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What you'll learn?
- Theory, Maths and Implementation of machine learning and deep learning algorithms.
- Regression Analysis
- Classification Models used in classical Machine Learning such as Logistic Regression, KNN, Support Vector Machines, Decision Trees, Random Forest, and Boosting Methods in Machine Learning.
- Build Artificial Neural Networks and use them for Regression and Classification Problems.
- Using GPU with Deep Learning Models.
- Convolutional Neural Networks
- Transfer Learning
- Recurrent Neural Networks
- Time series forecasting and classification
- Autoencoders
- Generative Adversarial Networks (GANs)
- Python from scratch
- Numpy, Matplotlib, Seaborn, Pandas, PyTorch, Scikit-learn and other Python libraries.
Course Overview
Introduction
Introduction of the Course
Introduction to Machine Learning and Deep Learning
Introduction to Google Colab
Python Crash Course
Data Preprocessing
Supervised Machine Learning
Regression Analysis
Logistic Regression
K-Nearest Neighbor (KNN)
Bayes Theorem and Naive Bayes Classifier
Support Vector Machine (SVM)
Decision Trees
Random Forest
Boosting Methods in Machine Learning
Introduction to Neural Networks and Deep Learning
Activation Functions
Loss Functions
Back Propagation
Neural Networks for Regression Analysis
Neural Networks for Classification
Dropout Regularization and Batch Normalization
Convolutional Neural Network (CNN)
Recurrent Neural Network (RNN)
Generative Adversarial Network (GAN)
Autoencoders
Unsupervised Machine Learning
K-Means Clustering
Hierarchical Clustering
Density Based Spatial Clustering Of Applications With Noise (DBSCAN)
Gaussian Mixture Model (GMM) Clustering
Principal Component Analysis (PCA)
Pre-requisites
- Gmail Account ( For Google Colab )
Target Audience
- Students in Machine Learning and Deep Learning course.
- Beginners Who want to Learn Machine Learning and Deep Learning from Scratch.
- Researchers in Artificial Intelligence
- Students and Researchers who want to develop Python Programming skills to solve Machine Learning and Deep Learning Tasks.
Curriculum 306 Lectures
-
Section 1 : Introduction of the course
- Lecture 2 :
- Course Material
-
Section 2 : Introduction to Machine Learning and Deep Learning
- Lecture 1 :
- Introduction of the Section
- Lecture 2 :
- What is Intelligence ?
- Lecture 3 :
- Machine Learning
- Lecture 4 :
- Supervised Machine Learning
- Lecture 5 :
- Unsupervised Machine Learning
- Lecture 6 :
- Deep Learning
-
Section 3 : Introduction to Google Colab
- Lecture 1 :
- Introduction of the Section
- Lecture 2 :
- Importing Dataset in Google Colab
- Lecture 3 :
- Importing and Displaying Image in Google Colab
- Lecture 4 :
- Importing More Datasets
- Lecture 5 :
- Uploading Course Material on Your Google Drive
-
Section 4 : Python Crash Course
- Lecture 1 :
- Introduction of the Section
- Lecture 2 :
- Arithmetic With Python
- Lecture 3 :
- Comparison and Logical Operations
- Lecture 4 :
- Conditional Statements
- Lecture 5 :
- Dealing With Arrays Part-01
- Lecture 6 :
- Dealing With Arrays Part-02
- Lecture 7 :
- Dealing With Arrays Part-03
- Lecture 8 :
- Plotting and Visualization Part-01
- Lecture 9 :
- Plotting and Visualization Part-02
- Lecture 10 :
- Plotting and Visualization Part-03
- Lecture 11 :
- Plotting and Visualization Part-04
- Lecture 12 :
- Lists In Python
- Lecture 13 :
- For Loops Part-01
- Lecture 14 :
- For Loops Part-02
- Lecture 15 :
- Strings in Python
- Lecture 16 :
- Print formatting With Strings
- Lecture 17 :
- Dictionaries Part-01
- Lecture 18 :
- Dictionaries Part-02
- Lecture 19 :
- Functions in Python Part-01
- Lecture 20 :
- Functions in Python Part-02
- Lecture 21 :
- Pandas Part-01
