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

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

by Abhilash Nelson
Released May 2019
Publisher(s): Packt Publishing
ISBN: 9781838980689

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Video 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.

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.

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 8 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.

Table of contents

  1. Chapter 1 : Course Overview and Table of Contents
    1. Course Overview and Table of Contents
  2. Chapter 2 : Introduction to Machine Learning
    1. Introduction to Machine Learning
  3. Chapter 3 : System and Environment Preparation
    1. System and Environment Preparation
  4. Chapter 4 : Learn Basics of Python
    1. Learn Basics of Python
  5. Chapter 5 : Learn Basics of NumPy
    1. Learn Basics of NumPy
  6. Chapter 6 : Learn Basics of Matplotlib
    1. Learn Basics of Matplotlib
  7. Chapter 7 : Learn Basics of Pandas
    1. Learn Basics of Pandas
  8. Chapter 8 : Understanding the CSV Data File
    1. Understanding the CSV Data File
  9. Chapter 9 : Load and Read CSV Data File
    1. Load and Read CSV Data File
  10. Chapter 10 : Dataset Summary
    1. Dataset Summary
  11. Chapter 11 : Dataset Visualization
    1. Dataset Visualization
  12. Chapter 12 : Data Preparation
    1. Data Preparation
  13. Chapter 13 : Feature Selection
    1. Feature Selection
  14. Chapter 14 : Refresher Session - the Mechanism of Re-Sampling, Training, and Testing
    1. Refresher Session - the Mechanism of Re-Sampling, Training, and Testing
  15. Chapter 15 : Algorithm Evaluation Techniques
    1. Algorithm Evaluation Techniques
  16. Chapter 16 : Algorithm Evaluation Metrics
    1. Algorithm Evaluation Techniques
  17. Chapter 17 : Classification Algorithm Spot Check - Logistic Regression
    1. Classification Algorithm Spot Check - Logistic Regression
  18. Chapter 18 : Classification Algorithm Spot Check - Linear Discriminant Analysis
    1. Classification Algorithm Spot Check - Linear Discriminant Analysis
  19. Chapter 19 : Classification Algorithm Spot Check - K-Nearest Neighbors
    1. Classification Algorithm Spot Check - K-Nearest Neighbors
  20. Chapter 20 : Classification Algorithm Spot Check - Naive Bayes
    1. Classification Algorithm Spot Check - Naive Bayes
  21. Chapter 21 : Classification Algorithm Spot Check – CART
    1. Classification Algorithm Spot Check – CART
  22. Chapter 22 : Classification Algorithm Spot Check - Support Vector Machines
    1. Classification Algorithm Spot Check - Support Vector Machines
  23. Chapter 23 : Regression Algorithm Spot Check - Linear Regression
    1. Regression Algorithm Spot Check - Linear Regression
  24. Chapter 24 : Regression Algorithm Spot Check - Ridge Regression
    1. Regression Algorithm Spot Check - Ridge Regression
  25. Chapter 25 : Regression Algorithm Spot Check - LASSO Linear Regression
    1. Regression Algorithm Spot Check - LASSO Linear Regression
  26. Chapter 26 : Regression Algorithm Spot Check - Elastic Net Regression
    1. Regression Algorithm Spot Check - Elastic Net Regression
  27. Chapter 27 : Regression Algorithm Spot Check - K-Nearest Neighbors
    1. Regression Algorithm Spot Check - K-Nearest Neighbors
  28. Chapter 28 : Regression Algorithm Spot Check – CART
    1. Regression Algorithm Spot Check – CART
  29. Chapter 29 : Regression Algorithm Spot Check - Support Vector Machines (SVM)
    1. Regression Algorithm Spot Check - Support Vector Machines
  30. Chapter 30 : Compare Algorithms - Part 1: Choosing the Best Machine Learning Model
    1. Compare Algorithms - Part 1: Choosing the Best Machine Learning Model
  31. Chapter 31 : Compare Algorithms - Part 2: Choosing the Best Machine Learning Model
    1. Compare Algorithms - Part 2: Choosing the Best Machine Learning Model
  32. Chapter 32 : Pipelines: Data Preparation and Data Modelling
    1. Pipelines: Data Preparation and Data Modelling
  33. Chapter 33 : Pipelines: Feature Selection and Data Modelling
    1. Pipelines: Feature Selection and Data Modelling
  34. Chapter 34 : Performance Improvement: Ensembles – Voting
    1. Performance Improvement: Ensembles – Voting
  35. Chapter 35 : Performance Improvement: Ensembles – Bagging
    1. Performance Improvement: Ensembles – Bagging
  36. Chapter 36 : Performance Improvement: Ensembles – Boosting
    1. Performance Improvement: Ensembles – Boosting
  37. Chapter 37 : Performance Improvement: Parameter Tuning Using Grid Search
    1. Performance Improvement: Parameter Tuning Using Grid Search
  38. Chapter 38 : Performance Improvement: Parameter Tuning Using Random Search
    1. Performance Improvement: Parameter Tuning Using Random Search
  39. Chapter 39 : Export, Save and Load Machine Learning Models: Pickle
    1. Export, Save and Load Machine Learning Models: Pickle
  40. Chapter 40 : Export, Save and Load Machine Learning Models: Joblib
    1. Export, Save and Load Machine Learning Models Joblib
  41. Chapter 41 : Finalizing a Model - Introduction and Steps
    1. Finalizing a Model - Introduction and Steps
  42. Chapter 42 : Finalizing a Classification Model - the Pima Indian Diabetes Dataset
    1. Finalizing a Classification Model - the Pima Indian Diabetes Dataset
  43. Chapter 43 : Quick Session: Imbalanced Dataset - Issue Overview and Steps
    1. Quick Session: Imbalanced Dataset - Issue Overview and Steps
  44. Chapter 44 : Iris Dataset: Finalizing Multi-Class Dataset
    1. Iris Dataset: Finalizing Multi-Class Dataset
  45. Chapter 45 : Finalizing a Regression Model - the Boston Housing Price Dataset
    1. Finalizing a Regression Model - the Boston Housing Price Dataset
  46. Chapter 46 : Real-Time Predictions: Using the Pima Indian Diabetes Classification Model
    1. Real-Time Predictions: Using the Pima Indian Diabetes Classification Model
  47. Chapter 47 : Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset
    1. Real-Time Predictions: Using Iris Flowers Multi-Class Classification Dataset
  48. Chapter 48 : Real-Time Predictions: Using the Boston Housing Regression Model
    1. Real-Time Predictions: Using the Boston Housing Regression Model

Product information

  • Title: Machine Learning and Data Science with Python: A Complete Beginners Guide
  • Author(s): Abhilash Nelson
  • Release date: May 2019
  • Publisher(s): Packt Publishing
  • ISBN: 9781838980689