Course Overview & Table of Contents 

Course Overview & Table of Contents 

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Introduction to Machine Learning  Part 1  Concepts , Definitions and Types 

Introduction to Machine Learning – Part 1 – Concepts , Definitions and Types 

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Introduction to Machine Learning  Part 2  Classifications and Applications 

Introduction to Machine Learning – Part 2 – Classifications and Applications 

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System and Environment preparation  Part 1 

System and Environment preparation – Part 1 

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ystem and Environment preparation  Part 2 

System and Environment preparation – Part 2 

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Learn Basics of python  Assignment 

Learn Basics of python – Assignment 1 

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Learn Basics of python  Assignment 

Learn Basics of python – Assignment 2 

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Learn Basics of python  Functions 

Learn Basics of python – Functions 

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Learn Basics of python  Data Structures 

Learn Basics of python – Data Structures 

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Learn Basics of NumPy  NumPy Array 

Learn Basics of NumPy – NumPy Array 

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Learn Basics of NumPy  NumPy Data 

Learn Basics of NumPy – NumPy Data 

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earn Basics of NumPy  NumPy Arithmetic 

Learn Basics of NumPy – NumPy Arithmetic 

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Learn Basics of Matplotlib 

Learn Basics of Matplotlib 

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Learn Basics of Pandas  Part 1 

Learn Basics of Pandas – Part 1 

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Learn Basics of Pandas  Part 2 

Learn Basics of Pandas – Part 2 

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Understanding the CSV data file 

Understanding the CSV data file 

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Load and Read CSV data file using Python Standard Library 

Load and Read CSV data file using Python Standard Library 

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Load and Read CSV data file using NumPy 

Load and Read CSV data file using NumPy 

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Load and Read CSV data file using Pandas 

Load and Read CSV data file using Pandas 

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Dataset Summary  Peek, Dimensions and Data Types 

Dataset Summary – Peek, Dimensions and Data Types 

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Dataset Summary  Class Distribution and Data Summary 

Dataset Summary – Class Distribution and Data Summary 

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Dataset Summary  Explaining Correlation 

Dataset Summary – Explaining Correlation 

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Dataset Summary  Explaining Skewness  Gaussian and Normal Curve 

Dataset Summary – Explaining Skewness – Gaussian and Normal Curve 

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Dataset Visualization  Using Histograms 

Dataset Visualization – Using Histograms 

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Dataset Visualization  Using Density Plots 

Dataset Visualization – Using Density Plots 

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Dataset Visualization  Box and Whisker Plots 

Dataset Visualization – Box and Whisker Plots 

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Multivariate Dataset Visualization  Correlation Plots 

Multivariate Dataset Visualization – Correlation Plots 

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Multivariate Dataset Visualization  Scatter Plots 

Multivariate Dataset Visualization – Scatter Plots 

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Data Preparation (PreProcessing)  Introduction 

Data Preparation (PreProcessing) – Introduction 

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Data Preparation  Rescaling Data  Part 1 

Data Preparation – Rescaling Data – Part 1 

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Data Preparation  Rescaling Data  Part 2 

Data Preparation – Rescaling Data – Part 2 

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Data Preparation  Standardizing Data  Part 1 

Data Preparation – Standardizing Data – Part 1 

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Data Preparation  Standardizing Data  Part 2 

Data Preparation – Standardizing Data – Part 2 

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Data Preparation  Normalizing Data 

Data Preparation – Normalizing Data 

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Data Preparation  Binarizing Data 

Data Preparation – Binarizing Data 

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Feature Selection  Introduction 

Feature Selection – Introduction 

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Feature Selection  Univariate Part 1  ChiSquared Test 

Feature Selection – Univariate Part 1 – ChiSquared Test 

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Feature Selection  Univariate Part 2  ChiSquared Test 

Feature Selection – Univariate Part 2 – ChiSquared Test 

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Feature Selection  Recursive Feature Elimination 

Feature Selection – Recursive Feature Elimination 

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Feature Selection  Principal Component Analysis (PCA) 

Feature Selection – Principal Component Analysis (PCA) 

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Feature Selection  Feature Importance 

Feature Selection – Feature Importance 

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Refresher Session  The Mechanism of Resampling, Training and Testing 

Refresher Session – The Mechanism of Resampling, Training and Testing 

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Algorithm Evaluation Techniques  Introduction 

Algorithm Evaluation Techniques – Introduction 

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Algorithm Evaluation Techniques  Train and Test Set 

Algorithm Evaluation Techniques – Train and Test Set 

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Algorithm Evaluation Techniques  KFold Cross Validation 

Algorithm Evaluation Techniques – KFold Cross Validation 

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Algorithm Evaluation Techniques  Leave One Out Cross Validation 

Algorithm Evaluation Techniques – Leave One Out Cross Validation 

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Algorithm Evaluation Techniques  Repeated Random TestTrain Splits 

Algorithm Evaluation Techniques – Repeated Random TestTrain Splits 

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Algorithm Evaluation Metrics  Introduction 

Algorithm Evaluation Metrics – Introduction 

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Algorithm Evaluation Metrics  Classification Accuracy 

