Data Science and Machine Learning Table of Contents

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Table of Content
 

 

Introduction to the Course

  • Introduction to Courses and Instructor

Basics for Data Science: Python for Data Science and Data Analysis

  • Introduction - Part 1
  • Introduction - Part 2
  • Basics of Programming: Understanding the Algorithm
  • Basics of Programming: FlowCharts and Pseudocodes
  • Basics of Programming: Example of Algorithms - Making Tea Problem
  • Basics of Programming: Example of Algorithms-Searching Minimum
  • Basics of Programming: Example of Algorithms-Sorting Problem
  • Basics of Programming: Sorting Problem in Python
  • Why Python and Jupyter Notebook: Why Python
  • Why Python and Jupyter Notebook: Why Jupyter Notebooks
  • Installation of Anaconda and IPython Shell: Installing Python and Jupyter Anaconda
  • Installation of Anaconda and IPython Shell: Your First Python Code - Hello World
  • Installation of Anaconda and IPython Shell: Coding in IPython Shell
  • Variable and Operator: Variables
  • Variable and Operator: Operators
  • Variable and Operator: Variable Name Quiz
  • Variable and Operator: Bool Data Type in Python
  • Variable and Operator: Comparison in Python
  • Variable and Operator: Combining Comparisons in Python
  • Variable and Operator: Combining Comparisons Quiz
  • Python Useful function: Python Function- Round
  • Python Useful function: Python Function- Divmod
  • Python Useful function: Python Function- Is instance and PowFunctions
  • Python Useful function: Python Function- Input
  • Control Flow in Python: If Python Condition
  • Control Flow in Python: if Elif Else Python Conditions
  • Control Flow in Python: More on if Elif Else Python Conditions
  • Control Flow in Python: Indentations
  • Control Flow in Python: Comments and Problem-Solving Practice with If
  • Control Flow in Python: While Loop
  • Control Flow in Python: While Loop Break Continue
  • Control Flow in Python: For Loop
  • Control Flow in Python: Else in For Loop
  • Control Flow in Python: Loops Practice-Sorting Problem
  • Function and Module in Python: Functions in Python
  • Function and Module in Python: DocString
  • Function and Module in Python: Input Arguments
  • Function and Module in Python: Multiple Input Arguments
  • Function and Module in Python: Ordering Multiple Input Arguments
  • Function and Module in Python: Output Arguments and Return Statement
  • Function and Module in Python: Function Practice-Output Arguments and Return Statement
  • Function and Module in Python: Variable Number of Input Arguments
  • Function and Module in Python: Variable Number of Input Arguments as Dictionary
  • Function and Module in Python: Default Values in Python
  • Function and Module in Python: Modules in Python
  • Function and Module in Python: Making Modules in Python
  • Function and Module in Python: Function Practice-Sorting List in Python
  • String in Python: Strings
  • String in Python: Multi-Line Strings
  • String in Python: Indexing Strings
  • String in Python: String Methods
  • String in Python: String Escape Sequences
  • Data Structure (List, Tuple, Set, Dictionary): Introduction to Data Structure
  • Data Structure (List, Tuple, Set, Dictionary): Defining and Indexing
  • Data Structure (List, Tuple, Set, Dictionary): Insertion and Deletion
  • Data Structure (List, Tuple, Set, Dictionary): Python Practice-Insertion and Deletion
  • Data Structure (List, Tuple, Set, Dictionary): Deep Copy or Reference Slicing
  • Data Structure (List, Tuple, Set, Dictionary): Exploring Methods Using TAB Completion
  • Data Structure (List, Tuple, Set, Dictionary): Data Structure Abstract Ways
  • Data Structure (List, Tuple, Set, Dictionary): Data Structure Practice
  • NumPy for Numerical Data Processing: Introduction to NumPy
  • NumPy for Numerical Data Processing: NumPy Dimensions
  • NumPy for Numerical Data Processing: NumPy Shape, Size, and Bytes
  • NumPy for Numerical Data Processing: Arrange, Random, and Reshape-Part 1
  • NumPy for Numerical Data Processing: Arrange, Random, and Reshape-Part 2
  • NumPy for Numerical Data Processing: Slicing-Part 1
  • NumPy for Numerical Data Processing: Slicing-Part 2
  • NumPy for Numerical Data Processing: NumPy Masking
  • NumPy for Numerical Data Processing: NumPy BroadCasting and Concatenation
  • NumPy for Numerical Data Processing: NumPy ufuncs Speed Test
  • Pandas for Data Manipulation: Introduction to Pandas
  • Pandas for Data Manipulation: Pandas Series
  • Pandas for Data Manipulation: Pandas Data Frame
  • Pandas for Data Manipulation: Pandas Missing Values
  • Pandas for Data Manipulation: Pandas .loc and .iloc
  • Pandas for Data Manipulation: Pandas Practice-Using COVID19 Data - Part 1
  • Pandas for Data Manipulation: Pandas Practice-Using COVID19 Data - Part 2
  • Matplotlib, Seaborn, and Bokeh for Data Visualization: Introduction to Matplotlib
  • Matplotlib, Seaborn, and Bokeh for Data Visualization: Seaborn Versus Matplotlib Style
  • Matplotlib, Seaborn, and Bokeh for Data Visualization: Histograms Kdeplot
  • Matplotlib, Seaborn, and Bokeh for Data Visualization: Seaborn Pairplot and Jointplot
  • Matplotlib, Seaborn, and Bokeh for Data Visualization: Seaborn Pairplot using Iris Data
  • Matplotlib, Seaborn, and Bokeh for Data Visualization: Introduction to Bokeh
  • Matplotlib, Seaborn, and Bokeh for Data Visualization: Bokeh Gridplot
  • Scikit-Learn for Machine Learning: Introduction to Scikit-Learn
  • Scikit-Learn for Machine Learning: Scikit-Learn for Linear Regression
  • Scikit-Learn for Machine Learning: Scikit-Learn for SVM and Random Forests
  • Scikit-Learn for Machine Learning: Scikit-Learn - Trend Analysis COVID19

