# Machine Learning in Python Level 1 Beginner

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

4.7/5

## Machine Learning in Python Level 1 Beginner

### What you’ll learn

Machine Learning
The Machine Learning Process
Regression
Ordinary Least Squares
Simple Linear Regression
Splitting your data into a Training set and a Test set
Multiple Linear Regression
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

### Overview

Section 1: Introduction

Lecture 1 Welcome to The Machine Learning Series Level 1

Lecture 2 The Machine Learning Process

Lecture 3 Get all the Datasets, Codes and Slides here

Lecture 4 A walk-through the course materials and Google Colab

Section 2: Regression

Lecture 5 What is Regression?

Lecture 6 Simple Linear Regression

Lecture 7 Ordinary Least Squares

Lecture 8 Multiple Linear Regression

Lecture 9 Assumptions of Linear Regression

Lecture 10 Linear Regression Hands-On – Step 1

Lecture 11 Linear Regression Hands-On – Step 2

Lecture 12 Linear Regression Hands-On – Step 3

Lecture 13 Training Set and Test Set

Lecture 14 Linear Regression Hands-On – Step 4

Lecture 15 Linear Regression Hands-On – Step 5

Lecture 16 Linear Regression Hands-On – Step 6

Lecture 17 Linear Regression Hands-On – Step 7

Lecture 18 Linear Regression Hands-On – Step 8

Lecture 19 R-Squared

Lecture 21 Linear Regression Hands-On – Step 9

Lecture 22 Linear Regression Hands-On – Step 10

Section 3: Classification

Lecture 23 What is Classification?

Lecture 24 Logistic Regression

Lecture 25 Maximum Likelihood

Lecture 26 Logistic Regression Hands-On – Step 1

Lecture 27 Logistic Regression Hands-On – Step 2

Lecture 28 Logistic Regression Hands-On – Step 3

Lecture 29 Logistic Regression Hands-On – Step 4

Lecture 30 Feature Scaling

Lecture 31 Logistic Regression Hands-On – Step 5

Lecture 32 Logistic Regression Hands-On – Step 6

Lecture 33 Logistic Regression Hands-On – Step 7

Lecture 34 Logistic Regression Hands-On – Step 8a

Lecture 35 Logistic Regression Hands-On – Step 8b

Lecture 36 Confusion Matrix and Accuracy

Lecture 37 Logistic Regression Hands-On – Step 9

Lecture 38 Logistic Regression Hands-On – Step 10

Section 4: Clustering

Lecture 39 What is Clustering?

Lecture 40 K-Means Clustering

Lecture 41 The Elbow Method

Lecture 42 K-Means Clustering – Step 1

Lecture 43 K-Means Clustering – Step 2

Lecture 44 K-Means Clustering – Step 3a

Lecture 45 K-Means Clustering – Step 3b

Lecture 46 K-Means++

Lecture 47 K-Means Clustering – Step 4

Lecture 48 K-Means Clustering – Step 5a

Lecture 49 K-Means Clustering – Step 5b

Section 5: Wrapping up

Lecture 50 Congrats!!