and apply feature scaling including both normalization and standardization.- Know how to build, train and evaluate a logistic regression model for a real-world case study.- Understand what is clustering.- Understand the theory behind the k-means clustering model.- Know how to build, train and evaluate the k-means clustering model for a real-world case study.

Overview

Section 1: Introduction

Lecture 1 Welcome to The Machine Learning Series Level 1

Lecture 2 The Machine Learning Process

Section 2: Regression

Lecture 3 What is Regression?

Lecture 4 Simple Linear Regression

Lecture 5 Ordinary Least Squares

Lecture 6 Multiple Linear Regression

Lecture 7 Linear Regression Hands-On - Step 1

Lecture 8 Linear Regression Hands-On - Step 2

Lecture 9 Linear Regression Hands-On - Step 3

Lecture 10 Training Set and Test Set

Lecture 11 Linear Regression Hands-On - Step 4

Lecture 12 Linear Regression Hands-On - Step 5

Lecture 13 Linear Regression Hands-On - Step 6

Lecture 14 Linear Regression Hands-On - Step 7

Lecture 15 Linear Regression Hands-On - Step 8

Lecture 16 R-Squared

Lecture 17 Adjusted R-Squared

Lecture 18 Linear Regression Hands-On - Step 9

Lecture 19 Linear Regression Hands-On - Step 10

Section 3: Classification

Lecture 20 What is Classification?

Lecture 21 Logistic Regression

Lecture 22 Maximum Likelihood

Lecture 23 Logistic Regression Hands-On - Step 1

Lecture 24 Logistic Regression Hands-On - Step 2

Lecture 25 Logistic Regression Hands-On - Step 3

Lecture 26 Logistic Regression Hands-On - Step 4

Lecture 27 Feature Scaling

Lecture 28 Logistic Regression Hands-On - Step 5

Lecture 29 Logistic Regression Hands-On - Step 6

Lecture 30 Logistic Regression Hands-On - Step 7

Lecture 31 Logistic Regression Hands-On - Step 8a

Lecture 32 Logistic Regression Hands-On - Step 8b

Lecture 33 Confusion Matrix and Accuracy

Lecture 34 Logistic Regression Hands-On - Step 9

Lecture 35 Logistic Regression Hands-On - Step 10

Section 4: Clustering

Lecture 36 What is Clustering?

Lecture 37 K-Means Clustering

Lecture 38 The Elbow Method

Lecture 39 K-Means Clustering - Step 1

Lecture 40 K-Means Clustering - Step 2

Lecture 41 K-Means Clustering - Step 3a

Lecture 42 K-Means Clustering - Step 3b

Lecture 43 K-Means Clustering - Step 4

Lecture 44 K-Means Clustering - Step 5a

Lecture 45 K-Means Clustering - Step 5b

Anyone interested in Data Science,Anyone who wants to become a Data Scientist,Anyone interested in Machine Learning,Anyone who wants to become a ML or AI engineer,Data Science professionals,Machine Learning professionals,Anyone who wants to add Machine Learning to their CV or career toolkit