Table of Content
Introduction to the Course
- Introduction to Courses and Instructor
Basics for Data Science: Python for Data Science and Data Analysis
- Introduction - Part 1
- Introduction - Part 2
- Basics of Programming: Understanding the Algorithm
- Basics of Programming: FlowCharts and Pseudocodes
- Basics of Programming: Example of Algorithms - Making Tea Problem
- Basics of Programming: Example of Algorithms-Searching Minimum
- Basics of Programming: Example of Algorithms-Sorting Problem
- Basics of Programming: Sorting Problem in Python
- Why Python and Jupyter Notebook: Why Python
- Why Python and Jupyter Notebook: Why Jupyter Notebooks
- Installation of Anaconda and IPython Shell: Installing Python and Jupyter Anaconda
- Installation of Anaconda and IPython Shell: Your First Python Code - Hello World
- Installation of Anaconda and IPython Shell: Coding in IPython Shell
- Variable and Operator: Variables
- Variable and Operator: Operators
- Variable and Operator: Variable Name Quiz
- Variable and Operator: Bool Data Type in Python
- Variable and Operator: Comparison in Python
- Variable and Operator: Combining Comparisons in Python
- Variable and Operator: Combining Comparisons Quiz
- Python Useful function: Python Function- Round
- Python Useful function: Python Function- Divmod
- Python Useful function: Python Function- Is instance and PowFunctions
- Python Useful function: Python Function- Input
- Control Flow in Python: If Python Condition
- Control Flow in Python: if Elif Else Python Conditions
- Control Flow in Python: More on if Elif Else Python Conditions
- Control Flow in Python: Indentations
- Control Flow in Python: Comments and Problem-Solving Practice with If
- Control Flow in Python: While Loop
- Control Flow in Python: While Loop Break Continue
- Control Flow in Python: For Loop
- Control Flow in Python: Else in For Loop
- Control Flow in Python: Loops Practice-Sorting Problem
- Function and Module in Python: Functions in Python
- Function and Module in Python: DocString
- Function and Module in Python: Input Arguments
- Function and Module in Python: Multiple Input Arguments
- Function and Module in Python: Ordering Multiple Input Arguments
- Function and Module in Python: Output Arguments and Return Statement
- Function and Module in Python: Function Practice-Output Arguments and Return Statement
- Function and Module in Python: Variable Number of Input Arguments
- Function and Module in Python: Variable Number of Input Arguments as Dictionary
- Function and Module in Python: Default Values in Python
- Function and Module in Python: Modules in Python
- Function and Module in Python: Making Modules in Python
- Function and Module in Python: Function Practice-Sorting List in Python
- String in Python: Strings
- String in Python: Multi-Line Strings
- String in Python: Indexing Strings
- String in Python: String Methods
- String in Python: String Escape Sequences
- Data Structure (List, Tuple, Set, Dictionary): Introduction to Data Structure
- Data Structure (List, Tuple, Set, Dictionary): Defining and Indexing
- Data Structure (List, Tuple, Set, Dictionary): Insertion and Deletion
- Data Structure (List, Tuple, Set, Dictionary): Python Practice-Insertion and Deletion
- Data Structure (List, Tuple, Set, Dictionary): Deep Copy or Reference Slicing
- Data Structure (List, Tuple, Set, Dictionary): Exploring Methods Using TAB Completion
- Data Structure (List, Tuple, Set, Dictionary): Data Structure Abstract Ways
- Data Structure (List, Tuple, Set, Dictionary): Data Structure Practice
- NumPy for Numerical Data Processing: Introduction to NumPy
- NumPy for Numerical Data Processing: NumPy Dimensions
- NumPy for Numerical Data Processing: NumPy Shape, Size, and Bytes
- NumPy for Numerical Data Processing: Arrange, Random, and Reshape-Part 1
- NumPy for Numerical Data Processing: Arrange, Random, and Reshape-Part 2
- NumPy for Numerical Data Processing: Slicing-Part 1
- NumPy for Numerical Data Processing: Slicing-Part 2
- NumPy for Numerical Data Processing: NumPy Masking
- NumPy for Numerical Data Processing: NumPy BroadCasting and Concatenation
- NumPy for Numerical Data Processing: NumPy ufuncs Speed Test
- Pandas for Data Manipulation: Introduction to Pandas
- Pandas for Data Manipulation: Pandas Series
- Pandas for Data Manipulation: Pandas Data Frame
- Pandas for Data Manipulation: Pandas Missing Values
- Pandas for Data Manipulation: Pandas .loc and .iloc
- Pandas for Data Manipulation: Pandas Practice-Using COVID19 Data - Part 1
- Pandas for Data Manipulation: Pandas Practice-Using COVID19 Data - Part 2
- Matplotlib, Seaborn, and Bokeh for Data Visualization: Introduction to Matplotlib
- Matplotlib, Seaborn, and Bokeh for Data Visualization: Seaborn Versus Matplotlib Style
- Matplotlib, Seaborn, and Bokeh for Data Visualization: Histograms Kdeplot
- Matplotlib, Seaborn, and Bokeh for Data Visualization: Seaborn Pairplot and Jointplot
- Matplotlib, Seaborn, and Bokeh for Data Visualization: Seaborn Pairplot using Iris Data
- Matplotlib, Seaborn, and Bokeh for Data Visualization: Introduction to Bokeh
- Matplotlib, Seaborn, and Bokeh for Data Visualization: Bokeh Gridplot
