Machine Learning and Data Science with Python: A Complete Beginners Guide

Packt Publishing

• Course Overview & Table of Contents 08:59

  • 1: Course Overview & Table of Contents 08:59

• Introduction to Machine Learning 10:31

  • 2: Introduction to Machine Learning 10:31

• System and Environment preparation 14:05

  • 3: System and Environment preparation 14:05

• Learn Basics of python 35:18

  • 4: Learn Basics of python 35:18

• Learn Basics of NumPy 18:18

  • 5: Learn Basics of NumPy 18:18

• Learn Basics of Matplotlib 07:06

  • 6: Learn Basics of Matplotib 07:06

• Learn Basics of Pandas 12:47

  • 7: Learn Basics of Pandas 12:47

• Understanding the CSV data file 08:55

  • 8: Understanding the CSV data file 08:55

• Load and Read CSV data file 18:08

  • 9: Load and Read CSV data file 18:08

• Dataset Summary 35:46

  • 10: Dataset Summary 35:46

• Dataset Visualization 30:43

  • 11: Dataset Visualization 30:43

• Data Preparation 51:29

  • 12: Data Preparation 51:29

• Feature Selection 52:09

  • 13: Feature Selection 52:09

• Refresher Session - The Mechanism of Re-sampling, Training and Testing 12:04

  • 14: Refresher Session - The Mechanism of Re-sampling, Training and Testing 12:04

• Algorithm Evaluation Techniques 38:27

  • 15: Algorithm Evaluation Techniques 38:27

• Algorithm Evaluation Metrics 1:00:38

  • 16: Algorithm Evaluation Metrics 1:00:38

• Classification Algorithm Spot Check - Logistic Regression 11:31

  • 17: Classification Algorithm Spot Check - Logistic Regression 11:31

• Classification Algorithm Spot Check - Linear Discriminant Analysis 03:48

  • 18: Classification Algorithm Spot Check - Linear Discriminant Analysis 03:48

• Classification Algorithm Spot Check - K-Nearest Neighbors 04:49

  • 19: Classification Algorithm Spot Check - K-Nearest Neighbors 04:49

• Classification Algorithm Spot Check - Naive Bayes 04:00

  • 20: Classification Algorithm Spot Check - Naive Bayes 04:00

• Classification Algorithm Spot Check – CART 03:48

  • 21: Classification Algorithm Spot Check – CART 03:48

• Classification Algorithm Spot Check - Support Vector Machines 04:36

  • 22: Classification Algorithm Spot Check - Support Vector Machines 04:36

• Regression Algorithm Spot Check - Linear Regression 07:38

  • 23: Regression Algorithm Spot Check - Linear Regression 07:38

• Regression Algorithm Spot Check - Ridge Regression 03:13

  • 24: Regression Algorithm Spot Check - Ridge Regression 03:13

• Regression Algorithm Spot Check - LASSO Linear Regression 02:54

  • 25: Regression Algorithm Spot Check - LASSO Linear Regression 02:54

• Regression Algorithm Spot Check - Elastic Net Regression 02:09

  • 26: Regression Algorithm Spot Check - Elastic Net Regression 02:09

• Regression Algorithm Spot Check - K-Nearest Neighbors 05:56

  • 27: Regression Algorithm Spot Check - K-Nearest Neighbors 05:56

• Regression Algorithm Spot Check – CART 04:03

  • 28: Regression Algorithm Spot Check – CART 04:03

• Regression Algorithm Spot Check - Support Vector Machines (SVM) 04:03

  • 29: Regression Algorithm Spot Check - Support Vector Machines (SVM) 04:03

• Compare Algorithms - Part 1: Choosing the best Machine Learning Model 08:51

  • 30: Compare Algorithms - Part 1: Choosing the best Machine Learning Model 08:51

• Compare Algorithms - Part 2: Choosing the best Machine Learning Model 05:01

  • 31: Compare Algorithms - Part 2: Choosing the best Machine Learning Model 05:01

