Machine Learning & AI Diploma

Module 1: Data Science Kickstart

• Data Science Introduction Session 1
• Data Science Introduction Session 2
• Data Science Methodology Session 1
• Data Science Methodology Session 2
• Data Science Tools Session 1
• Data Science Tools Session 2

Module 2: Deep Learning with PyTorch

• Logistic Regression and Softmax Regression
• Shallow Neural Networks sessions 1
• Shallow Neural Networks session 2
• Deep Networks session 1
• Deep Networks session 2
• Convolutional Neural Networks (CNNs) session 1
• Convolutional Neural Networks (CNNs) session 2

Module 3: Introduction to Deep Learning & Neural Networks with Keras

• Introduction to Neural Networks and Deep Learning
• Artificial Neural Networks
• Keras and Deep Learning Libraries
• Deep Learning Models
• Deep Learning Workshop

Module 4: Deep Learning with Keras and TensorFlow

• Advanced Keras Functionalities Session 1
• Advanced Keras Functionalities Session 2
• Advanced CNNs in Keras Session 1
• Advanced CNNs in Keras Session 2
• Transformers in Keras Session 1
• Transformers in Keras Session 2
• Unsupervised Learning and Generative Models in Keras
• Introduction to Reinforcement Learning with Keras

Module 5: Introduction to Deep Learning & Neural Networks with Keras

• Introduction to Neural Networks and Deep Learning
• Artificial Neural Networks
• Keras and Deep Learning Libraries
• Deep Learning Models
• Deep Learning Workshop

Module 6: Data Science Workshop

• Project 1: Data Cleaning & Preparation
• Project 1: Exploratory Data Analysis
• Project 1: Data Visualization
• Project 1: Predictive Modeling
• Project 2:Data Preparation & Modeling
• Project 2:Dashboard Creation & Presentation

Module 7: Machine Learning with Python

• Introduction to Machine Learning Concepts and Tools
• Linear Regression: Building Predictive Models
• Advanced Regression: Non-Linear Models and Evaluation
• Introduction to Classification and Logistic Regression
• KNN and Decision Trees for Classification
• Support Vector Machines (SVM) and Linear Classifiers
• Introduction to Clustering and K-Means Algorithm
• Hierarchical Clustering and DBSCAN Techniques
• Final Exam and Project

Module 8: Data Visualization for Data Science with Python

• Introduction to Data Visualization Tools
• . Basic and Specialized Visualization Tools Session 1
• . Basic and Specialized Visualization Tools Session 2
• Advanced Visualization Techniques and Geospatial Data
• Interactive Dashboards with Plotly and Dash

Module 9: Data Analysis for Data Science with Python

• Importing and Handling Data in Python
• Data Formatting and Transformation Techniques
• Exploratory Data Analysis with Pandas and Visualization
• Building Data Pipelines and Developing Predictive Models
• Evaluating and Optimizing Machine Learning Models

Module 10: Databases and SQL for Data Science with Python

• Fundamentals of Databases and SQL Basics
• SQL Data Types, Table Creation, and Keys
• Advanced SQL Queries: Sorting, Filtering, and Aggregations
• Complex Queries: Joins and Subqueries
• Connecting Python with Databases
• Automating Data Processing with Python and SQL
• Optimizing SQL Performance and Transactions

Module 11: Python Project for Data Science Workshop

• Project Introduction and Setup
• Data Collection and Preparation
• Data Analysis and Dashboard Development

Module 12: Python for AI and Data Science

• Introduction to Python for AI and Data Science
• Control Flow in Python: Conditions, Loops, and Functions
• Introduction to Data Structures: Lists, Tuples, and Sets
• Dictionaries and Nested Data Structures in Python
• Mastering Functions in Python
• Object-Oriented Programming (OOP) in Python
• Error Handling, Code Reusability, and Python Libraries
• Introduction to Data Analysis and Manipulation in Python
• Data Cleaning, Preprocessing, and Transformation
• APIs and Data Collection

Module 13: AI Real Projects

• Project 1: Building a Simple Image Classification Model
• Project 2: Data Preprocessing and Augmentation
• Project 3: Real-time Object Detection Model
• Project 4: Text Classification using RNNs
• Project 5: GANs for Image Generation
• Project 6: Model Evaluation and Hyperparameter Tuning

Offline Courses