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

Video Description

Machine learning and data science for programming beginners using Python with scikit-learn, SciPy, Matplotlib and Pandas

About This Video

  • Learn machine learning and data science using Python
  • A practical course designed for beginners who are interested in machine learning using Python

In Detail

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 which 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.

Downloading the example code for this course: You can download the example code files for this course on GitHub at the following link: https://github.com/PacktPublishing/Machine-Learning-and-Data-Science-with-Python-A-Complete-Beginners-Guide. If you require support please email: customercare@packt.com

Table of Contents

  1. Chapter 1 : Course Overview & Table of Contents
    1. Course Overview & Table of Contents 00:08:59
    2. Code Password 00:00:05
  2. Chapter 2 : Introduction to Machine Learning
    1. Introduction to Machine Learning 00:10:32
  3. Chapter 3 : System and Environment preparation
    1. System and Environment preparation 00:14:05
  4. Chapter 4 : Learn Basics of python
    1. Learn Basics of python 00:35:18
  5. Chapter 5 : Learn Basics of NumPy
    1. Learn Basics of NumPy 00:18:18
  6. Chapter 6 : Learn Basics of Matplotlib
    1. Learn Basics of Matplotlib 00:07:06
  7. Chapter 7 : Learn Basics of Pandas
    1. Learn Basics of Pandas 00:12:48
  8. Chapter 8 : Understanding the CSV data file
    1. Understanding the CSV data file 00:08:56
  9. Chapter 9 : Load and Read CSV data file
    1. Load and Read CSV data file 00:18:09
  10. Chapter 10 : Dataset Summary
    1. Dataset Summary 00:35:46
  11. Chapter 11 : Dataset Visualization
    1. Dataset Visualization 00:30:43
  12. Chapter 12 : Data Preparation
    1. Data Preparation 00:51:30
  13. Chapter 13 : Feature Selection
    1. Feature Selection 00:52:10
  14. Chapter 14 : Refresher Session - The Mechanism of Re-sampling, Training and Testing
    1. Refresher Session - The Mechanism of Re-sampling, Training and Testing 00:12:04
  15. Chapter 15 : Algorithm Evaluation Techniques
    1. Algorithm Evaluation Techniques 00:38:27
  16. Chapter 16 : Algorithm Evaluation Metrics
    1. Algorithm Evaluation Techniques 1:00:39
  17. Chapter 17 : Classification Algorithm Spot Check - Logistic Regression
    1. Classification Algorithm Spot Check - Logistic Regression 00:11:32
  18. Chapter 18 : Classification Algorithm Spot Check - Linear Discriminant Analysis
    1. Classification Algorithm Spot Check - Linear Discriminant Analysis 00:03:49
  19. Chapter 19 : Classification Algorithm Spot Check - K-Nearest Neighbors
    1. Classification Algorithm Spot Check - K-Nearest Neighbors 00:04:50
  20. Chapter 20 : Classification Algorithm Spot Check - Naive Bayes
    1. Classification Algorithm Spot Check - Naive Bayes 00:04:01
  21. Chapter 21 : Classification Algorithm Spot Check – CART
    1. Classification Algorithm Spot Check – CART 00:03:49
  22. Chapter 22 : Classification Algorithm Spot Check - Support Vector Machines
    1. Classification Algorithm Spot Check - Support Vector Machines 00:04:37
  23. Chapter 23 : Regression Algorithm Spot Check - Linear Regression
    1. Regression Algorithm Spot Check - Linear Regression 00:07:38
  24. Chapter 24 : Regression Algorithm Spot Check - Ridge Regression
    1. Regression Algorithm Spot Check - Ridge Regression 00:03:14
  25. Chapter 25 : Regression Algorithm Spot Check - LASSO Linear Regression
    1. Regression Algorithm Spot Check - LASSO Linear Regression 00:02:55
  26. Chapter 26 : Regression Algorithm Spot Check - Elastic Net Regression
    1. Regression Algorithm Spot Check - Elastic Net Regression 00:02:10
  27. Chapter 27 : Regression Algorithm Spot Check - K-Nearest Neighbors
    1. Regression Algorithm Spot Check - K-Nearest Neighbors 00:05:57
  28. Chapter 28 : Regression Algorithm Spot Check – CART
    1. Regression Algorithm Spot Check – CART 00:04:03
  29. Chapter 29 : Regression Algorithm Spot Check - Support Vector Machines (SVM)
    1. Regression Algorithm Spot Check - Support Vector Machines (SVM) 00:04:03
  30. Chapter 30 : Compare Algorithms - Part 1: Choosing the best Machine Learning Model
    1. Compare Algorithms - Part 1: Choosing the best Machine Learning Model 00:08:52
  31. Chapter 31 : Compare Algorithms - Part 2: Choosing the best Machine Learning Model
    1. Compare Algorithms - Part 2: Choosing the best Machine Learning Model 00:05:02
  32. Chapter 32 : Pipelines: Data Preparation and Data Modelling
    1. Pipelines: Data Preparation and Data Modelling 00:10:56
  33. Chapter 33 : Pipelines: Feature Selection and Data Modelling
    1. Pipelines: Feature Selection and Data Modelling 00:09:36
  34. Chapter 34 : Performance Improvement: Ensembles – Voting
    1. Performance Improvement: Ensembles – Voting 00:06:58
  35. Chapter 35 : Performance Improvement: Ensembles – Bagging
    1. Performance Improvement: Ensembles – Bagging 00:08:21
  36. Chapter 36 : Performance Improvement: Ensembles – Boosting
    1. Performance Improvement: Ensembles – Boosting 00:04:36
  37. Chapter 37 : Performance Improvement: Parameter Tuning using Grid Search
    1. Performance Improvement: Parameter Tuning using Grid Search 00:07:36
  38. Chapter 38 : Performance Improvement: Parameter Tuning using Random Search
    1. Performance Improvement: Parameter Tuning using Random Search 00:06:00
  39. Chapter 39 : Export, Save and Load Machine Learning Models: Pickle
    1. Export, Save and Load Machine Learning Models: Pickle 00:09:41
  40. Chapter 40 : Export, Save and Load Machine Learning Models: Joblib
    1. Export, Save and Load Machine Learning Models Joblib 00:05:53
  41. Chapter 41 : Finalizing a Model - Introduction and Steps
    1. Finalizing a Model - Introduction and Steps 00:06:39
  42. Chapter 42 : Finalizing a Classification Model - The Pima Indian Diabetes Dataset
    1. Finalizing a Classification Model - The Pima Indian Diabetes Dataset 00:06:46
  43. Chapter 43 : Quick Session: Imbalanced Data Set - Issue Overview and Steps
    1. Quick Session: Imbalanced Data Set - Issue Overview and Steps 00:08:35
  44. Chapter 44 : Iris Dataset: Finalizing Multi-Class Dataset
    1. Iris Dataset: Finalizing Multi-Class Dataset 00:09:16
  45. Chapter 45 : Finalizing a Regression Model - The Boston Housing Price Dataset
    1. Finalizing a Regression Model - The Boston Housing Price Dataset 00:08:17
  46. Chapter 46 : Real-time Predictions: Using the Pima Indian Diabetes Classification Model
    1. Real-time Predictions: Using the Pima Indian Diabetes Classification Model 00:06:39
  47. Chapter 47 : Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset
    1. Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset 00:03:26
  48. Chapter 48 : Real-time Predictions: Using the Boston Housing Regression Model
    1. Real-time Predictions: Using the Boston Housing Regression Model 00:07:45