- Scikit-Learn for Machine Learning: Introduction to Scikit-Learn
- Scikit-Learn for Machine Learning: Scikit-Learn for Linear Regression
- Scikit-Learn for Machine Learning: Scikit-Learn for SVM and Random Forests
- Scikit-Learn for Machine Learning: Scikit-Learn - Trend Analysis COVID19
Basics for Data Science: Data Understanding and Data Visualization with Python
- Introduction
- What We will Learn
- NumPy for Numerical Data Processing: Ufuncs Add, Sum, and Plus Operators
- NumPy for Numerical Data Processing: Ufuncs Subtract Power Mod
- NumPy for Numerical Data Processing: Ufuncs Comparisons Logical Operators
- NumPy for Numerical Data Processing: Ufuncs Comparisons Logical Operators Quiz
- NumPy for Numerical Data Processing: Ufuncs Comparisons Logical Operators Solution
- NumPy for Numerical Data Processing: Ufuncs Output Argument
- NumPy for Numerical Data Processing: NumPy Playing with Images
- NumPy for Numerical Data Processing: NumPy Playing with Images Quiz
- NumPy for Numerical Data Processing: NumPy Playing with Images Solution
- NumPy for Numerical Data Processing: NumPy KNN Classifier from Scratch
- NumPy for Numerical Data Processing: NumPy Structured Arrays
- NumPy for Numerical Data Processing: NumPy Structured Arrays Quiz
- NumPy for Numerical Data Processing: NumPy Structured Arrays Solution
- Pandas for Data Manipulation and Understanding: Introduction to Pandas
- Pandas for Data Manipulation and Understanding: Pandas Series
- Pandas for Data Manipulation and Understanding: Pandas DataFrame
- Pandas for Data Manipulation and Understanding: Pandas DataFrame Quiz
- Pandas for Data Manipulation and Understanding: Pandas DataFrame Solution
- Pandas for Data Manipulation and Understanding: Pandas Missing Values
- Pandas for Data Manipulation and Understanding: Pandas Loc Iloc
- Pandas for Data Manipulation and Understanding: Pandas in Practice
- Pandas for Data Manipulation and Understanding: Pandas Group By
- Pandas for Data Manipulation and Understanding: Pandas Group By Quiz
- Pandas for Data Manipulation and Understanding: Pandas Group by Solution
- Pandas for Data Manipulation and Understanding: Hierarchical Indexing
- Pandas for Data Manipulation and Understanding: Pandas Rolling
- Pandas for Data Manipulation and Understanding: Pandas Rolling Quiz
- Pandas for Data Manipulation and Understanding: Pandas Rolling Solution
- Pandas for Data Manipulation and Understanding: Pandas Where
- Pandas for Data Manipulation and Understanding: Pandas Clip
- Pandas for Data Manipulation and Understanding: Pandas Clip Quiz
- Pandas for Data Manipulation and Understanding: Pandas Clip Solution
- Pandas for Data Manipulation and Understanding: Pandas Merge
- Pandas for Data Manipulation and Understanding: Pandas Merge Quiz
- Pandas for Data Manipulation and Understanding: Pandas Merge Solution
- Pandas for Data Manipulation and Understanding: Pandas Pivot Table
- Pandas for Data Manipulation and Understanding: Pandas Strings
- Pandas for Data Manipulation and Understanding: Pandas DateTime
- Pandas for Data Manipulation and Understanding: Pandas Hands on COVID19 Data
- Pandas for Data Manipulation and Understanding: Pandas Hands on COVID19 Data Bug
- Matplotlib for Data Visualization: Introduction to Matplotlib
- Matplotlib for Data Visualization: Matplotlib Multiple Plots
- Matplotlib for Data Visualization: Matplotlib Colors and Styles
- Matplotlib for Data Visualization: Matplotlib Colors and Styles Quiz
- Matplotlib for Data Visualization: Matplotlib Colors and Styles Solution
- Matplotlib for Data Visualization: Matplotlib Colors and Styles Shortcuts
- Matplotlib for Data Visualization: Matplotlib Axis Limits
- Matplotlib for Data Visualization: Matplotlib Axis Limits Quiz
- Matplotlib for Data Visualization: Matplotlib Axis Limits Solution
- Matplotlib for Data Visualization: Matplotlib Legends Labels
- Matplotlib for Data Visualization: Matplotlib Set Function
- Matplotlib for Data Visualization: Matplotlib Set Function Quiz
- Matplotlib for Data Visualization: Matplotlib Set Function Solution
- Matplotlib for Data Visualization: Matplotlib Markers
- Matplotlib for Data Visualization: Matplotlib Markers Randomplots
- Matplotlib for Data Visualization: Matplotlib Scatter Plot
- Matplotlib for Data Visualization: Matplotlib Contour Plot
- Matplotlib for Data Visualization: Matplotlib Contour Plot Quiz
- Matplotlib for Data Visualization: Matplotlib Contour Plot Solution
- Matplotlib for Data Visualization: Matplotlib Histograms
- Matplotlib for Data Visualization: Matplotlib Subplots
- Matplotlib for Data Visualization: Matplotlib Subplots Quiz
- Matplotlib for Data Visualization: Matplotlib Subplots Solution
- Matplotlib for Data Visualization: Matplotlib 3D Introduction
- Matplotlib for Data Visualization: Matplotlib 3D Scatter Plots
- Matplotlib for Data Visualization: Matplotlib 3D Scatter Plots Quiz
- Matplotlib for Data Visualization: Matplotlib 3D Scatter Plots Solution
- Matplotlib for Data Visualization: Matplotlib 3D Surface Plots
- Seaborn for Data Visualization: Introduction to Seaborn
- Seaborn for Data Visualization: Seaborn Relplot
