AI & Machine Learning (AI/ML)

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AI & Machine Learning (AI/ML)

Instructor admin
Last Update 18 Apr, 2025
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About The Course

This 3-month intensive AI & ML course offers in-depth training in artificial intelligence and machine learning. Designed for aspiring data scientists and AI professionals, the course covers a wide range of topics — from foundational linear algebra and Python to advanced deep learning, NLP, and generative AI techniques.

Students will gain hands-on experience in model building, evaluation, tuning, and deploying AI models using industry-standard tools like Scikit-learn, NLTK, and LangChain. A capstone project at the end of the course ensures practical exposure and portfolio-ready outcomes.

What Will You Learn?

Topics Covered:

Introduction to AI & ML

Linear Algebra for ML

Python Programming for ML

Supervised & Unsupervised Learning

Deep Learning & Neural Networks

Natural Language Processing (NLP)

Generative AI & LLMs

Capstone Project

The Course Curriculam

Methodology:
Weekly theory and hands-on coding sessions Real-world projects and use cases Capstone project with team collaboration Peer reviews and final presentation

Mode of Training:
Online & Offline Flexi and Fixed Timings Available

Instructor:
Dr. Venkat Experience: 20+ Years in IT Sector Contact: +91 991 991 9452

Expected Salary Range:
₹15 to ₹25 LPA (based on 3–4 years of experience)

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Course Information

Course level: All Levels
Instructor: admin
Duration: 2190h 131400m
Last Updated: Apr 18, 2025
tutor-icon-ribbon-o Certificate Certificate of completion
Quizze: 1

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Week 1: Introduction to Machine Learning & Linear Algebra
ML types: supervised, unsupervised, reinforcement
Traditional programming vs ML
Real-world ML applications
ML workflow
Linear Algebra: eigenvalues/vectors, matrices Activities: Python review, Jupyter setup, Scikit-learn install
Week 2: Python for Machine Learning
Python libraries: NumPy, pandas, Scikit-learn
Data manipulation, preprocessing Activities: Hands-on with data cleaning and feature scaling
Week 3: Supervised Learning - Regression
Linear and polynomial regression
Model complexity and evaluation (MSE, RMSE, R²) Activities: Implement models, compare performance
Week 4: Supervised Learning - Classification
Logistic regression
k-Nearest Neighbors (k-NN)
Evaluation: accuracy, precision, recall, F1 Activities: Model building & evaluation with real data
Week 5: Decision Trees, Ensemble Methods & SVM
Tree-based models: decision trees, random forests
Bagging, boosting, XGBoost
SVM and kernel methods Activities: Apply ML algorithms and tune hyperparameters
Week 6: Model Evaluation and Selection
Cross-validation (k-fold, stratified)
Hyperparameter tuning (Grid/Random Search)
Bias-variance tradeoff Activities: Evaluation on various models
Week 7: Unsupervised Learning - Clustering
K-means, Hierarchical clustering
Dimensionality reduction (PCA, t-SNE) Activities: Visualizing and clustering data
Week 8: Natural Language Processing (NLP)
Preprocessing (tokenization, stemming, lemmatization)
Bag-of-Words, TF-IDF, embeddings
Intro to RNNs, LSTMs, Transformers Activities: NLP projects using NLTK & spaCy
Week 9: Neural Networks & Deep Learning
Neurons, layers, activation functions
Backpropagation, gradient descent Activities: Build and test neural networks
Week 10: Introduction to Generative AI & Architectures
Gen AI, Conversational AI, LLMs
Prompt engineering, RAG, transformers Activities: Coming soon
Week 11: Building LLM Applications
LangChain workflows
Fine-tuning LLMs for real-world tasks Activities: LLM development and customization
Week 12: Capstone Project Preparation
Project proposal, topic selection, team formation Activities: Proposal presentations, data exploration
Weeks 13–14: Capstone Project & Final Presentation
Full project development
Regular mentor check-ins
Final presentations, peer review, and course wrap-up

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AI & Machine Learning (AI/ML)