1. Foundations of AI
Duration: 1 Week · Program: 5 Months · 10
Modules · AI Integrated
→ Introduction to Artificial Intelligence: Understand AI
concepts, history, terminology, and real-world applications.
→ AI, Machine Learning & Data Science: Learn how these
technologies work together to build intelligent systems.
→ AI Landscape & Career Paths: Explore AI domains, industry
trends, and career opportunities.
→ Real-World AI Applications: Discover how AI is transforming
healthcare, finance, automation, and more.
→ AI Productivity Tools: Get introduced to modern AI platforms
used by developers and businesses.
2. Programming & Math for AI
→ Python Fundamentals: Learn Python syntax, programming
concepts, and essential libraries for AI.
→ Mathematics for Machine Learning: Understand linear algebra,
probability, and statistics for AI.
→ Programming Logic & Algorithms: Build problem-solving skills
using algorithms and logical thinking.
→ AI Development Environment: Set up Python, Jupyter Notebook,
and essential machine learning tools.
→ AI Coding Assistants: Accelerate coding, debugging, and
learning with AI-powered tools.
3. Introduction to Machine Learning
→ Machine Learning Fundamentals: Learn how machine learning
enables systems to learn from data and make predictions.
→ Types of Machine Learning: Explore supervised, unsupervised,
and reinforcement learning techniques.
→ Machine Learning Workflow: Understand data preparation,
model training, evaluation, and deployment.
→ Algorithm Selection: Learn how to choose the right machine
learning algorithm for different problems.
→ AI in Machine Learning: Use AI tools to simplify model
building, testing, and experimentation.
4. Data Science & Data Handling
→ Introduction to Data Science: Understand the role of data
science in artificial intelligence and machine learning.
→ Data Collection & Preparation: Learn to collect, import,
structure, and organize datasets efficiently.
→ Data Cleaning & Preprocessing: Prepare high-quality datasets
for machine learning models.
→ Exploratory Data Analysis: Analyze trends, patterns, and
relationships using EDA techniques.
→ AI in Data Science: Automate data preparation and generate
actionable insights using AI.
5. Supervised Learning – Regression
→ Supervised Learning Fundamentals: Learn how labeled data is
used to train predictive machine learning models.
→ Linear & Logistic Regression: Build regression and
classification models using real-world datasets.
→ Model Evaluation: Measure model performance using accuracy
metrics and validation techniques.
→ Regression Projects: Apply supervised learning to solve
practical business and prediction problems.
→ AI in Regression: Leverage AI tools to optimize model
building, testing, and performance evaluation.
6. Tree-Based Machine Learning
→ Decision Trees: Learn how decision trees classify data and
make accurate predictions.
→ Random Forests: Improve prediction accuracy using powerful
ensemble learning techniques.
→ Overfitting & Model Optimization: Understand pruning methods
and techniques to improve model performance.
→ Classification Projects: Build tree-based models for
real-world business and analytics applications.
→ AI in Tree-Based Models: Use AI to automate model
optimization and compare classification performance.
7. Classification Algorithms
→ Support Vector Machines & KNN: Learn powerful classification
algorithms for predictive analytics.
→ Naive Bayes Classification: Build fast probabilistic models
for real-world prediction tasks.
→ Model Comparison: Compare classifiers and choose the best
algorithm for different datasets.
→ Classification Projects: Apply multiple algorithms to solve
practical machine learning problems.
→ AI in Classification: Use AI tools to improve model
selection and prediction accuracy.
8. Unsupervised Learning
→ Unsupervised Learning Fundamentals: Discover hidden patterns
from unlabeled datasets.
→ K-Means Clustering: Group similar data points using
clustering techniques and segmentation.
→ Pattern Discovery & Insights: Identify trends, customer
segments, and meaningful relationships.
→ Clustering Projects: Solve real-world business problems
using unsupervised learning.
→ AI in Clustering: Generate insights and visualize clusters
with AI-powered analytics.
9. NLP & Neural Networks
→ Natural Language Processing: Learn how AI understands,
processes, and analyzes human language.
→ Neural Networks Fundamentals: Understand deep learning
concepts and neural network architecture.
→ CNNs & RNNs: Build deep learning models for image
recognition and sequential data.
→ Deep Learning Projects: Develop practical NLP and computer
vision applications.
→ AI in Deep Learning: Explore modern frameworks and
AI-assisted model development.
10. Advanced AI & Generative AI
→ Generative AI Fundamentals: Explore large language models,
deep learning, and generative AI concepts.
→ Prompt Engineering & LLMs: Master prompting techniques and
build AI-powered applications.
→ AI Applications & Automation: Integrate modern AI tools into
real-world business workflows.
→ Generative AI Projects: Build end-to-end AI solutions using
the latest AI technologies.
→ Future of AI: Explore emerging trends, responsible AI, and
career opportunities in artificial intelligence.