- Seaborn for Data Visualization: Seaborn Relplot Quiz
- Seaborn for Data Visualization: Seaborn Relplot Solution
- Seaborn for Data Visualization: Seaborn Relplot Kind Line
- Seaborn for Data Visualization: Seaborn Relplot Facets
- Seaborn for Data Visualization: Seaborn Relplot Facets Quiz
- Seaborn for Data Visualization: Seaborn Relplot Facets Solution
- Seaborn for Data Visualization: Seaborn Catplot
- Seaborn for Data Visualization: Seaborn Heatmaps
- Bokeh for Interactive Plotting: Introduction to Bokeh
- Bokeh for Interactive Plotting: Bokeh Multiplots Markers
- Bokeh for Interactive Plotting: Bokeh Multiplots Grid Plot
- Bokeh for Interactive Plotting: Bokeh Multiplots Grid Plot Quiz
- Bokeh for Interactive Plotting: Bokeh Multiplots Grid Plot Solution
- Plotly for 3D Interactive Plotting: Plotly 3D Interactive Scatter Plot
- Plotly for 3D Interactive Plotting: Plotly 3D Interactive Scatter Plot Quiz
- Plotly for 3D Interactive Plotting: Plotly 3D Interactive Scatter Plot Solution
- Plotly for 3D Interactive Plotting: Plotly 3D Interactive Surface Plot
- Plotly for 3D Interactive Plotting: Plotly 3D Interactive Surface Plot Quiz
- Plotly for 3D Interactive Plotting: Plotly 3D Interactive Surface Plot Solution
- Geographic Maps with Folium: Geographic Maps with Folium using COVID-19 Data
- Geographic Maps with Folium: Geographic Maps with Folium using COVID-19 Data Quiz
- Geographic Maps with Folium: Geographic Maps with Folium using COVID-19 Data Solution
- Pandas for Plotting: Pandas for Plotting
Basics for Data Science: Mastering Probability and Statistics in Python
- Introduction
- Probability Versus Statistics
- Sets: Definition of Set
- Sets: Definition of Set Exercise 01
- Sets: Definition of Set Solution 01
- Sets: Definition of Set Exercise 02
- Sets: Definition of Set Solution 02
- Sets: Cardinality of a Set
- Sets: Subsets PowerSet UniversalSet
- Sets: Python Practice Subsets
- Sets: PowerSets Solution
- Sets: Operations
- Sets: Operations Exercise 01
- Sets: Operations Solution 01
- Sets: Operations Exercise 02
- Sets: Operations Solution 02
- Sets: Operations Exercise 03
- Sets: Operations Solution 03
- Sets: Python Practice Operations
- Sets: Venn Diagrams Operations
- Sets: Homework
- Experiment: Random Experiment
- Experiment: Outcome and Sample Space
- Experiment: Outcome and Sample Space Exercise 01
- Experiment: Outcome and Sample Space Solution 01
- Experiment: Event
- Experiment: Event Exercise 01
- Experiment: Event Solution 01
- Experiment: Event Exercise 02
- Experiment: Event Solution 02
- Experiment: Recap and Homework
- Probability Model: Probability Model
- Probability Model: Probability Axioms
- Probability Model: Probability Axioms Derivations
- Probability Model: Probability Axioms Derivations Exercise 01
- Probability Model: Probability Axioms Derivations Solution 01
- Probability Model: Probability Models Example
- Probability Model: Probability Models More Examples
- Probability Model: Probability Models Continuous
- Probability Model: Conditional Probability
- Probability Model: Conditional Probability Example
- Probability Model: Conditional Probability Formula
- Probability Model: Conditional Probability in Machine Learning
- Probability Model: Conditional Probability Total Probability Theorem
- Probability Model: Probability Models Independence
- Probability Model: Probability Models Conditional Independence
- Probability Model: Probability Models Conditional Independence Exercise 01
- Probability Model: Probability Models Conditional Independence Solution 01
- Probability Model: Probability Models BayesRule
- Probability Model: Probability Models towards Random Variables
- Probability Model: Homework
- Random Variables: Introduction
- Random Variables: Random Variables Examples
- Random Variables: Random Variables Examples Exercise 01
- Random Variables: Random Variables Examples Solution 01
- Random Variables: Bernulli Random Variables
- Random Variables: Bernulli Trail Python Practice
- Random Variables: Bernulli Trail Python Practice Exercise 01
- Random Variables: Bernulli Trail Python Practice Solution 01
- Random Variables: Geometric Random Variable
- Random Variables: Geometric Random Variable Normalization Proof Optional
- Random Variables: Geometric Random Variable Python Practice
- Random Variables: Binomial Random Variables
- Random Variables: Binomial Python Practice
- Random Variables: Random Variables in Real Datasets
- Random Variables: Random Variables in Real Datasets Exercise 01
- Random Variables: Random Variables in Real Datasets Solution 01
- Random Variables: Homework
- Continuous Random Variables: Zero Probability to Individual Values
- Continuous Random Variables: Zero Probability to Individual Values Exercise 01
- Continuous Random Variables: Zero Probability to Individual Values Solution 01
- Continuous Random Variables: Probability Density Functions
- Continuous Random Variables: Probability Density Functions Exercise 01
- Continuous Random Variables: Probability Density Functions Solution 01
- Continuous Random Variables: Uniform Distribution
- Continuous Random Variables: Uniform Distribution Exercise 01