Basics for Data Science: Data Understanding and Data Visualization with Python

  • Introduction
  • What We will Learn
  • NumPy for Numerical Data Processing: Ufuncs Add, Sum, and Plus Operators
  • NumPy for Numerical Data Processing: Ufuncs Subtract Power Mod
  • NumPy for Numerical Data Processing: Ufuncs Comparisons Logical Operators
  • NumPy for Numerical Data Processing: Ufuncs Comparisons Logical Operators Quiz
  • NumPy for Numerical Data Processing: Ufuncs Comparisons Logical Operators Solution
  • NumPy for Numerical Data Processing: Ufuncs Output Argument
  • NumPy for Numerical Data Processing: NumPy Playing with Images
  • NumPy for Numerical Data Processing: NumPy Playing with Images Quiz
  • NumPy for Numerical Data Processing: NumPy Playing with Images Solution
  • NumPy for Numerical Data Processing: NumPy KNN Classifier from Scratch
  • NumPy for Numerical Data Processing: NumPy Structured Arrays
  • NumPy for Numerical Data Processing: NumPy Structured Arrays Quiz
  • NumPy for Numerical Data Processing: NumPy Structured Arrays Solution
  • Pandas for Data Manipulation and Understanding: Introduction to Pandas
  • Pandas for Data Manipulation and Understanding: Pandas Series
  • Pandas for Data Manipulation and Understanding: Pandas DataFrame
  • Pandas for Data Manipulation and Understanding: Pandas DataFrame Quiz
  • Pandas for Data Manipulation and Understanding: Pandas DataFrame Solution
  • Pandas for Data Manipulation and Understanding: Pandas Missing Values
  • Pandas for Data Manipulation and Understanding: Pandas Loc Iloc
  • Pandas for Data Manipulation and Understanding: Pandas in Practice
  • Pandas for Data Manipulation and Understanding: Pandas Group By
  • Pandas for Data Manipulation and Understanding: Pandas Group By Quiz
  • Pandas for Data Manipulation and Understanding: Pandas Group by Solution
  • Pandas for Data Manipulation and Understanding: Hierarchical Indexing
  • Pandas for Data Manipulation and Understanding: Pandas Rolling
  • Pandas for Data Manipulation and Understanding: Pandas Rolling Quiz
  • Pandas for Data Manipulation and Understanding: Pandas Rolling Solution
  • Pandas for Data Manipulation and Understanding: Pandas Where
  • Pandas for Data Manipulation and Understanding: Pandas Clip
  • Pandas for Data Manipulation and Understanding: Pandas Clip Quiz
  • Pandas for Data Manipulation and Understanding: Pandas Clip Solution
  • Pandas for Data Manipulation and Understanding: Pandas Merge
  • Pandas for Data Manipulation and Understanding: Pandas Merge Quiz
  • Pandas for Data Manipulation and Understanding: Pandas Merge Solution
  • Pandas for Data Manipulation and Understanding: Pandas Pivot Table
  • Pandas for Data Manipulation and Understanding: Pandas Strings
  • Pandas for Data Manipulation and Understanding: Pandas DateTime
  • Pandas for Data Manipulation and Understanding: Pandas Hands on COVID19 Data
  • Pandas for Data Manipulation and Understanding: Pandas Hands on COVID19 Data Bug
  • Matplotlib for Data Visualization: Introduction to Matplotlib
  • Matplotlib for Data Visualization: Matplotlib Multiple Plots
  • Matplotlib for Data Visualization: Matplotlib Colors and Styles
  • Matplotlib for Data Visualization: Matplotlib Colors and Styles Quiz
  • Matplotlib for Data Visualization: Matplotlib Colors and Styles Solution
  • Matplotlib for Data Visualization: Matplotlib Colors and Styles Shortcuts
  • Matplotlib for Data Visualization: Matplotlib Axis Limits
  • Matplotlib for Data Visualization: Matplotlib Axis Limits Quiz
  • Matplotlib for Data Visualization: Matplotlib Axis Limits Solution
  • Matplotlib for Data Visualization: Matplotlib Legends Labels
  • Matplotlib for Data Visualization: Matplotlib Set Function
  • Matplotlib for Data Visualization: Matplotlib Set Function Quiz
  • Matplotlib for Data Visualization: Matplotlib Set Function Solution
  • Matplotlib for Data Visualization: Matplotlib Markers
  • Matplotlib for Data Visualization: Matplotlib Markers Randomplots
  • Matplotlib for Data Visualization: Matplotlib Scatter Plot
  • Matplotlib for Data Visualization: Matplotlib Contour Plot
  • Matplotlib for Data Visualization: Matplotlib Contour Plot Quiz
  • Matplotlib for Data Visualization: Matplotlib Contour Plot Solution
  • Matplotlib for Data Visualization: Matplotlib Histograms
  • Matplotlib for Data Visualization: Matplotlib Subplots
  • Matplotlib for Data Visualization: Matplotlib Subplots Quiz
  • Matplotlib for Data Visualization: Matplotlib Subplots Solution
  • Matplotlib for Data Visualization: Matplotlib 3D Introduction
  • Matplotlib for Data Visualization: Matplotlib 3D Scatter Plots
  • Matplotlib for Data Visualization: Matplotlib 3D Scatter Plots Quiz
  • Matplotlib for Data Visualization: Matplotlib 3D Scatter Plots Solution
  • Matplotlib for Data Visualization: Matplotlib 3D Surface Plots
  • Seaborn for Data Visualization: Introduction to Seaborn
  • Seaborn for Data Visualization: Seaborn Relplot
  • Seaborn for Data Visualization: Seaborn Relplot Quiz
  • Seaborn for Data Visualization: Seaborn Relplot Solution
  • Seaborn for Data Visualization: Seaborn Relplot Kind Line
  • Seaborn for Data Visualization: Seaborn Relplot Facets
  • Seaborn for Data Visualization: Seaborn Relplot Facets Quiz
  • Seaborn for Data Visualization: Seaborn Relplot Facets Solution
  • Seaborn for Data Visualization: Seaborn Catplot
  • Seaborn for Data Visualization: Seaborn Heatmaps
  • Bokeh for Interactive Plotting: Introduction to Bokeh
  • Bokeh for Interactive Plotting: Bokeh Multiplots Markers
  • Bokeh for Interactive Plotting: Bokeh Multiplots Grid Plot
  • Bokeh for Interactive Plotting: Bokeh Multiplots Grid Plot Quiz
  • Bokeh for Interactive Plotting: Bokeh Multiplots Grid Plot Solution
  • Plotly for 3D Interactive Plotting: Plotly 3D Interactive Scatter Plot
  • Plotly for 3D Interactive Plotting: Plotly 3D Interactive Scatter Plot Quiz
  • Plotly for 3D Interactive Plotting: Plotly 3D Interactive Scatter Plot Solution
  • Plotly for 3D Interactive Plotting: Plotly 3D Interactive Surface Plot
  • Plotly for 3D Interactive Plotting: Plotly 3D Interactive Surface Plot Quiz
  • Plotly for 3D Interactive Plotting: Plotly 3D Interactive Surface Plot Solution
  • Geographic Maps with Folium: Geographic Maps with Folium using COVID-19 Data
  • Geographic Maps with Folium: Geographic Maps with Folium using COVID-19 Data Quiz
  • Geographic Maps with Folium: Geographic Maps with Folium using COVID-19 Data Solution
  • Pandas for Plotting: Pandas for Plotting