- Lecture 22 :
- Pandas Part-02
- Lecture 23 :
- Pandas Part-03
- Lecture 24 :
- Pandas Part-04
- Lecture 25 :
- Seaborn Part-01
- Lecture 26 :
- Seaborn Part-02
- Lecture 27 :
- Seaborn Part-03
- Lecture 28 :
- Tuples
- Lecture 29 :
- Classes in Python
-
Section 5 : Data Preprocessing
- Lecture 1 :
- Introduction of the Section
- Lecture 2 :
- Need Of Data Preprocessing
- Lecture 3 :
- Data Normalization and Min-Max Scaling
- Lecture 4 :
- Project01-Data Normalization and Min-Max Scaling Part-01
- Lecture 5 :
- Project01-Data Normalization and Min-Max Scaling Part-02
- Lecture 6 :
- Data Standardization
- Lecture 7 :
- Project02-Data Standardization
- Lecture 8 :
- Project03-Dealing With Missing Values
- Lecture 9 :
- Project04-Dealing With Categorical Features
- Lecture 10 :
- Project05-Feature Engineering
- Lecture 11 :
- Project06-Feature Engineering by Window Method
-
Section 6 : Supervised Machine Learning
- Lecture 1 :
- Supervised Machine Learning
-
Section 7 : Regression Analysis
- Lecture 1 :
- Introduction of the Section
- Lecture 2 :
- Origin of the Regression
- Lecture 3 :
- Definition of Regression
- Lecture 4 :
- Requirements from Regression
- Lecture 5 :
- Simple Linear Regression
- Lecture 6 :
- Multiple Linear Regression
- Lecture 7 :
- Target and Predicted Values
- Lecture 8 :
- Loss Function
- Lecture 9 :
- Regression With Least Square Method
- Lecture 10 :
- Least Square Method With Numerical Example
- Lecture 11 :
- Evaluation Metrics For Regression
- Lecture 12 :
- Project01-Simple Regression-Part01
- Lecture 13 :
- Project01-Simple Regression-Part02
- Lecture 14 :
- Project01-Simple Regression-Part03
- Lecture 15 :
- Project02-Multiple Regression-Part01
- Lecture 16 :
- Project02-Multiple Regression-Part02
- Lecture 17 :
- Project02-Multiple Regression-Part03
- Lecture 18 :
- Project03-Another Multiple Regression
- Lecture 19 :
- Regression By Gradient Descent
- Lecture 20 :
- Project04-Simple Regression With Gradient Descent
- Lecture 21 :
- Project05-Multiple Regression With Gradient Descent
- Lecture 22 :
- Polynomial Regression
- Lecture 23 :
- Project06-Polynomial Regression
- Lecture 24 :
- Cross-validation
- Lecture 25 :
- Project07-Cross-validation
- Lecture 26 :
- Underfitting and Overfitting (Bias-Variance Trade off)
- Lecture 27 :
- Concept of Regularization
- Lecture 28 :
- Ridge OR L2- Regularization
- Lecture 29 :
- Lasso Regression OR L1-Regularization
- Lecture 30 :
- Comparing Ridge and Lasso Regression
- Lecture 31 :
- Elastic Net Regularization
- Lecture 32 :
- Project08-Regularizations
- Lecture 33 :
- Grid Search Cross-validation
- Lecture 34 :
- Project09-Grid Search Cross-validation
-
Section 8 : Logistic Regression
- Lecture 1 :
- Introduction of the Section
- Lecture 2 :
- Fundamentals of Logistic Regression
- Lecture 3 :
- Limitations of Regression Models
- Lecture 4 :
- Transforming Linear Regression Into Logistic Regression
- Lecture 5 :
- Project01-Getting Class Probabilities-Part01
- Lecture 6 :
- Project01-Getting Class Probabilities-Part02
- Lecture 7 :
- Loss Function
- Lecture 8 :
- Model Evaluation-Confusion Matrix
- Lecture 9 :
- Accuracy, Precision, Recall and F1-Score
- Lecture 10 :
- ROC Curves and Area Under ROC
- Lecture 11 :
- Project02-Evaluating Logistic Regression Model
- Lecture 12 :
- Project03-Cross-validation With Logistic Regression Model
- Lecture 13 :
- Project04-Multiclass Classification
- Lecture 14 :
- Project05-Classification With Challenging Dataset-Part01
- Lecture 15 :
- Project05-Classification With Challenging Dataset-Part02
- Lecture 16 :
- Project05-Classification With Challenging Dataset-Part03
- Lecture 17 :
- Grid Search Cross-validation With Logistic Regression
-
Section 9 : K-Nearest Neighbors (KNN)
