Master Machine Learning in Python with Scikit-Learn

سرفصل های دوره

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

1. Introduction and Overview

1.1 01 - Introduction to the Course.pdf

1. Introduction to the Course

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

4.1 notebook - introduction and overview.zip

4.2 NumPy Homepage.html

4.3 solution - introduction and overview.zip

4. (Background) Introduction to NumPy

5.1 Pandas Homepage.html

5. (Background) Introduction to Pandas

2. What is Machine Learning

1.1 02 - What is Machine Learning.pdf

1. What is Machine Learning

2. ML Terminology

3. Basic ML Quiz.html

4. Anatomy of a ML Project

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

7. Importing a Machine Learning Model.html

8. Exploring the Diabetes Dataset

9. Further Resources.html

3. Our First Model Linear Regression

1.1 03 - Linear Regression.pdf

1. Introduction

2. Idea of Linear Regression

3. The Theory of Linear Regression

4. Linear Regression.html

5.1 notebook - linear regression.zip

5.2 solution - linear regression.zip

5. Linear Regression in Scikit-Learn

6. Your First Linear Regression Model!.html

7. Evaluating the Model

8. Evaluating the Model.html

9. Is our Model any Good

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

11. Evaluating using MSE.html

12. How is Training Done (Optional Theory)

13. Further Resources.html

4. Binary Classification with Logistic Regression

1.1 04 - Binary Classification with Logistic Regression.pdf

1. Introduction

2. Binary Classification and Logistic Regression

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

5. Implementing Logistic Regression

6. Implementing Logistic Regression.html

7. Accuracy Score

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

2. Preprocessing

3. Preprocessing.html

4.1 notebook - preprocessing and pipelines.zip

4.2 solution - preprocessing and pipelines.zip

4. Filling in Missing Values

5. Filling in Missing Values.html

6. Choosing Relevant Features

7. Standard Scaling in Scikit-Learn

8. Standard Scaling.html

9. Pipelines

10. Pipelines.html

11. Further Resources.html

6. Polynomial Regression and Overfitting

1.1 06 - Polynomial Regression and Overfitting.pdf

1. Introduction

2. Understanding Polynomial Regression

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

5. Adding Polynomial Features.html

6. Evaluating with Mean Absolute Error

7. Mean Absolute Error.html

8. Using the Polynomial Features Class

9. Adding Polynomial Features Properly.html

10. Fitting Everything Into a Pipeline

11. Overfitting and Underfitting

12. Overfitting and Underfitting.html

13. Overfitting in Practice

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

2. Solution Regression Project

8. Decision Trees and Different Metrics

1.1 08 - Decision Trees and Different Metrics.pdf

1. Introduction

2. Introduction to Trees

3. Decision Trees

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

6. Decision Trees for Regression.html

7. False Positives and False Negatives

8. Understanding Precision and Recall

9. Unbalanced Datasets.html

10. Using Precision and Recall

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

2. What is Ensemble Learning

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

5. Creating an Ensemble Majority Vote

6. Training and Fitting Multiple Models.html

7. Weak Learners and Bagging

8. Weak Learners and Bagging.html

9. Using Random Forests

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

2. One Hot Encoding

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

5. Using One Hot Encoding.html

6. Cross-Validation

7. Using Cross-Validation

8. Using Cross-Validation.html

9. Validation and Test Set

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

11. One Hot Encoding and Pipelines

12. Cross-Validation and Pipelines

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

2. Regularization (or Shrinkage)

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

5. Trying out Ridge Regression.html

6. Bias-Variance Tradeoff

7. Bias and Variance.html

8. Finding a Good Parameter Value

9. Further Resources.html

12. SVMs and Hyperparameters

1.1 12 - SVMs and Hyperparameters.pdf

1. Introduction

2. Support Vector Machine

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

5. Hyperparameters

6. SVMs and Hyperparameters.html

7. Implementing Grid Search

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

2. Solution Classification Project

14. Dimentionality Reduction Techniques

1.1 14 - Dimensionality Reduction Techniques.pdf

1. Introduction

2. Dimensionality Reduction

3. Dimensionality Reduction.html

4.1 notebook - dimensionality reduction techniques.zip

4.2 solution - dimensionality reduction techniques.zip

4. Introducing the CovType Dataset

5. Reduction Based on Correlation

6. Reduction Based on Variance

7. Using VarianceThreshold to Reduce Dimensionality.html

8. Principal Component Analysis (PCA)

9. Principal Component Analysis.html

10. Implementing PCA

11. Implementing PCA.html

12. Further Resources.html

15. KNN and Model Persistence

1.1 15 - KNN and Model Persistence.pdf

1. Introduction

2. K-Nearest Neighbors

3. K-Nearest Neighbors.html

4.1 notebook - knn and model persistence.zip

4.2 solution - knn and model persistence.zip

4. Implementing KNN

5. Implementing KNN.html

6. Model Persistence

7. Using Model Persistence

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

2. What are Neural Networks

3. What are Neural Networks.html

4. Weights and Activation Functions

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

7.1 Keras Homepage.html

7. Parameters and Keras

8. Parameters and Keras.html

9. Further Resources.html

17. Intro to Unsupervised ML

1.1 17 - Intro to Unsupervised ML.pdf

1. Introduction

2. What is Unsupervised Learning

3. What is Unsupervised Learning.html

4. K-Means Clustering

5.1 notebook - intro to unsupervised ml.zip

5.2 solution - intro to unsupervised ml.zip

5. Implementing K-Means Clustering

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

2.1 module 18 - unsupervised project solution.zip

2. Solution Unsupervised Project

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

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تولید کننده: Udemy