Data Science & Machine Learning(Theory+Projects)A-Z 90 HOURS

Introduction to the Course

Introduction to Courses and Instructor
Introduction to the Course: Feedbacks and Review
Link to Github to get the Python Notebooks

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

Introduction to the Course: Focus of the Course-Part 1
Introduction to the Course: Focus of the Course-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 Minimun
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 Anacon
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: Arange, Random and Reshape-Part 1
NumPy for Numerical Data Processing: Arange, 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 Concatination
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 Vs. 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: ScikitLearn- Trend Analysis COVID19
Scikit-Learn for Machine Learning: THANK YOU Bonus Video

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

Introduction to the Course: Focus of the Course
Introduction to the Course: Content of the Course
Introduction to the Course: Request for Your Honest Review
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 Output Argument
NumPy for Numerical Data Processing: NumPy Playing with Images
NumPy for Numerical Data Processing: NumPy KNN Classifier fromScratch
NumPy for Numerical Data Processing: NumPy Structured Arrays
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 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: Hierarchical Indexing
Pandas for Data Manipulation and Understanding: Pandas Rolling
Pandas for Data Manipulation and Understanding: Pandas Where
Pandas for Data Manipulation and Understanding: Pandas Clip
Pandas for Data Manipulation and Understanding: Pandas Merge
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 Shortcuts
Matplotlib for Data Visualization: Matplotlib Axis Limits
Matplotlib for Data Visualization: Matplotlib Legends Labels
Matplotlib for Data Visualization: Matplotlib Set Function
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 Histograms
Matplotlib for Data Visualization: Matplotlib Subplots
Matplotlib for Data Visualization: Matplotlib 3D Introduction
Matplotlib for Data Visualization: Matplotlib 3D Scatter Plots
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 Kind Line
Seaborn for Data Visualization: Seaborn Relplot Facets
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
Plotly for 3D Interactive Plotting: Plotly 3D Interactive Scatter Plot
Plotly for 3D Interactive Plotting: Plotly 3D Interactive Surface Plot
Geographic Maps with Folium: Geographic Maps with Folium using COVID-19 Data
Pandas for Plotting: Pandas for Plotting
Pandas for Plotting: THANK YOU Bonus Video

Basics for Data Science: Mastering Probability and Statistics in Python

Introduction to Course: Focus of the Course
Introduction to Course: Request for Your Honest Review
Probability vs Statistics: Probability vs Statistics
Sets: Definition of Set
Sets: Cardinality of a Set
Sets: Subsets PowerSet UniversalSet
Sets: Python Practice Subsets
Sets: PowerSets Solution
Sets: Operations
Sets: Python Practice Operations
Sets: VennDiagrams Operations
Sets: Homework
Experiment: Random Experiment
Experiment: Outcome and Sample Space
Experiment: Event
Experiment: Recap and Homework
Probability Model: Probability Model
Probability Model: Probability Axioms
Probability Model: Probability Axioms Derivations
Probability Model: Probablility Models Example
Probability Model: Probablility Models More Examples
Probability Model: Probablility Models Continous
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: Probablility Models Independence
Probability Model: Probablility Models Conditional Independence
Probability Model: Probablility Models BayesRule
Probability Model: Probablility Models towards Random Variables
Probability Model: HomeWork
Random Variables: Introduction
Random Variables: Random Variables Examples
Random Variables: Bernulli Random Variables
Random Variables: Bernulli Trail Python Practice
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: Homework
Continous Random Variables: Zero Probability to Individual Values
Continous Random Variables: Probability Density Functions
Continous Random Variables: Uniform Distribution
Continous Random Variables: Uniform Distribution Python
Continous Random Variables: Exponential
Continous Random Variables: Exponential Python
Continous Random Variables: Gaussian Random Variables
Continous Random Variables: Gaussian Python
Continous Random Variables: Transformation of Random Variables
Continous 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: 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: LogLiklihood
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
Mathematical Derivations for Math Lovers (Optional): THANK YOU Bonus Video

Machine Learning: Machine Learning Crash Course

Introduction to the Course: Focus of the Course
Introduction to the Course: Python Practical of the Course
Introduction to the Course: Your Feedback and Review
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: Classsification
Machine Learning Methods: Classification Practice with Python
Machine Learning Methods: Clustering
Machine Learning Methods: Clustering Practice with Python
Data Preparation and Preprocessing: 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: Minimun-to-mean Distance Classifier from Scratch- Part 1
Building Machine Learning Model from Scratch: Minimun-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 on 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
Hands-on Machine Learning Project Using Scikit-Learn: THANK YOU Bonus Video
OPTIONAL Section- Mathematics Wrap-up: Mathematical Wrap-up on Machine Learning