- Lecture 1 :
- Introduction of the Section
- Lecture 2 :
- Intuition Behind KNN
- Lecture 3 :
- Steps of KNN Algorithm
- Lecture 4 :
- Numerical Example of KNN Algorithm
- Lecture 5 :
- Project01-KNN Algorithm-Part01
- Lecture 6 :
- Project01-KNN Algorithm-Part02
- Lecture 7 :
- Finding Optimal Value of K
- Lecture 8 :
- Project02-Implementing KNN
- Lecture 9 :
- Project03-Implementing KNN
- Lecture 10 :
- Project04-Implementing KNN
- Lecture 11 :
- Advantages and Disadvantages of KNN
-
Section 10 : Bayes Theorem and Naive Bayes Classifier
- Lecture 1 :
- Introduction of the Section
- Lecture 2 :
- Fundamentals of Probability
- Lecture 3 :
- Conditional Probability and Bayes Theorem
- Lecture 4 :
- Numerical Example on Bayes Theorem
- Lecture 5 :
- Naive Bayes Classification
- Lecture 6 :
- Comparing Naive Bayes Classification With Logistic Regression
- Lecture 7 :
- Project01-Naive Bayes as Probabilistic Classifier
- Lecture 8 :
- . Project02_Comparing Naive Bayes and Logistic Regression
- Lecture 9 :
- Project03_Multiclass Classification With Naive Bayes Classifier
-
Section 11 : Support Vector Machines (SVM)
- Lecture 1 :
- Introduction of the Section
- Lecture 2 :
- Basic Concept of SVM
- Lecture 3 :
- Maths of SVM
- Lecture 4 :
- Hard and Soft Margin Classifier
- Lecture 5 :
- Decision Rules of SVM
- Lecture 6 :
- Kernel Trick in SVM
- Lecture 7 :
- Project01-Understanding SVM-Part01
- Lecture 8 :
- Project01-Understanding SVM-Part02
- Lecture 9 :
- Project02-Multiclass Classification With SVM
- Lecture 10 :
- Project03-GridSearch CV-Part01
- Lecture 11 :
- Project03-GridSearch CV-Part02
- Lecture 12 :
- Project04-Breast Cancer Classification
-
Section 12 : Decision Tree
- Lecture 1 :
- Introduction of the Section
- Lecture 2 :
- Concept of Decision Tree
- Lecture 3 :
- Important Terms Related to Decision Tree
- Lecture 4 :
- Entropy-An information Gain Criterion
- Lecture 5 :
- Numerical Example on Entropy-Part01
- Lecture 6 :
- Numerical Example on Entropy-Part02
- Lecture 7 :
- Gini Impurity-An Information Criterion
- Lecture 8 :
- Numerical Example on Gini Impurity
- Lecture 9 :
- Project01- Decision Tree Implementation
- Lecture 10 :
- Project02- Decision Tree Implementation
- Lecture 11 :
- Project03- Grid Search CV With Decision Tree
-
Section 13 : Random Forest
- Lecture 1 :
- Introduction of the Section
- Lecture 2 :
- Why Random Forest
- Lecture 3 :
- Working of Random Forest
- Lecture 4 :
- Hyper Parameters of Random Forest
- Lecture 5 :
- Boot Strap Sampling and OOB Error
- Lecture 6 :
- Project01-Random Forest-Part01
- Lecture 7 :
- Project01-Random Forest-Part02
- Lecture 8 :
- Project02-Random Forest-Part01
- Lecture 9 :
- Project02-Random Forest-Part02
-
Section 14 : Boosting Methods in Machine Learning
- Lecture 1 :
- Introduction of the Section
- Lecture 2 :
- Adaboost ( Adaptive Boosting )
- Lecture 3 :
- Numerical Example on AdaBoost
- Lecture 4 :
- Project01-Adaboost Classifier
- Lecture 5 :
- Project02-Adaboost Classifier
- Lecture 6 :
- Gradient Boosting
- Lecture 7 :
- Numerical Example on Gradient Boosting
- Lecture 8 :
- Project03-Gradient Boosting
- Lecture 9 :
- Project03-Gradient Boosting
- Lecture 10 :
- Extreme Gradient Boosting (XG Boost)
- Lecture 11 :
- Project05-XGBoost-Part01
- Lecture 12 :
- Project05-XGBoost-Part02
-
Section 15 : Unsupervised Machine Learning
- Lecture 1 :
- Unsupervised Machine Learning
-
Section 16 : K-Means Clustering
- Lecture 1 :
- Introduction of the Section
- Lecture 2 :
- Steps of K-means Clustering
- Lecture 3 :
- Numerical Example-K-means clustering in one D
- Lecture 4 :
- Numerical Example-K-means clustering in 2D
- Lecture 5 :
- Objective Function
- Lecture 6 :
- Selecting Optimal Number of Clusters (Elbow Method)
