Master Machine Learning in Python with Scikit-Learn

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1. Introduction and Overview

1.1 01 - Introduction to the Course.pdf

1. Introduction to the Course.mp4

03:12

2.1 All Course Materials.zip

2. All the material in the course!.html

3.1 Anaconda Homepage.html

3. (Background) Introduction to Jupyter Notebooks.mp4

08:27

4.1 notebook - introduction and overview.zip

4.2 NumPy Homepage.html

4.3 solution - introduction and overview.zip

4. (Background) Introduction to NumPy.mp4

11:30

5.1 Pandas Homepage.html

5. (Background) Introduction to Pandas.mp4

12:35

2. What is Machine Learning

1.1 02 - What is Machine Learning.pdf

1. What is Machine Learning.mp4

04:49

2. ML Terminology.mp4

06:27

3. Basic ML Quiz.html

4. Anatomy of a ML Project.mp4

09:02

5. The Steps in ML.html

6.1 notebook - what is machine learning.zip

6.2 solution - what is machine learning.zip

6. Introducing Scikit Learn.mp4

08:16

7. Importing a Machine Learning Model.html

8. Exploring the Diabetes Dataset.mp4

11:45

9. Further Resources.html

3. Our First Model Linear Regression

1.1 03 - Linear Regression.pdf

1. Introduction.mp4

01:23

2. Idea of Linear Regression.mp4

02:20

3. The Theory of Linear Regression.mp4

05:55

4. Linear Regression.html

5.1 notebook - linear regression.zip

5.2 solution - linear regression.zip

5. Linear Regression in Scikit-Learn.mp4

03:58

6. Your First Linear Regression Model!.html

7. Evaluating the Model.mp4

04:36

8. Evaluating the Model.html

9. Is our Model any Good.mp4

08:36

10. Splitting the Data into Training and Testing Sets.html

11. Evaluating using MSE.html

12. How is Training Done (Optional Theory).mp4

19:10

13. Further Resources.html

4. Binary Classification with Logistic Regression

1.1 04 - Binary Classification with Logistic Regression.pdf

1. Introduction.mp4

01:29

2. Binary Classification and Logistic Regression.mp4

07:07

3. Binary Classification and Logistic Regression.html

4.1 notebook - binary classification with logistic regression.zip

4.2 solution - binary classification with logistic regression.zip

4. The Iris Dataset.mp4

09:40

5. Implementing Logistic Regression.mp4

07:09

6. Implementing Logistic Regression.html

7. Accuracy Score.mp4

05:00

8. Accuracy Score.html

9. Predictors and Accuracy Score.html

10. Further Resources.html

5. Preprocessing and Pipelines

1.1 05 - Preprocessing and Pipelines.pdf

1. Introduction.mp4

01:14

2. Preprocessing.mp4

07:40

3. Preprocessing.html

4.1 notebook - preprocessing and pipelines.zip

4.2 solution - preprocessing and pipelines.zip

4. Filling in Missing Values.mp4

09:24

5. Filling in Missing Values.html

6. Choosing Relevant Features.mp4

07:33

7. Standard Scaling in Scikit-Learn.mp4

08:48

8. Standard Scaling.html

9. Pipelines.mp4

06:38

10. Pipelines.html

11. Further Resources.html

6. Polynomial Regression and Overfitting

1.1 06 - Polynomial Regression and Overfitting.pdf

1. Introduction.mp4

01:45

2. Understanding Polynomial Regression.mp4

08:06

3. Polynomial Regression.html

4.1 notebook - polynomial regression and overfitting.zip

4.2 solution - polynomial regression and overfitting.zip

4. Adding Polynomial Features Manually.mp4

06:40

5. Adding Polynomial Features.html

6. Evaluating with Mean Absolute Error.mp4

09:39

7. Mean Absolute Error.html

8. Using the Polynomial Features Class.mp4

06:16

9. Adding Polynomial Features Properly.html

10. Fitting Everything Into a Pipeline.mp4

06:06

11. Overfitting and Underfitting.mp4

05:43

12. Overfitting and Underfitting.html

13. Overfitting in Practice.mp4

06:02

14. Further Resources.html

7. Project 1 - Regression

1.1 07 - Project 1 - Regression.pdf

1.2 notebook - regression project.zip

1.3 solution - regression project.zip

1. Introduction.mp4

02:00

2. Solution Regression Project.mp4

13:40

8. Decision Trees and Different Metrics

1.1 08 - Decision Trees and Different Metrics.pdf

1. Introduction.mp4

00:52

2. Introduction to Trees.mp4

03:08

3. Decision Trees.mp4

04:29

4. Trees and Decision Trees.html

5.1 notebook - decision trees and different metrics.zip

5.2 solution - decision trees and different metrics.zip

5. Implementing Decision Trees.mp4

06:20

6. Decision Trees for Regression.html

7. False Positives and False Negatives.mp4

03:44

8. Understanding Precision and Recall.mp4

04:07

9. Unbalanced Datasets.html

10. Using Precision and Recall.mp4

06:37

11. Finding Precision of a Decision Tree.html

12. Further Resources.html

9. Ensemble Learning and Random Forests

