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Course Curriculum

Course Overview & Table of Contents
Course Overview & Table of Contents 00:09:00
Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types
Introduction to Machine Learning – Part 1 – Concepts , Definitions and Types 00:05:00
Introduction to Machine Learning - Part 2 - Classifications and Applications
Introduction to Machine Learning – Part 2 – Classifications and Applications 00:06:00
System and Environment preparation - Part 1
System and Environment preparation – Part 1 00:04:00
System and Environment preparation - Part 2
System and Environment preparation – Part 2 00:06:00
Learn Basics of python - Assignment
Learn Basics of python – Assignment 1 00:10:00
Learn Basics of python - Assignment
Learn Basics of python – Assignment 2 00:09:00
Learn Basics of python - Functions
Learn Basics of python – Functions 00:04:00
Learn Basics of python - Data Structures
Learn Basics of python – Data Structures 00:12:00
Learn Basics of NumPy - NumPy Array
Learn Basics of NumPy – NumPy Array 00:06:00
Learn Basics of NumPy - NumPy Data
Learn Basics of NumPy – NumPy Data 00:08:00
Learn Basics of NumPy - NumPy Arithmetic
Learn Basics of NumPy – NumPy Arithmetic 00:04:00
Learn Basics of Matplotlib
Learn Basics of Matplotlib 00:07:00
Learn Basics of Pandas - Part 1
Learn Basics of Pandas – Part 1 00:06:00
Learn Basics of Pandas - Part 2
Learn Basics of Pandas – Part 2 00:07:00
Understanding the CSV data file
Understanding the CSV data file 00:09:00
Load and Read CSV data file using Python Standard Library
Load and Read CSV data file using Python Standard Library 00:09:00
Load and Read CSV data file using NumPy
Load and Read CSV data file using NumPy 00:04:00
Load and Read CSV data file using Pandas
Load and Read CSV data file using Pandas 00:05:00
Dataset Summary - Peek, Dimensions and Data Types
Dataset Summary – Peek, Dimensions and Data Types 00:09:00
Dataset Summary - Class Distribution and Data Summary
Dataset Summary – Class Distribution and Data Summary 00:09:00
Dataset Summary - Explaining Correlation
Dataset Summary – Explaining Correlation 00:11:00
Dataset Summary - Explaining Skewness - Gaussian and Normal Curve
Dataset Summary – Explaining Skewness – Gaussian and Normal Curve 00:07:00
Dataset Visualization - Using Histograms
Dataset Visualization – Using Histograms 00:07:00
Dataset Visualization - Using Density Plots
Dataset Visualization – Using Density Plots 00:06:00
Dataset Visualization - Box and Whisker Plots
Dataset Visualization – Box and Whisker Plots 00:05:00
Multivariate Dataset Visualization - Correlation Plots
Multivariate Dataset Visualization – Correlation Plots 00:08:00
Multivariate Dataset Visualization - Scatter Plots
Multivariate Dataset Visualization – Scatter Plots 00:05:00
Data Preparation (Pre-Processing) - Introduction
Data Preparation (Pre-Processing) – Introduction 00:09:00
Data Preparation - Re-scaling Data - Part 1
Data Preparation – Re-scaling Data – Part 1 00:09:00
Data Preparation - Re-scaling Data - Part 2
Data Preparation – Re-scaling Data – Part 2 00:09:00
Data Preparation - Standardizing Data - Part 1
Data Preparation – Standardizing Data – Part 1 00:07:00
Data Preparation - Standardizing Data - Part 2
Data Preparation – Standardizing Data – Part 2 00:04:00
Data Preparation - Normalizing Data
Data Preparation – Normalizing Data 00:08:00
Data Preparation - Binarizing Data
Data Preparation – Binarizing Data 00:06:00
Feature Selection - Introduction
Feature Selection – Introduction 00:07:00
Feature Selection - Uni-variate Part 1 - Chi-Squared Test
Feature Selection – Uni-variate Part 1 – Chi-Squared Test 00:09:00
Feature Selection - Uni-variate Part 2 - Chi-Squared Test
Feature Selection – Uni-variate Part 2 – Chi-Squared Test 00:10:00
Feature Selection - Recursive Feature Elimination
Feature Selection – Recursive Feature Elimination 00:11:00
Feature Selection - Principal Component Analysis (PCA)
Feature Selection – Principal Component Analysis (PCA) 00:09:00
Feature Selection - Feature Importance
Feature Selection – Feature Importance 00:06:00
Refresher Session - The Mechanism of Re-sampling, Training and Testing
Refresher Session – The Mechanism of Re-sampling, Training and Testing 00:12:00
Algorithm Evaluation Techniques - Introduction
Algorithm Evaluation Techniques – Introduction 00:07:00
Algorithm Evaluation Techniques - Train and Test Set
Algorithm Evaluation Techniques – Train and Test Set 00:11:00
Algorithm Evaluation Techniques - K-Fold Cross Validation
Algorithm Evaluation Techniques – K-Fold Cross Validation 00:09:00
Algorithm Evaluation Techniques - Leave One Out Cross Validation
Algorithm Evaluation Techniques – Leave One Out Cross Validation 00:05:00
Algorithm Evaluation Techniques - Repeated Random Test-Train Splits
Algorithm Evaluation Techniques – Repeated Random Test-Train Splits 00:07:00
Algorithm Evaluation Metrics - Introduction
Algorithm Evaluation Metrics – Introduction 00:09:00