- Lecture 7 :
- Evaluating Metric for K-means clustering
- Lecture 8 :
- Project01-K means clustering-Part01
- Lecture 9 :
- Project01-K means clustering-Part02
- Lecture 10 :
- Project01-K means clustering-Part03
- Lecture 11 :
- Project02-K means clustering
- Lecture 12 :
- Project03-K means clustering
-
Section 17 : Hierarchical Clustering
- Lecture 1 :
- Introduction of the Section
- Lecture 2 :
- Hierarchical Clustering Algorithm
- Lecture 3 :
- Hierarchical Clustering Algorithm in One D
- Lecture 4 :
- Dendrograms-Selecting Optimal Clusters-Part01
- Lecture 5 :
- Dendrograms-Selecting Optimal Clusters-Part02
- Lecture 6 :
- Hierarchical Clustering Using d-max criterion
- Lecture 7 :
- Hierarchical Clustering in 2D
- Lecture 8 :
- Evaluating Metric for Hierarchical Clustering
- Lecture 9 :
- Project01-Hierarchical Clustering-Part01
- Lecture 10 :
- Project01-Hierarchical Clustering-Part02
- Lecture 11 :
- Project02-Hierarchical Clustering
- Lecture 12 :
- Project03-Hierarchical Clustering
-
Section 18 : Density Based Spatial Clustering of Applications With Noise (DBSCAN)
- Lecture 1 :
- Introduction of the Section
- Lecture 2 :
- Definition of DBSCAN
- Lecture 3 :
- Step by Step DBSCAN
- Lecture 4 :
- Comparing DBSCAN With K-means clustering
- Lecture 5 :
- Project01-DBSCAN-Part01
- Lecture 6 :
- Project01-DBSCAN-Part02
- Lecture 7 :
- Parameters of DBSCAN
- Lecture 8 :
- Project02-DBSCAN
-
Section 19 : Gaussian Mixture Model ( GMM ) Clustering
- Lecture 1 :
- Introduction of the Section
- Lecture 2 :
- Definition of GMM Clustering
- Lecture 3 :
- Limitations of K-means Clustering
- Lecture 4 :
- Project01-GMM Clustering
- Lecture 5 :
- Project02-GMM Clustering
- Lecture 6 :
- Project03-GMM Clustering
- Lecture 7 :
- Binomial Distribution
- Lecture 8 :
- Expectation Maximization (EM) Algorithm
- Lecture 9 :
- Expectation Maximization (EM) Algorithm (Numerical Example)
-
Section 20 : Principal Component Analysis (PCA)
- Lecture 1 :
- Introduction of the Section
- Lecture 2 :
- Key Concepts of PCA
- Lecture 3 :
- Need of PCA
- Lecture 4 :
- Numerical Example on PCA
- Lecture 5 :
- Project01-PCA
- Lecture 6 :
- Project02-PCA
- Lecture 7 :
- Project03-PCA
- Lecture 8 :
- Project04-PCA
- Lecture 9 :
- Project05-PCA
- Lecture 10 :
- Project06-PCA
-
Section 21 : Deep Learning
- Lecture 1 :
- Deep Learning
-
Section 22 : Introduction to Neural Networks and Deep Learning
- Lecture 1 :
- Introduction of the section
- Lecture 2 :
- The Perceptron
- Lecture 3 :
- Features, Weight and Activation Function
- Lecture 4 :
- Learning of Neural Network
- Lecture 5 :
- Rise of Deep Learning
-
Section 23 : Activation Functions
- Lecture 1 :
- Introduction of the Section
- Lecture 2 :
- Classification by Perceptron-Part01
- Lecture 3 :
- Classification by Perceptron-Part02
- Lecture 4 :
- Need of Activation Functions
- Lecture 5 :
- Adding Activation Functions to Neural Network
- Lecture 6 :
- Sigmoid as Activation Function
- Lecture 7 :
- Hyperbolic Tangent Function
- Lecture 8 :
- ReLU and Leaky ReLU Function
-
Section 24 : Loss Functions
- Lecture 1 :
- Introduction of the Section
- Lecture 2 :
- MSE Loss Function
- Lecture 3 :
- Cross Entropy Loss Function
- Lecture 4 :
- Softmax Function
-
Section 25 : Back Propagation
- Lecture 1 :
- Introduction of the Section
- Lecture 2 :
- Forward Propagation
- Lecture 3 :
- Backward Propagation-Part01
- Lecture 4 :
- Backward Propagation-Part02
-
Section 26 : Neural Networks for Regression Analysis
- Lecture 1 :
- Introduction of the Section
- Lecture 2 :
- Project01-Neural Network for Simple Regression-Part01
- Lecture 3 :
- Project01-Neural Network for Simple Regression-Part02
- Lecture 4 :
- Project02-Neural Network for Multiple Regression