Basics for Data Science: Mastering Probability and Statistics in Python

  • Introduction
  • Probability Versus Statistics
  • Sets: Definition of Set
  • Sets: Definition of Set Exercise 01
  • Sets: Definition of Set Solution 01
  • Sets: Definition of Set Exercise 02
  • Sets: Definition of Set Solution 02
  • Sets: Cardinality of a Set
  • Sets: Subsets PowerSet UniversalSet
  • Sets: Python Practice Subsets
  • Sets: PowerSets Solution
  • Sets: Operations
  • Sets: Operations Exercise 01
  • Sets: Operations Solution 01
  • Sets: Operations Exercise 02
  • Sets: Operations Solution 02
  • Sets: Operations Exercise 03
  • Sets: Operations Solution 03
  • Sets: Python Practice Operations
  • Sets: Venn Diagrams Operations
  • Sets: Homework
  • Experiment: Random Experiment
  • Experiment: Outcome and Sample Space
  • Experiment: Outcome and Sample Space Exercise 01
  • Experiment: Outcome and Sample Space Solution 01
  • Experiment: Event
  • Experiment: Event Exercise 01
  • Experiment: Event Solution 01
  • Experiment: Event Exercise 02
  • Experiment: Event Solution 02
  • Experiment: Recap and Homework
  • Probability Model: Probability Model
  • Probability Model: Probability Axioms
  • Probability Model: Probability Axioms Derivations
  • Probability Model: Probability Axioms Derivations Exercise 01
  • Probability Model: Probability Axioms Derivations Solution 01
  • Probability Model: Probability Models Example
  • Probability Model: Probability Models More Examples
  • Probability Model: Probability Models Continuous
  • Probability Model: Conditional Probability
  • Probability Model: Conditional Probability Example
  • Probability Model: Conditional Probability Formula
  • Probability Model: Conditional Probability in Machine Learning
  • Probability Model: Conditional Probability Total Probability Theorem
  • Probability Model: Probability Models Independence
  • Probability Model: Probability Models Conditional Independence
  • Probability Model: Probability Models Conditional Independence Exercise 01
  • Probability Model: Probability Models Conditional Independence Solution 01
  • Probability Model: Probability Models BayesRule
  • Probability Model: Probability Models towards Random Variables
  • Probability Model: Homework
  • Random Variables: Introduction
  • Random Variables: Random Variables Examples
  • Random Variables: Random Variables Examples Exercise 01
  • Random Variables: Random Variables Examples Solution 01
  • Random Variables: Bernulli Random Variables
  • Random Variables: Bernulli Trail Python Practice
  • Random Variables: Bernulli Trail Python Practice Exercise 01
  • Random Variables: Bernulli Trail Python Practice Solution 01
  • Random Variables: Geometric Random Variable
  • Random Variables: Geometric Random Variable Normalization Proof Optional
  • Random Variables: Geometric Random Variable Python Practice
  • Random Variables: Binomial Random Variables
  • Random Variables: Binomial Python Practice
  • Random Variables: Random Variables in Real Datasets
  • Random Variables: Random Variables in Real Datasets Exercise 01
  • Random Variables: Random Variables in Real Datasets Solution 01
  • Random Variables: Homework
  • Continuous Random Variables: Zero Probability to Individual Values
  • Continuous Random Variables: Zero Probability to Individual Values Exercise 01
  • Continuous Random Variables: Zero Probability to Individual Values Solution 01
  • Continuous Random Variables: Probability Density Functions
  • Continuous Random Variables: Probability Density Functions Exercise 01
  • Continuous Random Variables: Probability Density Functions Solution 01
  • Continuous Random Variables: Uniform Distribution
  • Continuous Random Variables: Uniform Distribution Exercise 01
  • Continuous Random Variables: Uniform Distribution Solution 01
  • Continuous Random Variables: Uniform Distribution Python
  • Continuous Random Variables: Exponential
  • Continuous Random Variables: Exponential Exercise 01
  • Continuous Random Variables: Exponential Solution 01
  • Continuous Random Variables: Exponential Python
  • Continuous Random Variables: Gaussian Random Variables
  • Continuous Random Variables: Gaussian Random Variables Exercise 01
  • Continuous Random Variables: Gaussian Random Variables Solution 01
  • Continuous Random Variables: Gaussian Python
  • Continuous Random Variables: Transformation of Random Variables
  • Continuous Random Variables: Homework
  • Expectations: Definition
  • Expectations: Sample Mean
  • Expectations: Law of Large Numbers
  • Expectations: Law of Large Numbers Famous Distributions
  • Expectations: Law of Large Numbers Famous Distributions Python
  • Expectations: Variance
  • Expectations: Homework
  • Project Bayes Classifier: Project Bayes Classifier from Scratch
  • Multiple Random Variables: Joint Distributions
  • Multiple Random Variables: Joint Distributions Exercise 01
  • Multiple Random Variables: Joint Distributions Solution 01
  • Multiple Random Variables: Joint Distributions Exercise 02
  • Multiple Random Variables: Joint Distributions Solution 02
  • Multiple Random Variables: Joint Distributions Exercise 03
  • Multiple Random Variables: Joint Distributions Solution 03
  • Multiple Random Variables: Multivariate Gaussian
  • Multiple Random Variables: Conditioning Independence
  • Multiple Random Variables: Classification
  • Multiple Random Variables: Naive Bayes Classification
  • Multiple Random Variables: Regression
  • Multiple Random Variables: Curse of Dimensionality
  • Multiple Random Variables: Homework
  • Optional Estimation: Parametric Distributions
  • Optional Estimation: MLE
  • Optional Estimation: Loglikelihood
  • Optional Estimation: MAP
  • Optional Estimation: Logistic Regression
  • Optional Estimation: Ridge Regression
  • Optional Estimation: DNN
  • Mathematical Derivations for Math Lovers (Optional): Permutations
  • Mathematical Derivations for Math Lovers (Optional): Combinations
  • Mathematical Derivations for Math Lovers (Optional): Binomial Random Variable
  • Mathematical Derivations for Math Lovers (Optional): Logistic Regression Formulation
  • Mathematical Derivations for Math Lovers (Optional): Logistic Regression Derivation

