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