- Continuous Random Variables: Uniform Distribution Solution 01
- Continuous Random Variables: Uniform Distribution Python
- Continuous Random Variables: Exponential
- Continuous Random Variables: Exponential Exercise 01
- Continuous Random Variables: Exponential Solution 01
- Continuous Random Variables: Exponential Python
- Continuous Random Variables: Gaussian Random Variables
- Continuous Random Variables: Gaussian Random Variables Exercise 01
- Continuous Random Variables: Gaussian Random Variables Solution 01
- Continuous Random Variables: Gaussian Python
- Continuous Random Variables: Transformation of Random Variables
- Continuous Random Variables: Homework
- Expectations: Definition
- Expectations: Sample Mean
- Expectations: Law of Large Numbers
- Expectations: Law of Large Numbers Famous Distributions
- Expectations: Law of Large Numbers Famous Distributions Python
- Expectations: Variance
- Expectations: Homework
- Project Bayes Classifier: Project Bayes Classifier from Scratch
- Multiple Random Variables: Joint Distributions
- Multiple Random Variables: Joint Distributions Exercise 01
- Multiple Random Variables: Joint Distributions Solution 01
- Multiple Random Variables: Joint Distributions Exercise 02
- Multiple Random Variables: Joint Distributions Solution 02
- Multiple Random Variables: Joint Distributions Exercise 03
- Multiple Random Variables: Joint Distributions Solution 03
- Multiple Random Variables: Multivariate Gaussian
- Multiple Random Variables: Conditioning Independence
- Multiple Random Variables: Classification
- Multiple Random Variables: Naive Bayes Classification
- Multiple Random Variables: Regression
- Multiple Random Variables: Curse of Dimensionality
- Multiple Random Variables: Homework
- Optional Estimation: Parametric Distributions
- Optional Estimation: MLE
- Optional Estimation: Loglikelihood
- Optional Estimation: MAP
- Optional Estimation: Logistic Regression
- Optional Estimation: Ridge Regression
- Optional Estimation: DNN
- Mathematical Derivations for Math Lovers (Optional): Permutations
- Mathematical Derivations for Math Lovers (Optional): Combinations
- Mathematical Derivations for Math Lovers (Optional): Binomial Random Variable
- Mathematical Derivations for Math Lovers (Optional): Logistic Regression Formulation
- Mathematical Derivations for Math Lovers (Optional): Logistic Regression Derivation
Machine Learning: Machine Learning Crash Course
- Introduction
- Introduction: Python Practical of the Course
- Why Machine Learning: Machine Learning Applications-Part 1
- Why Machine Learning: Machine Learning Applications-Part 2
- Why Machine Learning: Why Machine Learning is Trending Now
- Process of Learning from Data: Supervised Learning
- Process of Learning from Data: Unsupervised Learning and Reinforcement Learning
- Machine Learning Methods: Features
- Machine Learning Methods: Features Practice with Python
- Machine Learning Methods: Regression
- Machine Learning Methods: Regression Practice with Python
- Machine Learning Methods: Classification
- Machine Learning Methods: Classification Practice with Python
- Machine Learning Methods: Clustering
- Machine Learning Methods: Clustering Practice with Python
- Data Preparation and Pre-processing: Handling Image Data
- Data Preparation and Preprocessing: Handling Video and Audio Data
- Data Preparation and Preprocessing: Handling Text Data
- Data Preparation and Preprocessing: One Hot Encoding
- Data Preparation and Preprocessing: Data Standardization
- Machine Learning Models and Optimization: Machine Learning Model 1
- Machine Learning Models and Optimization: Machine Learning Model 2
- Machine Learning Models and Optimization: Machine Learning Model 3
- Machine Learning Models and Optimization: Training Process, Error, Cost and Loss
- Machine Learning Models and Optimization: Optimization
- Building Machine Learning Model from Scratch: Linear Regression from Scratch- Part 1
- Building Machine Learning Model from Scratch: Linear Regression from Scratch- Part 2
- Building Machine Learning Model from Scratch: Minimum-to-mean Distance Classifier from Scratch- Part 1
- Building Machine Learning Model from Scratch: Minimum-to-mean Distance Classifier from Scratch- Part 2
- Building Machine Learning Model from Scratch: K-Means Clustering from Scratch- Part 1
- Building Machine Learning Model from Scratch: K-Means Clustering from Scratch- Part 2
- Overfitting, Underfitting, and Generalization: Overfitting Introduction
- Overfitting, Underfitting, and Generalization: Overfitting Example in Python
- Overfitting, Underfitting, and Generalization: Regularization
- Overfitting, Underfitting, and Generalization: Generalization
- Overfitting, Underfitting, and Generalization: Data Snooping and the Test Set
- Overfitting, Underfitting and Generalization: Cross-validation
- Machine Learning Model Performance Metrics: The Accuracy
- Machine Learning Model Performance Metrics: The Confusion Matrix
- Dimensionality Reduction: The Curse of Dimensionality
- Dimensionality Reduction: The Principal Component Analysis (PCA)
- Deep Learning Overview: Introduction to Deep Neural Networks (DNN)