Machine Learning: Machine Learning Crash Course

  • Introduction
  • Introduction: Python Practical of the Course
  • Why Machine Learning: Machine Learning Applications-Part 1
  • Why Machine Learning: Machine Learning Applications-Part 2
  • Why Machine Learning: Why Machine Learning is Trending Now
  • Process of Learning from Data: Supervised Learning
  • Process of Learning from Data: Unsupervised Learning and Reinforcement Learning
  • Machine Learning Methods: Features
  • Machine Learning Methods: Features Practice with Python
  • Machine Learning Methods: Regression
  • Machine Learning Methods: Regression Practice with Python
  • Machine Learning Methods: Classification
  • Machine Learning Methods: Classification Practice with Python
  • Machine Learning Methods: Clustering
  • Machine Learning Methods: Clustering Practice with Python
  • Data Preparation and Pre-processing: Handling Image Data
  • Data Preparation and Preprocessing: Handling Video and Audio Data
  • Data Preparation and Preprocessing: Handling Text Data
  • Data Preparation and Preprocessing: One Hot Encoding
  • Data Preparation and Preprocessing: Data Standardization
  • Machine Learning Models and Optimization: Machine Learning Model 1
  • Machine Learning Models and Optimization: Machine Learning Model 2
  • Machine Learning Models and Optimization: Machine Learning Model 3
  • Machine Learning Models and Optimization: Training Process, Error, Cost and Loss
  • Machine Learning Models and Optimization: Optimization
  • Building Machine Learning Model from Scratch: Linear Regression from Scratch- Part 1
  • Building Machine Learning Model from Scratch: Linear Regression from Scratch- Part 2
  • Building Machine Learning Model from Scratch: Minimum-to-mean Distance Classifier from Scratch- Part 1
  • Building Machine Learning Model from Scratch: Minimum-to-mean Distance Classifier from Scratch- Part 2
  • Building Machine Learning Model from Scratch: K-Means Clustering from Scratch- Part 1
  • Building Machine Learning Model from Scratch: K-Means Clustering from Scratch- Part 2
  • Overfitting, Underfitting, and Generalization: Overfitting Introduction
  • Overfitting, Underfitting, and Generalization: Overfitting Example in Python
  • Overfitting, Underfitting, and Generalization: Regularization
  • Overfitting, Underfitting, and Generalization: Generalization
  • Overfitting, Underfitting, and Generalization: Data Snooping and the Test Set
  • Overfitting, Underfitting and Generalization: Cross-validation
  • Machine Learning Model Performance Metrics: The Accuracy
  • Machine Learning Model Performance Metrics: The Confusion Matrix
  • Dimensionality Reduction: The Curse of Dimensionality
  • Dimensionality Reduction: The Principal Component Analysis (PCA)
  • Deep Learning Overview: Introduction to Deep Neural Networks (DNN)
  • Deep Learning Overview: Introduction to Convolutional Neural Networks (CNN)
  • Deep Learning Overview: Introduction to Recurrent Neural Networks (CNN)
  • Hands-on Machine Learning Project Using Scikit-Learn: Principal Component Analysis (PCA) with Python
  • Hands-on Machine Learning Project Using Scikit-Learn: Pipeline in Scikit-Learn for Machine Learning Project
  • Hands-on Machine Learning Project Using Scikit-Learn: Cross-validation with Python
  • Hands-on Machine Learning Project Using Scikit-Learn: Face Recognition Project with Python
  • OPTIONAL Section- Mathematics Wrap-Up: Mathematical Wrap-Up on Machine Learning

