
Module Introduction
- Introduction to the Course
- Defining and Learning Important Vocabulary
- Demystifying Data Science, Decision Science, AI, ML and DL
- Overview of the tools
Module 2 Basic of python
- Python Quick Start and Setting Up Python
- General Syntax
- Variables Objects and Values
- Conditionals & Loops
- Operators & Regular Expressions
- Exceptions
- Functions & Classes
- String Methods
- Containers
- File IO
- Debugging
- Web Applications development
Module 3 Machine Learning
- Introduction to Machine Learning
- Supervised Machine Learning
- Unsupervised Machine Learning
- Reinforcement learning
- Evaluating Machine Learning models
- Regularization and Hyperparameter tuning
- Ensemble Modelling
- Exploratory Data Analysis & Feature Engineering
- Approach for model development, evaluation and optimization
- Mathematical Optimization
Module 4 Student Projects Agenda
- Trainee Project use-case overview
- Defining the problem statement
- Solution blueprint development
- Explore & define the machine learning use-case
- Course Project (Initial) Agenda
- Course Project (Final) Agenda
Module 5 Leveraging ML to Related Technologies
- Blockchain
- IoT
- Multidisciplinary Research
Module 6 Deep Learning
- Convolutional Neural Network (CNN)
- Recurrent Neural Networks (RNNs)
- Long Short-Term Memory Networks (LSTMs)
- Stacked Auto-Encoders
- Deep Boltzmann Machine (DBM)
- Deep Belief Networks (DBN)
- Generative Adversarial Networks (GANs)
- Deep Reinforcement Learning
Module 7 Disseminating project results
- Disseminating project results through Research Paper
- Research methodology and Vocabulary
- Writing Research Paper
- Deploying as software application
- Trainee: Muhammad Ali
- Trainee: SAJID Ali
- Trainee: Naz Farah
- Trainee: Javid Hussain
- Trainee: Qasim Ali Khan
- Trainee: Muhammad Orangzeb
- Trainee: Muhammad Kamran Safdar
- Trainee: Ahmed Hassan Saigal
- Trainee: Shazia Saqib
- Trainee: Mudassir Shah
- Trainee: Naveed Shah
- Trainee: Muhammad Zakria