- Deep Learning Overview: Introduction to Convolutional Neural Networks (CNN)
- Deep Learning Overview: Introduction to Recurrent Neural Networks (CNN)
- Hands-on Machine Learning Project Using Scikit-Learn: Principal Component Analysis (PCA) with Python
- Hands-on Machine Learning Project Using Scikit-Learn: Pipeline in Scikit-Learn for Machine Learning Project
- Hands-on Machine Learning Project Using Scikit-Learn: Cross-validation with Python
- Hands-on Machine Learning Project Using Scikit-Learn: Face Recognition Project with Python
- OPTIONAL Section- Mathematics Wrap-Up: Mathematical Wrap-Up on Machine Learning
Machine Learning: Feature Engineering and Dimensionality Reduction with Python
- Introduction
- Features in Data Science: Introduction to Feature in Data Science
- Features in Data Science: Marking Facial Features
- Features in Data Science: Feature Space
- Features in Data Science: Features Dimensions
- Features in Data Science: Features Dimensions Activity
- Features in Data Science: Why Dimensionality Reduction
- Features in Data Science: Activity-Dimensionality Reduction
- Features in Data Science: Feature Dimensionality Reduction Methods
- Feature Selection: Why Feature Selection
- Feature Selection: Feature Selection Methods
- Feature Selection: Filter Methods
- Feature Selection: Wrapper Methods
- Feature Selection: Embedded Methods
- Feature Selection: Search Strategy
- Feature Selection: Search Strategy Activity
- Feature Selection: Statistical Based Methods
- Feature Selection: Information Theoretic Methods
- Feature Selection: Similarity Based Methods Introduction
- Feature Selection: Similarity Based Methods Criteria
- Feature Selection: Activity- Feature Selection in Python
- Feature Selection: Activity- Feature Selection
- Mathematical Foundation: Introduction to Mathematical Foundation of Feature Selection
- Mathematical Foundation: Closure of a Set
- Mathematical Foundation: Linear Combinations
- Mathematical Foundation: Linear Independence
- Mathematical Foundation: Vector Space
- Mathematical Foundation: Basis and Dimensions
- Mathematical Foundation: Coordinates Versus Dimensions
- Mathematical Foundation: SubSpace
- Mathematical Foundation: Orthonormal Basis
- Mathematical Foundation: Matrix Product
- Mathematical Foundation: Least Squares
- Mathematical Foundation: Rank
- Mathematical Foundation: Eigen Space
- Mathematical Foundation: Positive Semi Definite Matrix
- Mathematical Foundation: Singular Value Decomposition (SVD)
- Mathematical Foundation: Lagrange Multipliers
- Mathematical Foundation: Vector Derivatives
- Mathematical Foundation: Linear Algebra Module Python
- Mathematical Foundation: Activity-Linear Algebra Module Python
- Feature Extraction: Feature Extraction Introduction
- Feature Extraction: PCA Introduction
- Feature Extraction: PCA Criteria
- Feature Extraction: PCA Properties
- Feature Extraction: PCA Max Variance Formulation
- Feature Extraction: PCA Derivation
- Feature Extraction: PCA Implementation
- Feature Extraction: PCA For Small Sample Size Problems(DualPCA)
- Feature Extraction: PCA Versus SVD
- Feature Extraction: Kernel PCA
- Feature Extraction: Kernel PCA Versus ISOMAP
- Feature Extraction: Kernel PCA Versus the Rest
- Feature Extraction: Encoder Decoder Networks for Dimensionality Reduction Versus Kernel PCA
- Feature Extraction: Supervised PCA and Fishers Linear Discriminant Analysis
- Feature Extraction: Supervised PCA and Fishers Linear Discriminant Analysis Activity
- Feature Extraction: Dimensionality Reduction Pipelines Python Project
- Feature Engineering: Categorical Features
- Feature Engineering: Categorical Features Python
- Feature Engineering: Text Features
- Feature Engineering: Image Features
- Feature Engineering: Derived Features
- Feature Engineering: Derived Features Histogram of Gradients Local Binary Patterns
- Feature Engineering: Feature Scaling
- Feature Engineering: Activity-Feature Scaling
Deep learning: Artificial Neural Networks with Python
- Introduction
- Introduction to Machine Learning: Introduction to Machine Learning
- Introduction to Machine Learning: Classification
- Introduction to Machine Learning: Classification Exercise
- Introduction to Machine Learning: Classification Solution
- Introduction to Machine Learning: Classification Training Process and Prediction Probabilities
- Introduction to Machine Learning: Classification Prediction Probabilities Exercise
- Introduction to Machine Learning: Classification Prediction Probabilities Exercise Solution
- Introduction to Machine Learning: Regression
- Introduction to Machine Learning: Regression Exercise
- Introduction to Machine Learning: Regression Exercise Solution
- Introduction to Machine Learning: Supervised Learning
- Introduction to Machine Learning: Unsupervised Learning
- Introduction to Machine Learning: Reinforcement Learning
- Introduction to Machine Learning: Machine Learning Model
- Introduction to Machine Learning: Machine Learning Model Example
- Introduction to Machine Learning: Machine Learning Model Exercise
- Introduction to Machine Learning: Machine Learning Model Exercise Solution
- Introduction to Machine Learning: Machine Learning Model Types