Machine Learning: Feature Engineering and Dimensionality Reduction with Python

  • Introduction
  • Features in Data Science: Introduction to Feature in Data Science
  • Features in Data Science: Marking Facial Features
  • Features in Data Science: Feature Space
  • Features in Data Science: Features Dimensions
  • Features in Data Science: Features Dimensions Activity
  • Features in Data Science: Why Dimensionality Reduction
  • Features in Data Science: Activity-Dimensionality Reduction
  • Features in Data Science: Feature Dimensionality Reduction Methods
  • Feature Selection: Why Feature Selection
  • Feature Selection: Feature Selection Methods
  • Feature Selection: Filter Methods
  • Feature Selection: Wrapper Methods
  • Feature Selection: Embedded Methods
  • Feature Selection: Search Strategy
  • Feature Selection: Search Strategy Activity
  • Feature Selection: Statistical Based Methods
  • Feature Selection: Information Theoretic Methods
  • Feature Selection: Similarity Based Methods Introduction
  • Feature Selection: Similarity Based Methods Criteria
  • Feature Selection: Activity- Feature Selection in Python
  • Feature Selection: Activity- Feature Selection
  • Mathematical Foundation: Introduction to Mathematical Foundation of Feature Selection
  • Mathematical Foundation: Closure of a Set
  • Mathematical Foundation: Linear Combinations
  • Mathematical Foundation: Linear Independence
  • Mathematical Foundation: Vector Space
  • Mathematical Foundation: Basis and Dimensions
  • Mathematical Foundation: Coordinates Versus Dimensions
  • Mathematical Foundation: SubSpace
  • Mathematical Foundation: Orthonormal Basis
  • Mathematical Foundation: Matrix Product
  • Mathematical Foundation: Least Squares
  • Mathematical Foundation: Rank
  • Mathematical Foundation: Eigen Space
  • Mathematical Foundation: Positive Semi Definite Matrix
  • Mathematical Foundation: Singular Value Decomposition (SVD)
  • Mathematical Foundation: Lagrange Multipliers
  • Mathematical Foundation: Vector Derivatives
  • Mathematical Foundation: Linear Algebra Module Python
  • Mathematical Foundation: Activity-Linear Algebra Module Python
  • Feature Extraction: Feature Extraction Introduction
  • Feature Extraction: PCA Introduction
  • Feature Extraction: PCA Criteria
  • Feature Extraction: PCA Properties
  • Feature Extraction: PCA Max Variance Formulation
  • Feature Extraction: PCA Derivation
  • Feature Extraction: PCA Implementation
  • Feature Extraction: PCA For Small Sample Size Problems(DualPCA)
  • Feature Extraction: PCA Versus SVD
  • Feature Extraction: Kernel PCA
  • Feature Extraction: Kernel PCA Versus ISOMAP
  • Feature Extraction: Kernel PCA Versus the Rest
  • Feature Extraction: Encoder Decoder Networks for Dimensionality Reduction Versus Kernel PCA
  • Feature Extraction: Supervised PCA and Fishers Linear Discriminant Analysis
  • Feature Extraction: Supervised PCA and Fishers Linear Discriminant Analysis Activity
  • Feature Extraction: Dimensionality Reduction Pipelines Python Project
  • Feature Engineering: Categorical Features
  • Feature Engineering: Categorical Features Python
  • Feature Engineering: Text Features
  • Feature Engineering: Image Features
  • Feature Engineering: Derived Features
  • Feature Engineering: Derived Features Histogram of Gradients Local Binary Patterns
  • Feature Engineering: Feature Scaling
  • Feature Engineering: Activity-Feature Scaling

