## 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

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

Do you want to learn Machine Learning but don’t know where to start? Have you been looking for a beginner-friendly course that will equip you with powerful tools for your career?You’ve come to the right place!This is Machine Learning in Python Level 1… and we will help you get started.My name is Kirill Eremenko, I’m a Data Science instructor with over 7 years of experience, and together with my co-instructor Hadelin de Ponteves we have taught over 2M students Worldwide.And now, we’ve created this course to help YOU get on track with Machine Learning and start applying it in YOUR career.This course has 3 main sections:First, we will dive into Regression, where we will learn to predict continuous variables and we will cover foundational concepts like Simple and Multiple Linear Regression, Ordinary Least Squares, Testing your Model, R-Squared and Adjusted R-Squared.In the second section you will master Logistic Regression, which is by far the most popular model for Classification. We will learn all about Maximum Likelihood, Feature Scaling, The Confusion Matrix, Accuracy Ratios…. and you will build your very first Logistic Regression!The third and final section is all about Clustering. We will investigate the concepts of unsupervised learning and you will practice using K-Means Clustering to discover previously unseen patterns in your data.Sound exciting?Well, in this course not only will you learn the theory behind all of these Machine Learning models, but you will also practice applying them in different scenarios so that you are prepared for the Real World.Plus, you will get Python code templates which you can download and keep. These are invaluable tools which you can apply in your own projects right away.So, what are you waiting for?Sign up today and take your career to the next level with Machine Learning!

### 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 20 Adjusted 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!!

Lecture 51 Your Special Bonus

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

#### Course Information:

Udemy | English | 3h 39m | 1.17 GB

Created by: Hadelin de Ponteves

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