- Introduction to Machine Learning: Machine Learning Model Linearity
- Introduction to Machine Learning: Machine Learning Model Linearity Exercise
- Introduction to Machine Learning: Machine Learning Model Linearity Exercise Solution
- Introduction to Machine Learning: Machine Learning Model Multi Target Models
- Introduction to Machine Learning: Machine Learning Model Multi Target Models Exercise
- Introduction to Machine Learning: Machine Learning Model Multi Target Models Exercise Solution
- Introduction to Machine Learning: Machine Learning Model Training Exercise
- Introduction to Machine Learning: Machine Learning Model Training Exercise Solution
- Introduction to Machine Learning: Machine Learning Model Training Loss
- Introduction to Machine Learning: Machine Learning Model Hyperparameters Exercise
- Introduction to Machine Learning: Machine Learning Model Hyperparameters Exercise Solution
- Introduction to Machine Learning: Machine Learning Occam's Razor
- Introduction to Machine Learning: Machine Learning Overfitting
- Introduction to Machine Learning: Machine Learning Overfitting Exercise
- Introduction to Machine Learning: Machine Learning Overfitting Exercise Solution Regularization
- Introduction to Machine Learning: Machine Learning Overfitting Generalization
- Introduction to Machine Learning: Machine Learning Data Snooping
- Introduction to Machine Learning: Machine Learning Cross Validation
- Introduction to Machine Learning: Machine Learning Hyperparameter Tunning Exercise
- Introduction to Machine Learning: Machine Learning Hyperparameter Tunning Exercise Solution
- DNN and Deep Learning Basics: Why PyTorch
- DNN and Deep Learning Basics: PyTorch Installation and Tensors Introduction
- DNN and Deep Learning Basics: Automatic Differentiation PyTorch New
- DNN and Deep Learning Basics: Why DNNs in Machine Learning
- DNN and Deep Learning Basics: Representational Power and Data Utilization Capacity of DNN
- DNN and Deep Learning Basics: Perceptron
- DNN and Deep Learning Basics: Perceptron Exercise
- DNN and Deep Learning Basics: Perceptron Exercise Solution
- DNN and Deep Learning Basics: Perceptron Implementation
- DNN and Deep Learning Basics: DNN Architecture
- DNN and Deep Learning Basics: DNN Architecture Exercise
- DNN and Deep Learning Basics: DNN Architecture Exercise Solution
- DNN and Deep Learning Basics: DNN ForwardStep Implementation
- DNN and Deep Learning Basics: DNN Why Activation Function Is Required
- DNN and Deep Learning Basics: DNN Why Activation Function Is Required Exercise
- DNN and Deep Learning Basics: DNN Why Activation Function Is Required Exercise Solution
- DNN and Deep Learning Basics: DNN Properties of Activation Function
- DNN and Deep Learning Basics: DNN Activation Functions in PyTorch
- DNN and Deep Learning Basics: DNN What is Loss Function
- DNN and Deep Learning Basics: DNN What is Loss Function Exercise
- DNN and Deep Learning Basics: DNN What is Loss Function Exercise Solution
- DNN and Deep Learning Basics: DNN What is Loss Function Exercise 02
- DNN and Deep Learning Basics: DNN What is Loss Function Exercise 02 Solution
- DNN and Deep Learning Basics: DNN Loss Function in PyTorch
- DNN and Deep Learning Basics: DNN Gradient Descent
- DNN and Deep Learning Basics: DNN Gradient Descent Exercise
- DNN and Deep Learning Basics: DNN Gradient Descent Exercise Solution
- DNN and Deep Learning Basics: DNN Gradient Descent Implementation
- DNN and Deep Learning Basics: DNN Gradient Descent Stochastic Batch Minibatch
- DNN and Deep Learning Basics: DNN Gradient Descent Summary
- DNN and Deep Learning Basics: DNN Implementation Gradient Step
- DNN and Deep Learning Basics: DNN Implementation Stochastic Gradient Descent
- DNN and Deep Learning Basics: DNN Implementation Batch Gradient Descent
- DNN and Deep Learning Basics: DNN Implementation Minibatch Gradient Descent
- DNN and Deep Learning Basics: DNN Implementation in PyTorch
- DNN and Deep Learning Basics: DNN Weights Initializations
- DNN and Deep Learning Basics: DNN Learning Rate
- DNN and Deep Learning Basics: DNN Batch Normalization
- DNN and Deep Learning Basics: DNN Batch Normalization Implementation
- DNN and Deep Learning Basics: DNN Optimizations
- DNN and Deep Learning Basics: DNN Dropout
- DNN and Deep Learning Basics: DNN Dropout in PyTorch
- DNN and Deep Learning Basics: DNN Early Stopping
- DNN and Deep Learning Basics: DNN Hyperparameters
- DNN and Deep Learning Basics: DNN PyTorch CIFAR10 Example
- Deep Neural Networks and Deep Learning Basics: Introduction to Artificial Neural Networks
- Deep Neural Networks and Deep Learning Basics: Neuron and Perceptron
- Deep Neural Networks and Deep Learning Basics: Deep Neural Network Architecture
- Deep Neural Networks and Deep Learning Basics: Feedforward Fully Connected MLP
- Deep Neural Networks and Deep Learning Basics: Calculating Number of Weights of DNN
- Deep Neural Networks and Deep Learning Basics: Number of Neurons Versus Number of Layers
- Deep Neural Networks and Deep Learning Basics: Discriminative Versus Generative Learning
- Deep Neural Networks and Deep Learning Basics: Universal Approximation Theorem
