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