1.1 09 - Ensemble Learning and Random Forests.pdf

1. Introduction.mp4

00:57

2. What is Ensemble Learning.mp4

04:43

3. Ensemble Learning.html

4.1 notebook - ensemble learning and random forests.zip

4.2 solution - ensemble learning and random forests.zip

4. Creating Multiple Models Fast.mp4

04:20

5. Creating an Ensemble Majority Vote.mp4

07:02

6. Training and Fitting Multiple Models.html

7. Weak Learners and Bagging.mp4

05:45

8. Weak Learners and Bagging.html

9. Using Random Forests.mp4

06:52

10. Random Forests.html

11. Further Resources.html

10. One-Hot-Encoding and Cross-Validation

1.1 10 - One-Hot-Encoding and Cross-Validation.pdf

1. Introduction.mp4

01:20

2. One Hot Encoding.mp4

02:59

3. One Hot Encoding.html

4.1 notebook - one-hot-encoding and cross-validation.zip

4.2 solution - one-hot-encoding and cross-validation.zip

4. Using One Hot Encoding.mp4

08:23

5. Using One Hot Encoding.html

6. Cross-Validation.mp4

02:08

7. Using Cross-Validation.mp4

05:51

8. Using Cross-Validation.html

9. Validation and Test Set.mp4

02:11

10. Validation, Test Set, and Cross-Validation.html

11. One Hot Encoding and Pipelines.mp4

06:59

12. Cross-Validation and Pipelines.mp4

05:05

13. Cross-Validation and Pipelines.html

14. Further Resources.html

11. Regularization and the Bias-Variance Tradeoff

1.1 11 - Regularization and the Bias-Variance Tradeoff.pdf

1. Introduction.mp4

01:05

2. Regularization (or Shrinkage).mp4

03:42

3. Regularization (or Shrinkage).html

4.1 cleaned tips.csv

4.2 notebook - regularization and the bias-variance tradeoff.zip

4.3 solution - regularization and the bias-variance tradeoff.zip

4. Lasso and Ridge Regression.mp4

06:42

5. Trying out Ridge Regression.html

6. Bias-Variance Tradeoff.mp4

05:05

7. Bias and Variance.html

8. Finding a Good Parameter Value.mp4

06:31

9. Further Resources.html

12. SVMs and Hyperparameters

1.1 12 - SVMs and Hyperparameters.pdf

1. Introduction.mp4

00:42

2. Support Vector Machine.mp4

02:33

3. Support Vector Machine.html

4.1 notebook - support vector machines and hyperparameters.zip

4.2 solution - support vector machines and hyperparameters.zip

4. Implementing SVM.mp4

04:16

5. Hyperparameters.mp4

06:09

6. SVMs and Hyperparameters.html

7. Implementing Grid Search.mp4

07:04

8. Implementing Grid Search.html

9. Further Resources.html

13. Project 2 - Classification

1.1 13 - Project 2 - Classification.pdf

1.2 notebook - classification project.zip

1.3 solution - classification project.zip

1. Introduction.mp4

02:35

2. Solution Classification Project.mp4

18:47

14. Dimentionality Reduction Techniques

1.1 14 - Dimensionality Reduction Techniques.pdf

1. Introduction.mp4

01:17

2. Dimensionality Reduction.mp4

05:12

3. Dimensionality Reduction.html

4.1 notebook - dimensionality reduction techniques.zip

4.2 solution - dimensionality reduction techniques.zip

4. Introducing the CovType Dataset.mp4

05:43

5. Reduction Based on Correlation.mp4

06:51

6. Reduction Based on Variance.mp4

06:50

7. Using VarianceThreshold to Reduce Dimensionality.html

8. Principal Component Analysis (PCA).mp4

06:48

9. Principal Component Analysis.html

10. Implementing PCA.mp4

03:01

11. Implementing PCA.html

12. Further Resources.html

15. KNN and Model Persistence

1.1 15 - KNN and Model Persistence.pdf

1. Introduction.mp4

00:59

2. K-Nearest Neighbors.mp4

02:37

3. K-Nearest Neighbors.html

4.1 notebook - knn and model persistence.zip

4.2 solution - knn and model persistence.zip

4. Implementing KNN.mp4

07:06

5. Implementing KNN.html

6. Model Persistence.mp4

03:11

7. Using Model Persistence.mp4

04:27

8. Model Persistence.html

9. Further Resources.html

16. The Basics of Neural Networks

1.1 16 - The Basics of Neural Networks.pdf

1. Introduction.mp4

01:04

2. What are Neural Networks.mp4

04:39

3. What are Neural Networks.html

4. Weights and Activation Functions.mp4

07:36

5. Weights and Activation Functions.html

6.1 notebook - the basics of neural networks.zip

6.2 solution - the basics of neural networks.zip

6. Basic Usage of MLPClassifier.mp4

03:53

7.1 Keras Homepage.html

7. Parameters and Keras.mp4

04:02

8. Parameters and Keras.html

9. Further Resources.html

17. Intro to Unsupervised ML

1.1 17 - Intro to Unsupervised ML.pdf

1. Introduction.mp4

01:07

2. What is Unsupervised Learning.mp4

08:18

3. What is Unsupervised Learning.html

4. K-Means Clustering.mp4

05:04

5.1 notebook - intro to unsupervised ml.zip

5.2 solution - intro to unsupervised ml.zip

5. Implementing K-Means Clustering.mp4

11:55

6. Implementing K-Means Clustering.html