- Deep Neural Networks and Deep Learning Basics: Why Depth
- Deep Neural Networks and Deep Learning Basics: Decision Boundary in DNN
- Deep Neural Networks and Deep Learning Basics: Bias Term
- Deep Neural Networks and Deep Learning Basics: The Activation Function
- Deep Neural Networks and Deep Learning Basics: DNN Training Parameters
- Deep Neural Networks and Deep Learning Basics: Gradient Descent
- Deep Neural Networks and Deep Learning Basics: Backpropagation
- Deep Neural Networks and Deep Learning Basics: Training DNN Animation
- Deep Neural Networks and Deep Learning Basics: Weight Initialization
- Deep Neural Networks and Deep Learning Basics: Batch Minibatch Stochastic
- Deep Neural Networks and Deep Learning Basics: Batch Normalization
- Deep Neural Networks and Deep Learning Basics: Rprop Momentum
- Deep Neural Networks and Deep Learning Basics: convergence Animation
- Deep Neural Networks and Deep Learning Basics: Drop Out Early Stopping Hyperparameters
- Python for Data Science: Python Packages for Data Science
- Python for Data Science: NumPy Pandas and Matplotlib (Part 1)
- Python for Data Science: NumPy Pandas and Matplotlib (Part 2)
- Python for Data Science: NumPy Pandas and Matplotlib (Part 3)
- Python for Data Science: NumPy Pandas and Matplotlib (Part 4)
- Python for Data Science: NumPy Pandas and Matplotlib (Part 5)
- Python for Data Science: NumPy Pandas and Matplotlib (Part 6)
- Python for Data Science: Dataset Preprocessing
- Python for Data Science: TensorFlow for classification
- Implementation of DNN for COVID 19 Analysis: COVID19 Data Analysis
- Implementation of DNN for COVID 19 Analysis: COVID19 Regression with TensorFlow
Deep learning: Convolutional Neural Networks with Python
- Introduction: Why CNN
- Introduction: Focus of the Course
- Image Processing: Grayscale Images
- Image Processing: RGB Images
- Image Processing: Reading and Showing Images in Python
- Image Processing: Converting an Image to Grayscale in Python
- Image Processing: Image Formation
- Image Processing: Image Blurring 1
- Image Processing: Image Blurring 2
- Image Processing: General Image Filtering
- Image Processing: Convolution
- Image Processing: Edge Detection
- Image Processing: Image Sharpening
- Image Processing: Implementation of Image Blurring Edge Detection Image Sharpening in Python
- Image Processing: Parametric Shape Detection
- Image Processing: Image Processing Activity
- Object Detection: Introduction to Object Detection
- Object Detection: Classification Pipeline
- Object Detection: Sliding Window Implementation
- Object Detection: Shift Scale Rotation Invariance
- Object Detection: Person Detection
- Object Detection: HOG Features
- Object Detection: Hand Engineering Versus CNNs
- Object Detection: Object Detection Activity
- Deep Neural Network Architecture: Convolution Revisited
- Deep Neural Network Architecture: Implementing Convolution in Python Revisited
- Deep Neural Network Architecture: Why Convolution
- Deep Neural Network Architecture: Filters Padding Strides
- Deep Neural Network Architecture: Pooling Tensors
- Deep Neural Network Architecture: CNN Example
- Deep Neural Network Architecture: Convolution and Pooling Details
- Deep Neural Network Architecture: Nonvectorized Implementations of Conv2d and Pool2d
- Deep Neural Network Architecture Activity
- Gradient Descent in CNNs: Example Setup
- Gradient Descent in CNNs: Why Derivatives
- Gradient Descent in CNNs: What is Chain Rule
- Gradient Descent in CNNs: Applying Chain Rule
- Gradient Descent in CNNs: Gradients of Convolutional Layer
- Gradient Descent in CNNs: Extending to Multiple Filters
- Gradient Descent in CNNs: Gradients of MaxPooling Layer
- Gradient Descent in CNNs: Extending to Multiple Layers
- Gradient Descent in CNNs: Implementation in NumPy ForwardPass.mp4.
- Gradient Descent in CNNs: Implementation in NumPy BackwardPass 1
- Gradient Descent in CNNs: Implementation in NumPy BackwardPass 2
- Gradient Descent in CNNs: Implementation in NumPy BackwardPass 3
- Gradient Descent in CNNs: Implementation in NumPy BackwardPass 4
- Gradient Descent in CNNs: Implementation in NumPy BackwardPass 5
- Gradient Descent in CNNs: Gradient Descent in CNNs Activity
- Introduction to TensorFlow: Introduction
- Introduction to TensorFlow: FashionMNIST Example Plan Neural Network
- Introduction to TensorFlow: FashionMNIST Example CNN
- Introduction to TensorFlow: Introduction to TensorFlow Activity
- Classical CNNs: LeNet
- Classical CNNs: AlexNet
- Classical CNNs: VGG
- Classical CNNs: InceptionNet
- Classical CNNs: Google Net
- Classical CNNs: Resnet
- Classical CNNs: Classical CNNs Activity
- Transfer Learning: What is Transfer learning
- Transfer Learning: Why Transfer Learning
- Transfer Learning: ImageNet Challenge
- Transfer Learning: Practical Tips
- Transfer Learning: Project in TensorFlow
- Transfer Learning: Transfer Learning Activity
- Yolo: Image Classification Revisited
- Yolo: Sliding Window Object Localization
- Yolo: Sliding Window Efficient Implementation
- Yolo: Yolo Introduction
- Yolo: Yolo Training Data Generation
- Yolo: Yolo Anchor Boxes
- Yolo: Yolo Algorithm
