Skip to Content on-demand course Machine Learning and Data Science with Python: A Complete Beginners Guide May 2019
Beginner to intermediate
10h 19m
English
Closed Captioning available in English Course outline Chapter 1 : Course Overview and Table of Contents 8m
Chapter 2 : Introduction to Machine Learning 10m
Chapter 3 : System and Environment Preparation 14m
Chapter 4 : Learn Basics of Python 35m
Chapter 5 : Learn Basics of NumPy 18m
Chapter 6 : Learn Basics of Matplotlib 7m
Chapter 7 : Learn Basics of Pandas 12m
Chapter 8 : Understanding the CSV Data File 8m
Chapter 9 : Load and Read CSV Data File 18m
Chapter 10 : Dataset Summary 35m
Chapter 11 : Dataset Visualization 30m
Chapter 12 : Data Preparation 51m
Chapter 13 : Feature Selection 52m
Chapter 14 : Refresher Session - the Mechanism of Re-Sampling, Training, and Testing 12m
Chapter 15 : Algorithm Evaluation Techniques 38m
Chapter 16 : Algorithm Evaluation Metrics 1h
Chapter 17 : Classification Algorithm Spot Check - Logistic Regression 11m
Chapter 18 : Classification Algorithm Spot Check - Linear Discriminant Analysis 3m
Chapter 19 : Classification Algorithm Spot Check - K-Nearest Neighbors 4m
Chapter 20 : Classification Algorithm Spot Check - Naive Bayes 4m
Chapter 21 : Classification Algorithm Spot Check – CART 3m
Chapter 22 : Classification Algorithm Spot Check - Support Vector Machines 4m
Chapter 23 : Regression Algorithm Spot Check - Linear Regression 7m
Chapter 24 : Regression Algorithm Spot Check - Ridge Regression 3m
Chapter 25 : Regression Algorithm Spot Check - LASSO Linear Regression 2m
Chapter 26 : Regression Algorithm Spot Check - Elastic Net Regression 2m
Chapter 27 : Regression Algorithm Spot Check - K-Nearest Neighbors 5m
Chapter 28 : Regression Algorithm Spot Check – CART 4m
Chapter 29 : Regression Algorithm Spot Check - Support Vector Machines (SVM) 4m
Chapter 30 : Compare Algorithms - Part 1: Choosing the Best Machine Learning Model 8m
Chapter 31 : Compare Algorithms - Part 2: Choosing the Best Machine Learning Model 5m
Chapter 32 : Pipelines: Data Preparation and Data Modelling 10m
Chapter 33 : Pipelines: Feature Selection and Data Modelling 9m
Chapter 34 : Performance Improvement: Ensembles – Voting 6m
Chapter 35 : Performance Improvement: Ensembles – Bagging 8m
Chapter 36 : Performance Improvement: Ensembles – Boosting 4m
Chapter 37 : Performance Improvement: Parameter Tuning Using Grid Search 7m
Chapter 38 : Performance Improvement: Parameter Tuning Using Random Search 6m
Chapter 39 : Export, Save and Load Machine Learning Models: Pickle 9m
Chapter 40 : Export, Save and Load Machine Learning Models: Joblib 5m
Chapter 41 : Finalizing a Model - Introduction and Steps 6m
Chapter 42 : Finalizing a Classification Model - the Pima Indian Diabetes Dataset 6m
Chapter 43 : Quick Session: Imbalanced Dataset - Issue Overview and Steps 8m
Chapter 44 : Iris Dataset: Finalizing Multi-Class Dataset 9m
Chapter 45 : Finalizing a Regression Model - the Boston Housing Price Dataset 8m
Chapter 46 : Real-Time Predictions: Using the Pima Indian Diabetes Classification Model 6m
Chapter 47 : Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset 3m
Chapter 48 : Real-Time Predictions: Using the Boston Housing Regression Model 7m
Show More Quick Session: Imbalanced Dataset - Issue Overview and Steps
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