Algorithm Evaluation Metrics – Classification Accuracy 

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Algorithm Evaluation Metrics  Log Loss 

Algorithm Evaluation Metrics – Log Loss 

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Algorithm Evaluation Metrics  Area Under ROC Curve 

Algorithm Evaluation Metrics – Area Under ROC Curve 

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Algorithm Evaluation Metrics  Confusion Matrix 

Algorithm Evaluation Metrics – Confusion Matrix 

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Algorithm Evaluation Metrics  Classification Report 

Algorithm Evaluation Metrics – Classification Report 

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Algorithm Evaluation Metrics  Mean Absolute Error  Dataset Introduction 

Algorithm Evaluation Metrics – Mean Absolute Error – Dataset Introduction 

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Algorithm Evaluation Metrics  Mean Absolute Error 

Algorithm Evaluation Metrics – Mean Absolute Error 

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Algorithm Evaluation Metrics  Mean Square Error 

Algorithm Evaluation Metrics – Mean Square Error 

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Algorithm Evaluation Metrics  R Squared 

Algorithm Evaluation Metrics – R Squared 

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Classification Algorithm Spot Check  Logistic Regression 

Classification Algorithm Spot Check – Logistic Regression 

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Classification Algorithm Spot Check  Linear Discriminant Analysis 

Classification Algorithm Spot Check – Linear Discriminant Analysis 

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Classification Algorithm Spot Check  KNearest Neighbors 

Classification Algorithm Spot Check – KNearest Neighbors 

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Classification Algorithm Spot Check  Naive Bayes 

Classification Algorithm Spot Check – Naive Bayes 

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Classification Algorithm Spot Check  CART 

Classification Algorithm Spot Check – CART 

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Classification Algorithm Spot Check  Support Vector Machines 

Classification Algorithm Spot Check – Support Vector Machines 

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Regression Algorithm Spot Check  Linear Regression 

Regression Algorithm Spot Check – Linear Regression 

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Regression Algorithm Spot Check  Ridge Regression 

Regression Algorithm Spot Check – Ridge Regression 

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Regression Algorithm Spot Check  Lasso Linear Regression 

Regression Algorithm Spot Check – Lasso Linear Regression 

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Regression Algorithm Spot Check  Elastic Net Regression 

Regression Algorithm Spot Check – Elastic Net Regression 

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Regression Algorithm Spot Check  KNearest Neighbors 

Regression Algorithm Spot Check – KNearest Neighbors 

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Regression Algorithm Spot Check  CART 

Regression Algorithm Spot Check – CART 

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Regression Algorithm Spot Check  Support Vector Machines (SVM) 

Regression Algorithm Spot Check – Support Vector Machines (SVM) 

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Compare Algorithms  Part 1 : Choosing the best Machine Learning Model 

Compare Algorithms – Part 1 : Choosing the best Machine Learning Model 

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Compare Algorithms  Part 2 : Choosing the best Machine Learning Model 

Compare Algorithms – Part 2 : Choosing the best Machine Learning Model 

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Pipelines : Data Preparation and Data Modelling 

Pipelines : Data Preparation and Data Modelling 

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Pipelines : Feature Selection and Data Modelling 

Pipelines : Feature Selection and Data Modelling 

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Performance Improvement: Ensembles  Voting 

Performance Improvement: Ensembles – Voting 

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Performance Improvement: Ensembles  Bagging 

Performance Improvement: Ensembles – Bagging 

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Performance Improvement: Ensembles  Boosting 

Performance Improvement: Ensembles – Boosting 

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Performance Improvement: Parameter Tuning using Grid Search 

Performance Improvement: Parameter Tuning using Grid Search 

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Performance Improvement: Parameter Tuning using Random Search 

Performance Improvement: Parameter Tuning using Random Search 

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Export, Save and Load Machine Learning Models : Pickle 

Export, Save and Load Machine Learning Models : Pickle 

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Export, Save and Load Machine Learning Models : Joblib 

Export, Save and Load Machine Learning Models : Joblib 

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Finalizing a Model  Introduction and Steps 

Finalizing a Model – Introduction and Steps 

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Finalizing a Classification Model  The Pima Indian Diabetes Dataset 

Finalizing a Classification Model – The Pima Indian Diabetes Dataset 

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Quick Session: Imbalanced Data Set  Issue Overview and Steps 

Quick Session: Imbalanced Data Set – Issue Overview and Steps 

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Iris Dataset : Finalizing MultiClass Dataset 

Iris Dataset : Finalizing MultiClass Dataset 

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Finalizing a Regression Model  The Boston Housing Price Dataset 

Finalizing a Regression Model – The Boston Housing Price Dataset 

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Realtime Predictions: Using the Pima Indian Diabetes Classification Model 

Realtime Predictions: Using the Pima Indian Diabetes Classification Model 

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Realtime Predictions: Using Iris Flowers MultiClass Classification Dataset 

Realtime Predictions: Using Iris Flowers MultiClass Classification Dataset 

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Realtime Predictions: Using the Boston Housing Regression Model 

Realtime Predictions: Using the Boston Housing Regression Model 

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Resources 

Resource – Data Science & Machine Learning with Python 

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Certificate and Transcript 

Order Your Certificates or Transcripts 

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