- Yolo: Yolo Non-Maxima Suppression
- Yolo: RCNN
- Yolo: Yolo Activity
- Face Verification: Problem Setup
- Face Verification: Project Implementation
- Face Verification: Face Verification Activity
- Neural Style Transfer: Problem Setup
- Neural Style Transfer: Implementation TensorFlow Hub
Deep learning: Recurrent Neural Networks with Python
- Introduction
- Applications of RNN (Motivation): Human Activity Recognition
- Applications of RNN (Motivation): Image Captioning
- Applications of RNN (Motivation): Machine Translation
- Applications of RNN (Motivation): Speech Recognition
- Applications of RNN (Motivation): Stock Price Predictions
- Applications of RNN (Motivation): When to Model RNN
- Applications of RNN (Motivation): Activity
- RNN Architecture: Introduction to Module
- RNN Architecture: Fixed Length Memory Model
- RNN Architecture: Fixed Length Memory Model Exercise
- RNN Architecture: Fixed Length Memory Model Exercise Solution Part 01
- RNN Architecture: Fixed Length Memory Model Exercise Solution Part 02
- RNN Architecture: Infinite Memory Architecture
- RNN Architecture: Infinite Memory Architecture Exercise
- RNN Architecture: Infinite Memory Architecture Solution
- RNN Architecture: Weight Sharing
- RNN Architecture: Notations
- RNN Architecture: ManyToMany Model
- RNN Architecture: ManyToMany Model Exercise 01
- RNN Architecture: ManyToMany Model Solution 01
- RNN Architecture: ManyToMany Model Exercise 02
- RNN Architecture: ManyToMany Model Solution 02
- RNN Architecture: ManyToOne Model
- RNN Architecture: ManyToOne Model Exercise
- RNN Architecture: ManyToOne Model Solution
- RNN Architecture: OneToMany Model
- RNN Architecture: OneToMany Model Exercise
- RNN Architecture: OneToMany Model Solution
- RNN Architecture: Activity Many to One
- RNN Architecture: Activity Many to One Exercise
- RNN Architecture: Activity Many to One Solution
- RNN Architecture: ManyToMany Different Sizes Model
- RNN Architecture: Activity Many to Many Nmt
- RNN Architecture: Models Summary
- RNN Architecture: Deep RNNs
- RNN Architecture: Deep RNNs Exercise
- RNN Architecture: Deep RNNs Solution
- Gradient Descent in RNN: Introduction to Gradient Descent Module
- Gradient Descent in RNN: Example Setup
- Gradient Descent in RNN: Equations
- Gradient Descent in RNN: Equations Exercise
- Gradient Descent in RNN: Equations Solution
- Gradient Descent in RNN: Loss Function
- Gradient Descent in RNN: Why Gradients
- Gradient Descent in RNN: Why Gradients Exercise
- Gradient Descent in RNN: Why Gradients Solution
- Gradient Descent in RNN: Chain Rule
- Gradient Descent in RNN: Chain Rule in Action
- Gradient Descent in RNN: Backpropagation Through Time
- Gradient Descent in RNN: Activity
- RNN Implementation: Automatic Differentiation
- RNN Implementation: Automatic Differentiation PyTorch
- RNN Implementation: Language Modelling Next Word Prediction Vocabulary Index
- RNN Implementation: Language Modelling Next Word Prediction Vocabulary Index Embeddings
- RNN Implementation: Language Modelling Next Word Prediction RNN Architecture
- RNN Implementation: Language Modelling Next Word Prediction Python 1
- RNN Implementation: Language Modelling Next Word Prediction Python 2
- RNN Implementation: Language Modelling Next Word Prediction Python 3
- RNN Implementation: Language Modelling Next Word Prediction Python 4
- RNN Implementation: Language Modelling Next Word Prediction Python 5
- RNN Implementation: Language Modelling Next Word Prediction Python 6
- Sentiment Classification using RNN: Vocabulary Implementation
- Sentiment Classification using RNN: Vocabulary Implementation Helpers
- Sentiment Classification using RNN: Vocabulary Implementation from File
- Sentiment Classification using RNN: Vectorizer
- Sentiment Classification using RNN: RNN Setup 1
- Sentiment Classification using RNN: RNN Setup 2
- Sentiment Classification using RNN: What Next
- Vanishing Gradients in RNN: Introduction to Better RNNs Module
- Vanishing Gradients in RNN: Introduction Vanishing Gradients in RNN
- Vanishing Gradients in RNN: GRU
- Vanishing Gradients in RNN: GRU Optional
- Vanishing Gradients in RNN: LSTM
- Vanishing Gradients in RNN: LSTM Optional
- Vanishing Gradients in RNN: Bidirectional RNN
- Vanishing Gradients in RNN: Attention Model
- Vanishing Gradients in RNN: Attention Model Optional
- TensorFlow: Introduction to TensorFlow
- TensorFlow: TensorFlow Text Classification Example using RNN
- Project I_ Book Writer: Introduction
- Project I_ Book Writer: Data Mapping
- Project I_ Book Writer: Modelling RNN Architecture
- Project I_ Book Writer: Modelling RNN Model in TensorFlow
- Project I_ Book Writer: Modelling RNN Model Training
- Project I_ Book Writer: Modelling RNN Model Text Generation
- Project I_ Book Writer: Activity
- Project II_ Stock Price Prediction: Problem Statement
- Project II_ Stock Price Prediction: Dataset
- Project II_ Stock Price Prediction: Data Preparation
- Project II_ Stock Price Prediction: RNN Model Training and Evaluation
- Project II_ Stock Price Prediction: Activity
- Further Readings and Resources: Further Readings and Resources
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