Machine Learning: Feature Engineering and Dimensionality Reduction with Python

Introduction: Focus of the Course
Introduction: Request for Your Honest Review
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 Theoratic 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 vs 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 vs SVD
Feature Extraction: Kernel PCA
Feature Extraction: Kernel PCA vs ISOMAP
Feature Extraction: Kernel PCA vs The Rest
Feature Extraction: Encoder Decoder Networks For Dimensionality Reduction vs 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
Feature Engineering: THANK YOU Bonus Video

Deep learning: Artificial Neural Networks with Python

Introduction to the Course: Why Deep learning Networks (DNN)
Introduction to the Course: Feedbacks and Review
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 Vs Number Of Layers
Deep Neural Networks and Deep Learning Basics: Discriminative Vs 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 Animantion
Deep Neural Networks and Deep Learning Basics: Weigth Initialization
Deep Neural Networks and Deep Learning Basics: Batch MiniBatch Stocastic
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
Implementation of DNN for COVID 19 Analysis: THANK YOU Bonus Video

Deep learning: Convolutional Neural Networks with Python

Introduction: Why CNN
Introduction: Focus of the Course
Introduction: Request for Your Honest Review
Image Processing: Gray Scale 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: Parameteric Shape Detection
Image Processing: Image Processing Activity
Object Detection: Introduction to Object Detection
Object Detection: Classification PipleLine
Object Detection: Sliding Window Implementation
Object Detection: Shift Scale Rotation Invariance
Object Detection: Person Detection
Object Detection: HOG Features
Object Detection: Hand Engineering vs 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: Deep Neural Network Architecture Activity
Gradient Descent in CNNs: Example Setup
Gradient Descent in CNNs: Why Derivaties
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: GoogLeNet
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 Classfication 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 Supression
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
Neural Style Transfer: THANK YOU Bonus Video

Deep learning: Recurrent Neural Networks with Python

Introduction to Course: Focus of the Course
Introduction to Course: Request for Your Honest Review
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: Infinite Memory Architecture
RNN Architecture: Weight Sharing
RNN Architecture: Notations
RNN Architecture: ManyToMany Model
RNN Architecture: OneToMany Model
RNN Architecture: ManyToOne Model
RNN Architecture: Activity Many to One
RNN Architecture: ManyToMany Different Sizes Model
RNN Architecture: Activity Many to Many Nmt
RNN Architecture: Models Summary
RNN Architecture: Deep RNNs
Gradient Decsent in RNN: Introduction to Gradient Descent Module
Gradient Decsent in RNN: Example Setup
Gradient Decsent in RNN: Equations
Gradient Decsent in RNN: Loss Function
Gradient Decsent in RNN: Why Gradients
Gradient Decsent in RNN: Chain Rule
Gradient Decsent in RNN: Chain Rule in Action
Gradient Decsent in RNN: BackPropagation Through Time
Gradient Decsent in RNN: Activity
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: Modling RNN Architecture
Project I_ Book Writer: Modling RNN Model in TensorFlow
Project I_ Book Writer: Modling RNN Model Training
Project I_ Book Writer: Modling RNN Model Text Generation
Project I_ Book Writer: Activity
Project II_ Stock Price Prediction: Problem Statement
Project II_ Stock Price Prediction: Data Set
Project II_ Stock Price Prediction: Data Prepration
Project II_ Stock Price Prediction: RNN Model Training and Evaluation
Project II_ Stock Price Prediction: Activity
Further Readings and Resourses: Further Readings and Resourses 1
Bonus Lecture: THANK YOU Bonus Video