Algorithm Evaluation Metrics - Classification Accuracy
Algorithm Evaluation Metrics – Classification Accuracy 00:08:00
Algorithm Evaluation Metrics - Log Loss
Algorithm Evaluation Metrics – Log Loss 00:03:00
Algorithm Evaluation Metrics - Area Under ROC Curve
Algorithm Evaluation Metrics – Area Under ROC Curve 00:06:00
Algorithm Evaluation Metrics - Confusion Matrix
Algorithm Evaluation Metrics – Confusion Matrix 00:10:00
Algorithm Evaluation Metrics - Classification Report
Algorithm Evaluation Metrics – Classification Report 00:04:00
Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction
Algorithm Evaluation Metrics – Mean Absolute Error – Dataset Introduction 00:06:00
Algorithm Evaluation Metrics - Mean Absolute Error
Algorithm Evaluation Metrics – Mean Absolute Error 00:07:00
Algorithm Evaluation Metrics - Mean Square Error
Algorithm Evaluation Metrics – Mean Square Error 00:03:00
Algorithm Evaluation Metrics - R Squared
Algorithm Evaluation Metrics – R Squared 00:04:00
Classification Algorithm Spot Check - Logistic Regression
Classification Algorithm Spot Check – Logistic Regression 00:12:00
Classification Algorithm Spot Check - Linear Discriminant Analysis
Classification Algorithm Spot Check – Linear Discriminant Analysis 00:04:00
Classification Algorithm Spot Check - K-Nearest Neighbors
Classification Algorithm Spot Check – K-Nearest Neighbors 00:05:00
Classification Algorithm Spot Check - Naive Bayes
Classification Algorithm Spot Check – Naive Bayes 00:04:00
Classification Algorithm Spot Check - CART
Classification Algorithm Spot Check – CART 00:04:00
Classification Algorithm Spot Check - Support Vector Machines
Classification Algorithm Spot Check – Support Vector Machines 00:05:00
Regression Algorithm Spot Check - Linear Regression
Regression Algorithm Spot Check – Linear Regression 00:08:00
Regression Algorithm Spot Check - Ridge Regression
Regression Algorithm Spot Check – Ridge Regression 00:03:00
Regression Algorithm Spot Check - Lasso Linear Regression
Regression Algorithm Spot Check – Lasso Linear Regression 00:03:00
Regression Algorithm Spot Check - Elastic Net Regression
Regression Algorithm Spot Check – Elastic Net Regression 00:02:00
Regression Algorithm Spot Check - K-Nearest Neighbors
Regression Algorithm Spot Check – K-Nearest Neighbors 00:06:00
Regression Algorithm Spot Check - CART
Regression Algorithm Spot Check – CART 00:04:00
Regression Algorithm Spot Check - Support Vector Machines (SVM)
Regression Algorithm Spot Check – Support Vector Machines (SVM) 00:04:00
Compare Algorithms - Part 1 : Choosing the best Machine Learning Model
Compare Algorithms – Part 1 : Choosing the best Machine Learning Model 00:09:00
Compare Algorithms - Part 2 : Choosing the best Machine Learning Model
Compare Algorithms – Part 2 : Choosing the best Machine Learning Model 00:05:00
Pipelines : Data Preparation and Data Modelling
Pipelines : Data Preparation and Data Modelling 00:11:00
Pipelines : Feature Selection and Data Modelling
Pipelines : Feature Selection and Data Modelling 00:10:00
Performance Improvement: Ensembles - Voting
Performance Improvement: Ensembles – Voting 00:07:00
Performance Improvement: Ensembles - Bagging
Performance Improvement: Ensembles – Bagging 00:08:00
Performance Improvement: Ensembles - Boosting
Performance Improvement: Ensembles – Boosting 00:05:00
Performance Improvement: Parameter Tuning using Grid Search
Performance Improvement: Parameter Tuning using Grid Search 00:08:00
Performance Improvement: Parameter Tuning using Random Search
Performance Improvement: Parameter Tuning using Random Search 00:06:00
Export, Save and Load Machine Learning Models : Pickle
Export, Save and Load Machine Learning Models : Pickle 00:10:00
Export, Save and Load Machine Learning Models : Joblib
Export, Save and Load Machine Learning Models : Joblib 00:06:00
Finalizing a Model - Introduction and Steps
Finalizing a Model – Introduction and Steps 00:07:00
Finalizing a Classification Model - The Pima Indian Diabetes Dataset
Finalizing a Classification Model – The Pima Indian Diabetes Dataset 00:07:00
Quick Session: Imbalanced Data Set - Issue Overview and Steps
Quick Session: Imbalanced Data Set – Issue Overview and Steps 00:09:00
Iris Dataset : Finalizing Multi-Class Dataset
Iris Dataset : Finalizing Multi-Class Dataset 00:09:00
Finalizing a Regression Model - The Boston Housing Price Dataset
Finalizing a Regression Model – The Boston Housing Price Dataset 00:08:00
Real-time Predictions: Using the Pima Indian Diabetes Classification Model
Real-time Predictions: Using the Pima Indian Diabetes Classification Model 00:07:00
Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset
Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset 00:03:00
Real-time Predictions: Using the Boston Housing Regression Model
Real-time Predictions: Using the Boston Housing Regression Model 00:08:00
Resources
Resources – Data Science & Machine Learning with Python 00:00:00

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