Deep learning: Artificial Neural Networks with Python

  • Introduction
  • Introduction to Machine Learning: Introduction to Machine Learning
  • Introduction to Machine Learning: Classification
  • Introduction to Machine Learning: Classification Exercise
  • Introduction to Machine Learning: Classification Solution
  • Introduction to Machine Learning: Classification Training Process and Prediction Probabilities
  • Introduction to Machine Learning: Classification Prediction Probabilities Exercise
  • Introduction to Machine Learning: Classification Prediction Probabilities Exercise Solution
  • Introduction to Machine Learning: Regression
  • Introduction to Machine Learning: Regression Exercise
  • Introduction to Machine Learning: Regression Exercise Solution
  • Introduction to Machine Learning: Supervised Learning
  • Introduction to Machine Learning: Unsupervised Learning
  • Introduction to Machine Learning: Reinforcement Learning
  • Introduction to Machine Learning: Machine Learning Model
  • Introduction to Machine Learning: Machine Learning Model Example
  • Introduction to Machine Learning: Machine Learning Model Exercise
  • Introduction to Machine Learning: Machine Learning Model Exercise Solution
  • Introduction to Machine Learning: Machine Learning Model Types
  • Introduction to Machine Learning: Machine Learning Model Linearity
  • Introduction to Machine Learning: Machine Learning Model Linearity Exercise
  • Introduction to Machine Learning: Machine Learning Model Linearity Exercise Solution
  • Introduction to Machine Learning: Machine Learning Model Multi Target Models
  • Introduction to Machine Learning: Machine Learning Model Multi Target Models Exercise
  • Introduction to Machine Learning: Machine Learning Model Multi Target Models Exercise Solution
  • Introduction to Machine Learning: Machine Learning Model Training Exercise
  • Introduction to Machine Learning: Machine Learning Model Training Exercise Solution
  • Introduction to Machine Learning: Machine Learning Model Training Loss
  • Introduction to Machine Learning: Machine Learning Model Hyperparameters Exercise
  • Introduction to Machine Learning: Machine Learning Model Hyperparameters Exercise Solution
  • Introduction to Machine Learning: Machine Learning Occam's Razor
  • Introduction to Machine Learning: Machine Learning Overfitting
  • Introduction to Machine Learning: Machine Learning Overfitting Exercise
  • Introduction to Machine Learning: Machine Learning Overfitting Exercise Solution Regularization
  • Introduction to Machine Learning: Machine Learning Overfitting Generalization
  • Introduction to Machine Learning: Machine Learning Data Snooping
  • Introduction to Machine Learning: Machine Learning Cross Validation
  • Introduction to Machine Learning: Machine Learning Hyperparameter Tunning Exercise
  • Introduction to Machine Learning: Machine Learning Hyperparameter Tunning Exercise Solution
  • DNN and Deep Learning Basics: Why PyTorch
  • DNN and Deep Learning Basics: PyTorch Installation and Tensors Introduction
  • DNN and Deep Learning Basics: Automatic Differentiation PyTorch New
  • DNN and Deep Learning Basics: Why DNNs in Machine Learning
  • DNN and Deep Learning Basics: Representational Power and Data Utilization Capacity of DNN
  • DNN and Deep Learning Basics: Perceptron
  • DNN and Deep Learning Basics: Perceptron Exercise
  • DNN and Deep Learning Basics: Perceptron Exercise Solution
  • DNN and Deep Learning Basics: Perceptron Implementation
  • DNN and Deep Learning Basics: DNN Architecture
  • DNN and Deep Learning Basics: DNN Architecture Exercise
  • DNN and Deep Learning Basics: DNN Architecture Exercise Solution
  • DNN and Deep Learning Basics: DNN ForwardStep Implementation
  • DNN and Deep Learning Basics: DNN Why Activation Function Is Required
  • DNN and Deep Learning Basics: DNN Why Activation Function Is Required Exercise
  • DNN and Deep Learning Basics: DNN Why Activation Function Is Required Exercise Solution
  • DNN and Deep Learning Basics: DNN Properties of Activation Function
  • DNN and Deep Learning Basics: DNN Activation Functions in PyTorch
  • DNN and Deep Learning Basics: DNN What is Loss Function
  • DNN and Deep Learning Basics: DNN What is Loss Function Exercise
  • DNN and Deep Learning Basics: DNN What is Loss Function Exercise Solution
  • DNN and Deep Learning Basics: DNN What is Loss Function Exercise 02
  • DNN and Deep Learning Basics: DNN What is Loss Function Exercise 02 Solution
  • DNN and Deep Learning Basics: DNN Loss Function in PyTorch
  • DNN and Deep Learning Basics: DNN Gradient Descent
  • DNN and Deep Learning Basics: DNN Gradient Descent Exercise
  • DNN and Deep Learning Basics: DNN Gradient Descent Exercise Solution
  • DNN and Deep Learning Basics: DNN Gradient Descent Implementation
  • DNN and Deep Learning Basics: DNN Gradient Descent Stochastic Batch Minibatch
  • DNN and Deep Learning Basics: DNN Gradient Descent Summary
  • DNN and Deep Learning Basics: DNN Implementation Gradient Step
  • DNN and Deep Learning Basics: DNN Implementation Stochastic Gradient Descent
  • DNN and Deep Learning Basics: DNN Implementation Batch Gradient Descent
  • DNN and Deep Learning Basics: DNN Implementation Minibatch Gradient Descent
  • DNN and Deep Learning Basics: DNN Implementation in PyTorch
  • DNN and Deep Learning Basics: DNN Weights Initializations
  • DNN and Deep Learning Basics: DNN Learning Rate
  • DNN and Deep Learning Basics: DNN Batch Normalization
  • DNN and Deep Learning Basics: DNN Batch Normalization Implementation
  • DNN and Deep Learning Basics: DNN Optimizations
  • DNN and Deep Learning Basics: DNN Dropout
  • DNN and Deep Learning Basics: DNN Dropout in PyTorch
  • DNN and Deep Learning Basics: DNN Early Stopping
  • DNN and Deep Learning Basics: DNN Hyperparameters
  • DNN and Deep Learning Basics: DNN PyTorch CIFAR10 Example
  • Deep Neural Networks and Deep Learning Basics: Introduction to Artificial Neural Networks
  • Deep Neural Networks and Deep Learning Basics: Neuron and Perceptron
  • Deep Neural Networks and Deep Learning Basics: Deep Neural Network Architecture
  • Deep Neural Networks and Deep Learning Basics: Feedforward Fully Connected MLP
  • Deep Neural Networks and Deep Learning Basics: Calculating Number of Weights of DNN
  • Deep Neural Networks and Deep Learning Basics: Number of Neurons Versus Number of Layers
  • Deep Neural Networks and Deep Learning Basics: Discriminative Versus Generative Learning
  • Deep Neural Networks and Deep Learning Basics: Universal Approximation Theorem
  • Deep Neural Networks and Deep Learning Basics: Why Depth
  • Deep Neural Networks and Deep Learning Basics: Decision Boundary in DNN
  • Deep Neural Networks and Deep Learning Basics: Bias Term
  • Deep Neural Networks and Deep Learning Basics: The Activation Function
  • Deep Neural Networks and Deep Learning Basics: DNN Training Parameters
  • Deep Neural Networks and Deep Learning Basics: Gradient Descent
  • Deep Neural Networks and Deep Learning Basics: Backpropagation
  • Deep Neural Networks and Deep Learning Basics: Training DNN Animation
  • Deep Neural Networks and Deep Learning Basics: Weight Initialization
  • Deep Neural Networks and Deep Learning Basics: Batch Minibatch Stochastic
  • Deep Neural Networks and Deep Learning Basics: Batch Normalization
  • Deep Neural Networks and Deep Learning Basics: Rprop Momentum
  • Deep Neural Networks and Deep Learning Basics: convergence Animation
  • Deep Neural Networks and Deep Learning Basics: Drop Out Early Stopping Hyperparameters
  • Python for Data Science: Python Packages for Data Science
  • Python for Data Science: NumPy Pandas and Matplotlib (Part 1)
  • Python for Data Science: NumPy Pandas and Matplotlib (Part 2)
  • Python for Data Science: NumPy Pandas and Matplotlib (Part 3)
  • Python for Data Science: NumPy Pandas and Matplotlib (Part 4)
  • Python for Data Science: NumPy Pandas and Matplotlib (Part 5)
  • Python for Data Science: NumPy Pandas and Matplotlib (Part 6)
  • Python for Data Science: Dataset Preprocessing
  • Python for Data Science: TensorFlow for classification
  • Implementation of DNN for COVID 19 Analysis: COVID19 Data Analysis
  • Implementation of DNN for COVID 19 Analysis: COVID19 Regression with TensorFlow

