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You're reading from Machine Learning and Data Science with Python: A Complete Beginners Guide Comprehensive beginners guide for machine learning and data science with Python for programming beginners

Product type Video

Published in May 2019

Last Updated in Feb 2026

Publisher Packt

ISBN-13 9781838980689

Length 10hrs 19mins

Edition 1st Edition

Languages

Python

Concepts

Data Science

Author (1):

Abhilash Nelson

View More author details

Table of Contents (48) Chapters

1. Course Overview and Table of Contents

Course Overview and Table of Contents

2. Introduction to Machine Learning FREE CHAPTER

Introduction to Machine Learning

3. System and Environment Preparation

System and Environment Preparation

4. Learn Basics of Python

Learn Basics of Python

5. Learn Basics of NumPy

Learn Basics of NumPy

6. Learn Basics of Matplotlib

Learn Basics of Matplotlib

7. Learn Basics of Pandas

Learn Basics of Pandas

8. Understanding the CSV Data File

Understanding the CSV Data File

9. Load and Read CSV Data File

Load and Read CSV Data File

10. Dataset Summary

Dataset Summary

11. Dataset Visualization

Dataset Visualization

12. Data Preparation

Data Preparation

13. Feature Selection

Feature Selection

14. Refresher Session - the Mechanism of Re-Sampling, Training, and Testing

Refresher Session - the Mechanism of Re-Sampling, Training, and Testing

15. Algorithm Evaluation Techniques

Algorithm Evaluation Techniques

16. Algorithm Evaluation Metrics

Algorithm Evaluation Techniques

17. Classification Algorithm Spot Check - Logistic Regression

Classification Algorithm Spot Check - Logistic Regression

18. Classification Algorithm Spot Check - Linear Discriminant Analysis

Classification Algorithm Spot Check - Linear Discriminant Analysis

19. Classification Algorithm Spot Check - K-Nearest Neighbors

Classification Algorithm Spot Check - K-Nearest Neighbors

20. Classification Algorithm Spot Check - Naive Bayes

Classification Algorithm Spot Check - Naive Bayes

21. Classification Algorithm Spot Check – CART

Classification Algorithm Spot Check – CART

22. Classification Algorithm Spot Check - Support Vector Machines

Classification Algorithm Spot Check - Support Vector Machines

23. Regression Algorithm Spot Check - Linear Regression

Regression Algorithm Spot Check - Linear Regression

24. Regression Algorithm Spot Check - Ridge Regression

Regression Algorithm Spot Check - Ridge Regression

25. Regression Algorithm Spot Check - LASSO Linear Regression

Regression Algorithm Spot Check - LASSO Linear Regression

26. Regression Algorithm Spot Check - Elastic Net Regression

Regression Algorithm Spot Check - Elastic Net Regression

27. Regression Algorithm Spot Check - K-Nearest Neighbors

Regression Algorithm Spot Check - K-Nearest Neighbors

28. Regression Algorithm Spot Check – CART

Regression Algorithm Spot Check – CART

29. Regression Algorithm Spot Check - Support Vector Machines (SVM)

Regression Algorithm Spot Check - Support Vector Machines

30. Compare Algorithms - Part 1: Choosing the Best Machine Learning Model

Compare Algorithms - Part 1: Choosing the Best Machine Learning Model

31. Compare Algorithms - Part 2: Choosing the Best Machine Learning Model

Compare Algorithms - Part 2: Choosing the Best Machine Learning Model

32. Pipelines: Data Preparation and Data Modelling

Pipelines: Data Preparation and Data Modelling

33. Pipelines: Feature Selection and Data Modelling

Pipelines: Feature Selection and Data Modelling

34. Performance Improvement: Ensembles – Voting

Performance Improvement: Ensembles – Voting

35. Performance Improvement: Ensembles – Bagging

Performance Improvement: Ensembles – Bagging

36. Performance Improvement: Ensembles – Boosting

Performance Improvement: Ensembles – Boosting

37. Performance Improvement: Parameter Tuning Using Grid Search

Performance Improvement: Parameter Tuning Using Grid Search

38. Performance Improvement: Parameter Tuning Using Random Search

Performance Improvement: Parameter Tuning Using Random Search

39. Export, Save and Load Machine Learning Models: Pickle

Export, Save and Load Machine Learning Models: Pickle

40. Export, Save and Load Machine Learning Models: Joblib

Export, Save and Load Machine Learning Models Joblib

41. Finalizing a Model - Introduction and Steps

Finalizing a Model - Introduction and Steps

42. Finalizing a Classification Model - the Pima Indian Diabetes Dataset

Finalizing a Classification Model - the Pima Indian Diabetes Dataset

43. Quick Session: Imbalanced Dataset - Issue Overview and Steps

Quick Session: Imbalanced Dataset - Issue Overview and Steps

44. Iris Dataset: Finalizing Multi-Class Dataset

Iris Dataset: Finalizing Multi-Class Dataset

45. Finalizing a Regression Model - the Boston Housing Price Dataset

Finalizing a Regression Model - the Boston Housing Price Dataset

46. Real-Time Predictions: Using the Pima Indian Diabetes Classification Model

Real-Time Predictions: Using the Pima Indian Diabetes Classification Model

47. Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset

Real-Time Predictions: Using Iris Flowers Multi-Class Classification Dataset

48. Real-Time Predictions: Using the Boston Housing Regression Model

Real-Time Predictions: Using the Boston Housing Regression Model

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You're reading from Machine Learning and Data Science with Python: A Complete Beginners Guide Comprehensive beginners guide for machine learning and data science with Python for programming beginners

Table of Contents (48) Chapters

Authors (1)

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You're reading from Machine Learning and Data Science with Python: A Complete Beginners Guide Comprehensive beginners guide for machine learning and data science with Python for programming beginners

Table of Contents (48) Chapters

Authors (1)

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