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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