Artificial Intelligence (AI) and Machine Learning (ML) are two closely related fields of computer science that focus on creating intelligent machines and developing algorithms that can learn from data and make predictions or decisions.

• What is Artificial Intelligence (AI)
• Concepts of machine learning
• Data and machine learning
• Real world applications of machine learning
• How machine learning works
• ChatGPT and the Future of Machine Learning

Python provides a rich set of data structures and libraries for managing and processing data. This makes it a popular choice for data science and machine learning applications. In this module, participants will gain first hand experience on the essential components required for any machine learning application.

• Data and Data Types
• Deep Dive into Python
• Data Types
• Variable Operators in Python
• Data Vectors and Data Frames
• Reading and Writing Data Files to Python
• Communicating with Database via Python
• Executing SQL Using Python
• Joining Structured & Semi Structured Data with Python
• Big Data Concepts & Application of Python

One of the most commonly used libraries for data exploration is Pandas, which provides a high-performance data manipulation and analysis toolset. Pandas allows for easy data cleaning, merging, filtering, and grouping, and provides tools for creating visualizations such as histograms, scatterplots, and heatmaps.

• Bar Chart
• Pie Chart
• Trend Chart
• Histogram
• Box Plot
• Scattered Plot & Correlation
• Other Chart

Python is a powerful tool for implementing basic classification models and techniques, and with the help of ChatGPT, users can get personalized guidance and support in their machine learning journey.

• Concept of Classification
• Supervised and Unsupervised Classification
• Decision Tree Classification
• Deploying Classification Using ChatGPT x Python
• Random Forest Classification
• Naive Bayes Classification
• Support Vector Machine

Scikit-learn is a popular library for regression analysis in Python, providing algorithms such as linear regression, logistic regression, and support vector regression. Together with ChatGPT, users can identify insights and best practices on data preparation, feature engineering, and performance evaluation, enabling users to build accurate and effective regression and forecasting models.

• Concept of Regression Modelling
• Modelling Stages
• Simple linear Regression
• Multiple Linear Regression
• Deploying Regression Models Using ChatGPT x Python
• Refining the Model
• Model Validation and Prediction
• Logistic Regression

Association rule mining is a popular technique for discovering patterns and relationships in large datasets. Python provides several libraries for implementing association rule mining.

• Concepts of Association Rules
• Market Basket Analysis (MBA)
• Support, Confidence & Lift
• Other Techniques of Association
• Application of Association

K-Means clustering is a widely used technique in machine learning for grouping data into clusters based on similarity. In this module, participants will learn how to use Python and Chatgpt to receive personalized guidance on selecting the appropriate clustering algorithms and libraries for their specific tasks, and learn how to fine-tune the hyperparameters of the algorithms for optimal performance.

• Introduction to Unsupervised Learning Models
• Deploying Clustering Models using ChatGPT x Python Programming
• Cluster Analysis
• Hierarchical Clustering
• K-Means Clustering

Acquire knowledge on how to use Python and its libraries to Evaluate and improve model performance is a critical step in machine learning, as it helps to ensure that models are accurate and effective.

• Model Evaluation and Comparison
• Parameters to Evaluate the Model Accuracy
• Selection of Right Parameters for a Model

Deep learning has revolutionized the field of machine learning, enabling the development of highly accurate and complex models that can learn from large amounts of data.ChatGPT can also provide insights and best practices on data preparation, feature engineering, and evaluation metrics, enabling users to build accurate and effective deep learning models.

• Understanding Learning Representation of Data
• Fundamentals of Deep Learning
• How Deep Learning Works
• Deep Learning and Its Application
• Future of Deep Learning
• Deep Learning in the world of ChatGPT