Deep learning: Convolutional Neural Networks with Python

  • Introduction: Why CNN
  • Introduction: Focus of the Course
  • Image Processing: Grayscale Images
  • Image Processing: RGB Images
  • Image Processing: Reading and Showing Images in Python
  • Image Processing: Converting an Image to Grayscale in Python
  • Image Processing: Image Formation
  • Image Processing: Image Blurring 1
  • Image Processing: Image Blurring 2
  • Image Processing: General Image Filtering
  • Image Processing: Convolution
  • Image Processing: Edge Detection
  • Image Processing: Image Sharpening
  • Image Processing: Implementation of Image Blurring Edge Detection Image Sharpening in Python
  • Image Processing: Parametric Shape Detection
  • Image Processing: Image Processing Activity
  • Object Detection: Introduction to Object Detection
  • Object Detection: Classification Pipeline
  • Object Detection: Sliding Window Implementation
  • Object Detection: Shift Scale Rotation Invariance
  • Object Detection: Person Detection
  • Object Detection: HOG Features
  • Object Detection: Hand Engineering Versus CNNs
  • Object Detection: Object Detection Activity
  • Deep Neural Network Architecture: Convolution Revisited
  • Deep Neural Network Architecture: Implementing Convolution in Python Revisited
  • Deep Neural Network Architecture: Why Convolution
  • Deep Neural Network Architecture: Filters Padding Strides
  • Deep Neural Network Architecture: Pooling Tensors
  • Deep Neural Network Architecture: CNN Example
  • Deep Neural Network Architecture: Convolution and Pooling Details
  • Deep Neural Network Architecture: Nonvectorized Implementations of Conv2d and Pool2d
  • Deep Neural Network Architecture Activity
  • Gradient Descent in CNNs: Example Setup
  • Gradient Descent in CNNs: Why Derivatives
  • Gradient Descent in CNNs: What is Chain Rule
  • Gradient Descent in CNNs: Applying Chain Rule
  • Gradient Descent in CNNs: Gradients of Convolutional Layer
  • Gradient Descent in CNNs: Extending to Multiple Filters
  • Gradient Descent in CNNs: Gradients of MaxPooling Layer
  • Gradient Descent in CNNs: Extending to Multiple Layers
  • Gradient Descent in CNNs: Implementation in NumPy ForwardPass.mp4.
  • Gradient Descent in CNNs: Implementation in NumPy BackwardPass 1
  • Gradient Descent in CNNs: Implementation in NumPy BackwardPass 2
  • Gradient Descent in CNNs: Implementation in NumPy BackwardPass 3
  • Gradient Descent in CNNs: Implementation in NumPy BackwardPass 4
  • Gradient Descent in CNNs: Implementation in NumPy BackwardPass 5
  • Gradient Descent in CNNs: Gradient Descent in CNNs Activity
  • Introduction to TensorFlow: Introduction
  • Introduction to TensorFlow: FashionMNIST Example Plan Neural Network
  • Introduction to TensorFlow: FashionMNIST Example CNN
  • Introduction to TensorFlow: Introduction to TensorFlow Activity
  • Classical CNNs: LeNet
  • Classical CNNs: AlexNet
  • Classical CNNs: VGG
  • Classical CNNs: InceptionNet
  • Classical CNNs: Google Net
  • Classical CNNs: Resnet
  • Classical CNNs: Classical CNNs Activity
  • Transfer Learning: What is Transfer learning
  • Transfer Learning: Why Transfer Learning
  • Transfer Learning: ImageNet Challenge
  • Transfer Learning: Practical Tips
  • Transfer Learning: Project in TensorFlow
  • Transfer Learning: Transfer Learning Activity
  • Yolo: Image Classification Revisited
  • Yolo: Sliding Window Object Localization
  • Yolo: Sliding Window Efficient Implementation
  • Yolo: Yolo Introduction
  • Yolo: Yolo Training Data Generation
  • Yolo: Yolo Anchor Boxes
  • Yolo: Yolo Algorithm
  • Yolo: Yolo Non-Maxima Suppression
  • Yolo: RCNN
  • Yolo: Yolo Activity
  • Face Verification: Problem Setup
  • Face Verification: Project Implementation
  • Face Verification: Face Verification Activity
  • Neural Style Transfer: Problem Setup
  • Neural Style Transfer: Implementation TensorFlow Hub