Reinforcement Learning

Motivation Reinforcement Learning: What is Reinforcement Learning
Motivation Reinforcement Learning: What is Reinforcement Learning Hiders and Seekers by OpenAI
Motivation Reinforcement Learning: RL vs Other ML Frameworks
Motivation Reinforcement Learning: Why Reinforcement Learning
Motivation Reinforcement Learning: Examples of Reinforcement Learning
Motivation Reinforcement Learning: Limitations of Reinforcement Learning
Motivation Reinforcement Learning: Exercises
Terminology of Reinforcement Learning: What is Environment
Terminology of Reinforcement Learning: What is Environment_2
Terminology of Reinforcement Learning: What is Agent
Terminology of Reinforcement Learning: What is State
Terminology of Reinforcement Learning: State Belongs to Environment and not to Agent
Terminology of Reinforcement Learning: What is Action
Terminology of Reinforcement Learning: What is Reward
Terminology of Reinforcement Learning: Goal
Terminology of Reinforcement Learning: Policy
Terminology of Reinforcement Learning: Summary
GridWorld Example: Setup 1
GridWorld Example: Setup 2
GridWorld Example: Setup 3
GridWorld Example: Policy Comparison
GridWorld Example: Deterministic Environment
GridWorld Example: Stochastic Environment
GridWorld Example: Stochastic Environment 2
GridWorld Example: Stochastic Environment 3
GridWorld Example: Non Stationary Environment
GridWorld Example: GridWorld Summary
GridWorld Example: Activity
Markov Decision Process Prerequisites: Probability
Markov Decision Process Prerequisites: Probability 2
Markov Decision Process Prerequisites: Probability 3
Markov Decision Process Prerequisites: Conditional Probability
Markov Decision Process Prerequisites: Conditional Probability Fun Example
Markov Decision Process Prerequisites: Joint Probability
Markov Decision Process Prerequisites: Joint probability 2
Markov Decision Process Prerequisites: Joint Probability 3
Markov Decision Process Prerequisites: Expected Value
Markov Decision Process Prerequisites: Conditional Expectation
Markov Decision Process Prerequisites: Modeling Uncertainity of Environment
Markov Decision Process Prerequisites: Modeling Uncertainity of Environment 2
Markov Decision Process Prerequisites: Modeling Uncertainity of Environment 3
Markov Decision Process Prerequisites: Modeling Uncertainity of Environment Stochastic Policy
Markov Decision Process Prerequisites: Modeling Uncertainity of Environment Stochastic Policy 2
Markov Decision Process Prerequisites: Modeling Uncertainity of Environment Value Functions
Markov Decision Process Prerequisites: Running Averages
Markov Decision Process Prerequisites: Running Averages 2
Markov Decision Process Prerequisites: Running Averages as Temporal Difference
Markov Decision Process Prerequisites: Activity
Elements of Markov Decision Process: Markov Property
Elements of Markov Decision Process: State Space
Elements of Markov Decision Process: Action Space
Elements of Markov Decision Process: Transition Probabilities
Elements of Markov Decision Process: Reward Function
Elements of Markov Decision Process: Discount Factor
Elements of Markov Decision Process: Summary
Elements of Markov Decision Process: Activity
More on Reword: MOR Quiz 1
More on Reword: MOR Quiz Solution 1
More on Reword: MOR Quiz 2
More on Reword: MOR Quiz Solution 2
More on Reword: MOR Reward Scaling
More on Reword: MOR Infinite Horizons
More on Reword: MOR Quiz 3
More on Reword: MOR Quiz Solution 3
Solving MDP: MDP Recap
Solving MDP: Value Functions
Solving MDP: Optimal Value Function
Solving MDP: Optimal Policy
Solving MDP: Balman Equation
Solving MDP: Value Iteration
Solving MDP: Value Iteration Quiz
Solving MDP: Value Iteration Quiz Gamma Missing
Solving MDP: Value Iteration Solution
Solving MDP: Problems of Value Iteration
Solving MDP: Policy Evaluation
Solving MDP: Policy Evaluation 2
Solving MDP: Policy Evaluation 3
Solving MDP: Policy Evaluation Closed Form Solution
Solving MDP: Policy Iteration
Solving MDP: State Action Values
Solving MDP: V and Q Comparisons
Value Approximation: What does it mean that MDP is Unknown
Value Approximation: Why Transition Probabilities are Important
Value Approximation: Model Based Solutions
Value Approximation: Model Free Solutions
Value Approximation: Monte-Carlo Learning
Value Approximation: Monte-Carlo Learning Example
Value Approximation: Monte-Carlo Learning Limitations
Temporal Differencing-Q Learning: Running Average
Temporal Differencing-Q Learning: Learning Rate
Temporal Differencing-Q Learning: Learning Equation
Temporal Differencing-Q Learning: TD Algorithm
Temporal Differencing-Q Learning: Exploration vs Exploitation
Temporal Differencing-Q Learning: Epsilon Greedy Policy
Temporal Differencing-Q Learning: SARSA
Temporal Differencing-Q Learning: Q-Learning
Temporal Differencing-Q Learning: Q-Learning Implementation for MAPROVER Clipped
TD Lambda: N Step Look a Head
TD Lambda: Formulation
TD Lambda: Values
TD Lambda: TD Eligibility Trace
TD Lambda: TD Q-Learning TD Lambda
Project Frozenlake (Open AI Gym): Frozenlake 1
Project Frozenlake (Open AI Gym): Frozenlake Implementation
Conclusion