- Lecture 5 :
- Creating Neural Network Using Python Class
-
Section 27 : Neural Network for Classification
- Lecture 1 :
- Introduction of the Section
- Lecture 2 :
- Epoch, Batch size and Iteration
- Lecture 3 :
- Project00-Tensor Dataset and Data Loader
- Lecture 4 :
- Code Preparation for Iris Dataset
- Lecture 5 :
- Project01-Neural Network for Iris Data Classification
- Lecture 6 :
- Code Preparation for MNIST dataset
- Lecture 7 :
- Project02-Neural Network for MNIST data classification-Part01
- Lecture 8 :
- Project02-Neural Network for MNIST data classification-Part02
- Lecture 9 :
- Save and Load Trained Model
- Lecture 10 :
- Code Preparation for Custom Images
- Lecture 11 :
- Project03- Neural Network for Custom Images
- Lecture 12 :
- Code Preparation for Human Action Recognition
- Lecture 13 :
- Project04-Neural Network for Human Action Recognition
- Lecture 14 :
- Project05- Neural Network for Feature Engineered Dataset
-
Section 28 : Dropout Regularization and Batch Normalization
- Lecture 1 :
- Introduction of the Section
- Lecture 2 :
- Dropout Regularization
- Lecture 3 :
- Introducing Dataset for dropout Regularization
- Lecture 4 :
- Project01-Dropout Regularization
- Lecture 5 :
- Project02-Dropout Regularization
- Lecture 6 :
- Batch Normalization
- Lecture 7 :
- Project03-Batch Normalization
- Lecture 8 :
- Project03-Batch Normalization
-
Section 29 : Convolutional Neural Network (CNN)
- Lecture 1 :
- Introduction of the Section
- Lecture 2 :
- CNN Architecture and main operations
- Lecture 3 :
- 2D Convolution
- Lecture 4 :
- Shape of Feature Map After Convolution
- Lecture 5 :
- Average and Maximum Pooling
- Lecture 6 :
- Pooling to Classification
- Lecture 7 :
- Project01-CNN on MNIST-Part01
- Lecture 8 :
- Project01-CNN on MNIST-Part01
- Lecture 9 :
- An Efficient Lazy Linear Layer
- Lecture 10 :
- Project02-CNN on Custom Images
- Lecture 11 :
- Transfer Learning
- Lecture 12 :
- Project03-Transfer Learning With ResNet
- Lecture 13 :
- Project03-Transfer Learning With VGG-16
-
Section 30 : Recurrent Neural Network (RNN)
- Lecture 1 :
- Introduction of the Section
- Lecture 2 :
- Why we need RNN
- Lecture 3 :
- Sequential data
- Lecture 4 :
- ANN to RNN
- Lecture 5 :
- Back Propagation Through Time
- Lecture 6 :
- Long-Short term Memory ( LSTM )
- Lecture 7 :
- LSTM Gates
- Lecture 8 :
- Project01-LSTM Shapes
- Lecture 9 :
- Project01-LSTM Basics
- Lecture 10 :
- Batch Size, Sequence Length and Feature Dimension
- Lecture 11 :
- Project03-Interpolation and Extrapolation With LSTM
- Lecture 12 :
- Project04-Data Classification With LSTM
-
Section 31 : Autoencoders
- Lecture 1 :
- Introduction of the Section
- Lecture 2 :
- Architecture of Autoencoder
- Lecture 3 :
- Application of Autoencoder
- Lecture 4 :
- Project01-Image Denoising Using Autoencoder
- Lecture 5 :
- Project02-Occlusion Removing Using Autoencoder
- Lecture 6 :
- Project03-Autoencoder as an Image Classifier
-
Section 32 : Generative Adversarial Networks ( GANs )
- Lecture 1 :
- Introduction of the Section
- Lecture 2 :
- Discriminative and Generative Models
- Lecture 3 :
- Training of GAN
- Lecture 4 :
- Project01_GAN Implementation
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Instructor
1251 Course Views
4 Courses
Dr. Zeeshan is PhD in Electrical and Computer Engineering from Toronto Metropolitan University. He has more than 18 years of teaching and research experience. He has taught many courses related to Computer Science and Computer Engineering. such as Machine Learning, Deep Learning, Image Processing, Computer Vision, Signal Processing and Python Programming. He has publications in reputed journals and conferences.
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