Deep learning: Recurrent Neural Networks with Python

  • Introduction
  • Applications of RNN (Motivation): Human Activity Recognition
  • Applications of RNN (Motivation): Image Captioning
  • Applications of RNN (Motivation): Machine Translation
  • Applications of RNN (Motivation): Speech Recognition
  • Applications of RNN (Motivation): Stock Price Predictions
  • Applications of RNN (Motivation): When to Model RNN
  • Applications of RNN (Motivation): Activity
  • RNN Architecture: Introduction to Module
  • RNN Architecture: Fixed Length Memory Model
  • RNN Architecture: Fixed Length Memory Model Exercise
  • RNN Architecture: Fixed Length Memory Model Exercise Solution Part 01
  • RNN Architecture: Fixed Length Memory Model Exercise Solution Part 02
  • RNN Architecture: Infinite Memory Architecture
  • RNN Architecture: Infinite Memory Architecture Exercise
  • RNN Architecture: Infinite Memory Architecture Solution
  • RNN Architecture: Weight Sharing
  • RNN Architecture: Notations
  • RNN Architecture: ManyToMany Model
  • RNN Architecture: ManyToMany Model Exercise 01
  • RNN Architecture: ManyToMany Model Solution 01
  • RNN Architecture: ManyToMany Model Exercise 02
  • RNN Architecture: ManyToMany Model Solution 02
  • RNN Architecture: ManyToOne Model
  • RNN Architecture: ManyToOne Model Exercise
  • RNN Architecture: ManyToOne Model Solution
  • RNN Architecture: OneToMany Model
  • RNN Architecture: OneToMany Model Exercise
  • RNN Architecture: OneToMany Model Solution
  • RNN Architecture: Activity Many to One
  • RNN Architecture: Activity Many to One Exercise
  • RNN Architecture: Activity Many to One Solution
  • RNN Architecture: ManyToMany Different Sizes Model
  • RNN Architecture: Activity Many to Many Nmt
  • RNN Architecture: Models Summary
  • RNN Architecture: Deep RNNs
  • RNN Architecture: Deep RNNs Exercise
  • RNN Architecture: Deep RNNs Solution
  • Gradient Descent in RNN: Introduction to Gradient Descent Module
  • Gradient Descent in RNN: Example Setup
  • Gradient Descent in RNN: Equations
  • Gradient Descent in RNN: Equations Exercise
  • Gradient Descent in RNN: Equations Solution
  • Gradient Descent in RNN: Loss Function
  • Gradient Descent in RNN: Why Gradients
  • Gradient Descent in RNN: Why Gradients Exercise
  • Gradient Descent in RNN: Why Gradients Solution
  • Gradient Descent in RNN: Chain Rule
  • Gradient Descent in RNN: Chain Rule in Action
  • Gradient Descent in RNN: Backpropagation Through Time
  • Gradient Descent in RNN: Activity
  • RNN Implementation: Automatic Differentiation
  • RNN Implementation: Automatic Differentiation PyTorch
  • RNN Implementation: Language Modelling Next Word Prediction Vocabulary Index
  • RNN Implementation: Language Modelling Next Word Prediction Vocabulary Index Embeddings
  • RNN Implementation: Language Modelling Next Word Prediction RNN Architecture
  • RNN Implementation: Language Modelling Next Word Prediction Python 1
  • RNN Implementation: Language Modelling Next Word Prediction Python 2
  • RNN Implementation: Language Modelling Next Word Prediction Python 3
  • RNN Implementation: Language Modelling Next Word Prediction Python 4
  • RNN Implementation: Language Modelling Next Word Prediction Python 5
  • RNN Implementation: Language Modelling Next Word Prediction Python 6
  • Sentiment Classification using RNN: Vocabulary Implementation
  • Sentiment Classification using RNN: Vocabulary Implementation Helpers
  • Sentiment Classification using RNN: Vocabulary Implementation from File
  • Sentiment Classification using RNN: Vectorizer
  • Sentiment Classification using RNN: RNN Setup 1
  • Sentiment Classification using RNN: RNN Setup 2
  • Sentiment Classification using RNN: What Next
  • Vanishing Gradients in RNN: Introduction to Better RNNs Module
  • Vanishing Gradients in RNN: Introduction Vanishing Gradients in RNN
  • Vanishing Gradients in RNN: GRU
  • Vanishing Gradients in RNN: GRU Optional
  • Vanishing Gradients in RNN: LSTM
  • Vanishing Gradients in RNN: LSTM Optional
  • Vanishing Gradients in RNN: Bidirectional RNN
  • Vanishing Gradients in RNN: Attention Model
  • Vanishing Gradients in RNN: Attention Model Optional
  • TensorFlow: Introduction to TensorFlow
  • TensorFlow: TensorFlow Text Classification Example using RNN
  • Project I_ Book Writer: Introduction
  • Project I_ Book Writer: Data Mapping
  • Project I_ Book Writer: Modelling RNN Architecture
  • Project I_ Book Writer: Modelling RNN Model in TensorFlow
  • Project I_ Book Writer: Modelling RNN Model Training
  • Project I_ Book Writer: Modelling RNN Model Text Generation
  • Project I_ Book Writer: Activity
  • Project II_ Stock Price Prediction: Problem Statement
  • Project II_ Stock Price Prediction: Dataset
  • Project II_ Stock Price Prediction: Data Preparation
  • Project II_ Stock Price Prediction: RNN Model Training and Evaluation
  • Project II_ Stock Price Prediction: Activity
  • Further Readings and Resources: Further Readings and Resources


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