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