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

Description

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. All the code and supporting files for this course are available at https://github.com/PacktPublishing/Machine-Learning-and-Data-Science-with-Python-A-Complete-Beginners-Guide

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

Audience

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

Approach

An exhaustive course packed with step-by-step instructions, working examples, and helpful advice. This course is divided into clear chunks so you can learn at your own pace and focus on your own area of interest.

Key Features

Learn machine learning and data science using Python * A practical course designed for beginners who are interested in machine learning using Python * Work on predictions and case studies

Github Repo

https://github.com/packtpublishing/machine-learning-and-data-science-with-python-a-complete-beginners-guide

About the Author

Abhilash Nelson

Abhilash Nelson is a pioneering, talented, and security-oriented Android/iOS mobile and PHP/Python web application developer with more than eight years of IT experience involving designing, implementing, integrating, testing, and supporting impactful web and mobile applications. He has a master's degree in computer science and engineering and has PHP/Python programming experience, which is an added advantage for server-based Android and iOS client applications. Abhilash is currently a senior solution architect managing projects from start to finish to ensure high quality and innovative and functional design.

Course Outline

1. Course Overview and Table of Contents

2. Introduction to Machine Learning

3. System and Environment Preparation

4. Learn Basics of Python

5. Learn Basics of NumPy

6. Learn Basics of Matplotlib

7. Learn Basics of Pandas

8. Understanding the CSV Data File

9. Load and Read CSV Data File

10. Dataset Summary

11. Dataset Visualization

12. Data Preparation

13. Feature Selection

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

15. Algorithm Evaluation Techniques

16. Algorithm Evaluation Metrics

17. Classification Algorithm Spot Check - Logistic Regression

18. Classification Algorithm Spot Check - Linear Discriminant Analysis

19. Classification Algorithm Spot Check - K-Nearest Neighbors

20. Classification Algorithm Spot Check - Naive Bayes

21. Classification Algorithm Spot Check - CART

22. Classification Algorithm Spot Check - Support Vector Machines

23. Regression Algorithm Spot Check - Linear Regression

24. Regression Algorithm Spot Check - Ridge Regression

25. Regression Algorithm Spot Check - LASSO Linear Regression

26. Regression Algorithm Spot Check - Elastic Net Regression

27. Regression Algorithm Spot Check - K-Nearest Neighbors

28. Regression Algorithm Spot Check - CART

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

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

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

32. Pipelines: Data Preparation and Data Modelling

33. Pipelines: Feature Selection and Data Modelling

34. Performance Improvement: Ensembles - Voting

35. Performance Improvement: Ensembles - Bagging

36. Performance Improvement: Ensembles - Boosting

37. Performance Improvement: Parameter Tuning Using Grid Search

38. Performance Improvement: Parameter Tuning Using Random Search

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

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

41. Finalizing a Model - Introduction and Steps

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

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

44. Iris Dataset: Finalizing Multi-Class Dataset

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

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

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

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