Course Details

This course is appropriate for a wide range of professionals but not limited to:

• Aspiring data scientists and AI/ML enthusiasts.
• Software developers seeking to incorporate AI/ML into their applications.
• Business professionals interested in understanding the impact of AI/ML on their industries.
• Researchers or students exploring career opportunities in Artificial Intelligence and Machine Learning.

• Expert-led sessions with dynamic visual aids
• Comprehensive course manual to support practical application and reinforcement
• Interactive discussions addressing participants’ real-world projects and challenges
• Insightful case studies and proven best practices to enhance learning

By the end of this course, participants should be able to:

• Understand the core concepts of Artificial Intelligence and Machine Learning.
• Learn how machine learning algorithms work, including regression, classification, and clustering techniques.
• Gain hands-on experience with training and evaluating models using popular ML frameworks.
• Explore deep learning techniques and neural networks for solving complex tasks.
• Learn about AI applications in various industries, including healthcare, finance, and autonomous systems.
• Understand the ethical considerations and challenges in AI and ML implementations.

DAY 1

Introduction to AI and ML

• Pre Test

• Overview of AI and ML

• Definitions and Concepts
• History and Evolution of AI
• Applications of AI in Modern Society

• Types of AI

• Narrow AI vs. General AI
• Reactive Machines, Limited Memory, Theory of Mind, and Self-aware AI

• Key Terminologies in AI and ML

• Algorithms, Models, and Data
• Supervised vs. Unsupervised Learning
• Reinforcement Learning

• Tools and Libraries for AI/ML

• Python, TensorFlow, PyTorch, scikit-learn, Keras

Understanding Data in ML

• The Importance of Data in ML

• Data Collection and Preprocessing
• Types of Data (Structured vs. Unstructured)

• Data Exploration and Visualization

• Descriptive Statistics
• Visualizing Data using Matplotlib and Seaborn

• Hands-on Exercise: Exploratory Data Analysis (EDA)

• Loading datasets and performing basic analysis

DAY 2

Supervised Learning

Regression Models

• Introduction to Regression

• Linear Regression: Theory and Applications
• Polynomial Regression
• Evaluation Metrics: Mean Squared

• Hands-on Exercise: Building a Linear Regression Model

• Implementing linear regression using Python and scikit-learn

Classification Models

• Introduction to Classification

• Logistic Regression
• Decision Trees and Random Forests
• Evaluation Metrics: Accuracy, Precision, Recall, F1 Score

• Hands-on Exercise: Building a Classification Model

• Implementing a classification model using Python and scikit-learn

DAY 3

Unsupervised Learning & Clustering

Clustering Algorithms

• Introduction to Unsupervised Learning

• K-Means Clustering
• Hierarchical Clustering
• DBSCAN

• Dimensionality Reduction

• PCA (Principal Component Analysis)
• t-SNE (t-distributed Stochastic Neighbor Embedding)

Applications of Clustering

• Market Segmentation
• Image Compression
• Hands-on Exercise: Clustering with K-Means

• Implementing K-Means clustering and visualizing the results

DAY 4

Neural Networks & Deep Learning

Introduction to Neural Networks

• What is Deep Learning?

• Artificial Neural Networks (ANN)
• Anatomy of a Neuron
• Forward Propagation and Backpropagation

• Activation Functions

• Sigmoid, ReLU, Tanh

• Hands-on Exercise: Building a Simple Neural Network

• Implementing a neural network using TensorFlow/Keras

Advanced Neural Networks

• Convolutional Neural Networks (CNNs)

• Architecture and Use Cases (e.g., Image Recognition)

• Recurrent Neural Networks (RNNs)

• Applications in Time Series and Sequence Data

• Transfer Learning

• Using Pre-trained Models for New Tasks

• Hands-on Exercise: Building a CNN for Image Classification

DAY 5

Reinforcement Learning & Real-World Applications

Introduction to Reinforcement Learning

• What is Reinforcement Learning?

• Key Concepts: Agents, Actions, Rewards
• Markov Decision Processes (MDP)
• Q-Learning and Deep Q-Networks (DQN)

• Applications of Reinforcement Learning

• Game AI (e.g., AlphaGo)
• Robotics and Autonomous Systems

AI/ML in the Real World

• AI in Business and Industry

• Applications in Healthcare, Finance, Marketing, etc.

• Ethical Considerations in AI

• Bias in Algorithms
• Privacy and Security in AI Systems

• Hands-on Exercise: Applying ML in Real-world Problem Solving

• Using datasets to build a model for a real-world scenario (e.g., predicting customer churn or classifying email spam)

Final Thoughts and Q&A

• Discussion on Future Trends in AI/ML
• Resources for Further Learning

• Books, Online Courses, Research Papers

• Q&A Session

• Addressing Participant Questions and Concerns

• Post test

Pre-requisites:

Basic understanding of programming (preferably in Python) but it is not mandatory