7. Further Resources.html

18. Project 3 - Unsupervised

1.1 18 - Project 3 - Unsupervised ML.pdf

1.2 Dataset Description.html

1.3 notebook - unsupervised project.zip

1.4 Sales Transactions Dataset Weekly.csv

1.5 solution - unsupervised project.zip

1. Introduction.mp4

02:05

2.1 module 18 - unsupervised project solution.zip

2. Solution Unsupervised Project.mp4

16:23

19. Further Resources and Goodbye

1.1 19 - The End of Our Journey.pdf

1.2 Hands on Machine Learning Book.html

1.3 Kaggle.html

1. The End of Our Journey.mp4

01:02

Description

A comprehensive introduction to machine learning and data science in Python with the scikit-learn library!

What You'll Learn?

Understand the fundamental concepts in machine learning.
Use Python and Scikit-Learn to create machine learning models.
Make data ready for machine learning algorithms to use.
Critically examine machine learning models and algorithms.
Use regression models to predict continuous values.
Use classification models to predict classes.
Use unsupervised algorithms to cluster data.

Who is this for?

Anyone who wants to break into machine learning and data science

Beginner Python developers curious about machine learning

What You Need to Know?

Some basic knowledge of Python

More details

Description
Do you want to get started with machine learning and data science in Python? This course is both a comprehensive and hands-on introduction to machine learning! We will use Scikit-Learn, which is the most awesome library in Python for machine learning!

What this course is all about:
In this course, we will teach you the ins and outs of both machine learning in general, as well as the Python library Scikit-Learn. Scikit-Learn is not only super popular but is also incredibly powerful for many machine learning tasks. If you are interested in machine learning in general, or specifically in Scikit-Learn, then this is the course for you. The course will teach you everything you need to know to professionally use Scikit-Learn for machine learning. We will start with the basics, and then gradually move on to more complicated topics.

Why choose us?
This course is a comprehensive introduction to machine learning in Python by using Scikit-Learn! We don't shy away from the technical stuff and want you to stand out with your newly learned Scikit-Learn skills.
The course is filled with carefully made exercises that will reinforce the topics we teach. In between videos, we give small exercises that help you reinforce the material. Additionally, we have larger exercises where you will be given a Jupiter Notebook sheet and asked to solve a series of questions that revolve around a single topic. The exercises include data processing and cleaning, making them much closer to real-life machine learning.
We're a couple (Eirik and Stine) who love to create high-quality courses! Eirik has used Scikit-Learn professional as a data scientist, while Stine has experience with teaching programming at the university level. We both love Scikit-Learn and can't wait to teach you all about it!

Topics we will cover:
We will cover a lot of different topics in this course. In order of appearance, they are:
Introduction to Scikit-Learn
Linear Regression
Logistic Regression
Preprocessing and Pipelines
Polynomial Regression
Decision Trees and Random Forests
Cross-Validation
Regularization Techniques
Support Vector Machines
Dimensionality Reduction & PCA
Basics of Neural Networks
Supervised and Unsupervised Learning
and much more! By completing our course, you will be comfortable with both machine learning and the Python library Scikit-Learn. This gives you a great starting point for working professionally with machine learning.

Still not decided?
The course has a 30-day refund policy, so if you are unhappy with the course, then you can get your money back painlessly. If are still uncertain after reading this, then take a look at some of the free previews and see if you enjoy them.Â Hope to see you soon!
Who this course is for:
Anyone who wants to break into machine learning and data science
Beginner Python developers curious about machine learning

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Hi there! We're a couple who love teaching about topics related to mathematics and informatics. Are you perhaps interested in data science or scientific programming? Check out any of our two courses on Udemy below.We are planning on making many interesting new courses in the future. Do you have a suggestion for us? Don't hesitate to send us a message.

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