Course curriculum

  • 1

    Introduction to the Course

  • 2

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

    • Introduction to the Course: Focus of the Course-Part 1 FREE PREVIEW
    • 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
  • 3

    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
    • 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
  • 4

    Basics for Data Science: Mastering Probability and Statistics in Python

    • Introduction to Course: Focus of the Course
    • 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
  • 5

    Machine Learning: Machine Learning Crash Course

    • Introduction to the Course: Focus of the Course
    • Introduction to the Course: 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: 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
    • OPTIONAL Section- Mathematics Wrap-up: Mathematical Wrap-up on Machine Learning
  • 6

    Machine Learning: Feature Engineering and Dimensionality Reduction with Python

    • Introduction: Focus of the Course
    • 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
  • 7

    Deep learning: Artificial Neural Networks with Python

    • Introduction to the Course: Why Deep learning Networks (DNN)
    • 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
  • 8

    Deep learning: Convolutional Neural Networks with Python

    • Introduction: Why CNN
    • Introduction: Focus of the Course
    • 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
  • 9

    Deep learning: Recurrent Neural Networks with Python

    • Introduction to Course: Focus of the Course
    • 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
    • RNN Implementation: Automatic Diffrenciation
    • RNN Implementation: Automatic Diffrenciation Pytorch
    • RNN Implementation: Language Modeling Next Word Prediction Vocabulary Index
    • RNN Implementation: Language Modeling Next Word Prediction Vocabulary Index Embeddings
    • RNN Implementation: Language Modeling Next Word Prediction RNN Architecture
    • RNN Implementation: Language Modeling Next Word Prediction Python 1
    • RNN Implementation: Language Modeling Next Word Prediction Python 2
    • RNN Implementation: Language Modeling Next Word Prediction Python 3
    • RNN Implementation: Language Modeling Next Word Prediction Python 4
    • RNN Implementation: Language Modeling Next Word Prediction Python 5
    • RNN Implementation: Language Modeling 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:WhatNext
    • 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
  • 10

    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