• Pipelines: Data Preparation and Data Modelling 10:56

  • 32: Pipelines: Data Preparation and Data Modelling 10:56

• Pipelines: Feature Selection and Data Modelling 09:35

  • 33: Pipelines: Feature Selection and Data Modelling 09:35

• Performance Improvement: Ensembles – Voting 06:57

  • 34: Performance Improvement: Ensembles – Voting 06:57

• Performance Improvement: Ensembles – Bagging 08:21

  • 35: Performance Improvement: Ensembles – Bagging 08:21

• Performance Improvement: Ensembles – Boosting 04:35

  • 36: Performance Improvement: Ensembles – Boosting 04:35

• Performance Improvement: Parameter Tuning using Grid Search 07:36

  • 37: Performance Improvement: Parameter Tuning using Grid Search 07:36

• Performance Improvement: Parameter Tuning using Random Search 05:59

  • 38: Performance Improvement: Parameter Tuning using Random Search 05:59

• Export, Save and Load Machine Learning Models: Pickle 09:41

  • 39: Export, Save and Load Machine Learning Models: Pickle 09:41

• Export, Save and Load Machine Learning Models: Joblib 05:52

  • 40: Export, Save and Load Machine Learning Models: Joblib 05:52

• Finalizing a Model - Introduction and Steps 06:39

  • 41: Finalizing a Model - Introduction and Steps 06:39

• Finalizing a Classification Model - The Pima Indian Diabetes Dataset 06:45

  • 42: Finalizing a Classification Model - The Pima Indian Diabetes Dataset 06:45

• Quick Session: Imbalanced Data Set - Issue Overview and Steps 08:35

  • 43: Quick Session: Imbalanced Data Set - Issue Overview and Steps 08:35

• Iris Dataset: Finalizing Multi-Class Dataset 09:15

  • 44: Iris Dataset: Finalizing Multi-Class Dataset 09:15

• Finalizing a Regression Model - The Boston Housing Price Dataset 08:16

  • 45: Finalizing a Regression Model - The Boston Housing Price Dataset 08:16

• Real-time Predictions: Using the Pima Indian Diabetes Classification 06:39

  • 46: Real-time Predictions: Using the Pima Indian Diabetes Classification 06:39

• Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset 03:25

  • 47: Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset 03:25

• Real-time Predictions: Using the Boston Housing Regression Model 07:44

  • 48: Real-time Predictions: Using the Boston Housing Regression Model 07:44

Artificial intelligence, machine learning, and deep learning neural networks are the most used terms in the technology world today. They're also the most misunderstood and confused terms. Artificial intelligence is a broad spectrum of science that tries to make machines intelligent like humans, while machine learning and neural networks are two subsets that sit within this vast machine learning platform. But in this course, you will focus mainly on machine learning, which will include preparing your machine to make it ready for a prediction test.
You will be using Python as your programming language. Python is a great tool for the development of programs that perform data analysis and prediction. It has a variety of classes and features that perform complex mathematical analyses and provide solutions in just a few lines of code, making it easier for you to get up to speed with data science and machine learning.

Machine learning and data science jobs are among the most lucrative in the technology industry in recent times. Exploring this course will help you get well-versed with essential concepts and prepare you for a career in these fields.

What You Will Learn

• Install Python and required libraries
• Choose the best machine learning model
• Automate and combine workflows with pipeline
• Look at performance improvement with ensembles
• Study performance improvement with algorithm parameter tuning
• Finalize a machine learning project

About the Author

Abilash Nelson is a pioneering, talented and security-oriented Android/iOS mobile and PHP/Python web application developer offering more than eight years' overall IT experience, which involves designing, implementing, integrating, testing, and supporting impactful web and mobile applications.
His experience with PHP/Python programming is an added advantage for server-based Android and iOS client applications.

This course is for beginners who are interested in machine learning using Python.

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