Learn Python for Machine Learning & Deep Learning in One Semester

Python for Machine Learning & Deep Learning In One Semester

Practical Oriented explanations of Machine Learning and Deep Learning Models With more than 80 Projects solved in NumPy, Matplotlib, Pandas, Scikit-learn, SciPy, Pytorch etc.

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Last Updated: 2024-12-02 07:12:35 English Course Type: Self Learning

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.

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)

Gmail Account ( For Google Colab )

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.

Section 1 : Introduction of the course 2 Lectures 00:00:00
Lecture 1 :
Introduction of the course Preview
Lecture 2 :
Course Material
Download the Course Material file to your hard drive. First unzip the file and then upload the Course Material Folder to your Google drive because we will use Google colab in this course to solve our projects. In Section 3 "Introduction to Google Colab" I explained how to upload Course Material Folder in Google drive. This Course Material Folder has all the python scripts and datasets used to solve projects in this course. More explanations about the Course Material Folder is provided in Section 3 of the course.
Section 2 : Introduction to Machine Learning and Deep Learning 6 Lectures 00:00:00
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 5 Lectures 00:00:00
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 29 Lectures 00:00:00
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 11 Lectures 00:00:00
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 1 Lectures 00:00:00
Lecture 1 :
Supervised Machine Learning
Section 7 : Regression Analysis 34 Lectures 00:00:00
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 17 Lectures 00:00:00
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) 11 Lectures 00:00:00
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 9 Lectures 00:00:00
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) 12 Lectures 00:00:00
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 11 Lectures 00:00:00
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 9 Lectures 00:00:00
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 12 Lectures 00:22:42
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 1 Lectures 00:01:17
Lecture 1 :
Unsupervised Machine Learning
Section 16 : K-Means Clustering 12 Lectures 01:40:08
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 12 Lectures 01:21:44
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) 8 Lectures 00:58:17
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 9 Lectures 01:21:57
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) 10 Lectures 01:28:31
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 1 Lectures 00:01:46
Lecture 1 :
Deep Learning
Section 22 : Introduction to Neural Networks and Deep Learning 5 Lectures 00:42:44
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 8 Lectures 00:48:15
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 4 Lectures 00:24:57
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 4 Lectures 00:39:34
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 5 Lectures 01:03:54
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 14 Lectures 02:36:30
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 8 Lectures 01:41:37
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) 13 Lectures 02:00:32
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) 12 Lectures 02:16:45
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 6 Lectures 00:53:10
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 ) 4 Lectures 00:33:50
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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Zeeshan Ahmad,

3777 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.