AI ML & DS Expert Program in Bangalore

Month 1: Python for Data Science & AI Fundamentals

• Introduction to AI, Machine Learning, and Data Science
• Python Essentials: Data types, loops, functions, OOP
• Working with libraries: NumPy, Pandas, Matplotlib, Seaborn
• Data manipulation and exploratory data analysis (EDA)
• Data cleaning, feature engineering, missing value handling

• Data analysis projects using Pandas & Matplotlib
• Real-time data visualization (COVID-19 trends, etc.)
• Practice Python problems from LeetCode/Kaggle

• EDA on a public dataset (Kaggle or UCI) with data cleaning and insights visualization

Month 2: Applied Statistics & Data Science Concepts

• Probability Theory, Descriptive & Inferential Statistics
• Hypothesis Testing, Confidence Intervals, p-value
• Central Limit Theorem, z-tests, t-tests, Chi-square
• Correlation, Covariance, Linear Algebra for ML
• SQL for Data Science (Joins, Aggregates, Subqueries)

• Real-world hypothesis testing examples
• Mini-projects using statistical inference
• SQL queries on sample data (Sales, HR, etc.)

• Build a data story report using statistical insights from a real-world dataset

Month 3: Machine Learning with Python (Supervised + Unsupervised)

• ML pipeline: preprocessing, modeling, evaluation
• Supervised Learning: Linear/Logistic Regression, SVM, Decision Trees, Random Forest, Gradient Boosting
• Model Evaluation Metrics: Accuracy, Precision, Recall, AUC, Confusion Matrix
• Unsupervised Learning: KMeans, Hierarchical Clustering, PCA

• Train and evaluate multiple ML models
• Perform dimensionality reduction and clustering
• Hyperparameter tuning with GridSearchCV

• End-to-end ML project: Predict customer churn or house prices with model comparison

Month 4: Deep Learning & Neural Networks

• Introduction to Deep Learning & Neural Nets
• Feedforward Neural Networks, Backpropagation
• Activation Functions, Optimizers, Loss Functions
• CNNs for Image Classification, RNNs for Sequence Modeling
• Introduction to TensorFlow, Keras, PyTorch (focus on TensorFlow)

• Build a digit recognizer using MNIST dataset
• Image classification using CNN (Dogs vs Cats)
• Time series prediction using RNNs or LSTM

• Deep learning project: Image classifier or sentiment analyzer with evaluation metrics

Month 5: Cloud AI + NLP + Deployment

• NLP Basics: Tokenization, Lemmatization, POS Tagging, N-grams, Word Embeddings
• Sentiment Analysis, Text Classification using Transformers
• AWS AI/ML Services: SageMaker, Rekognition, Comprehend
• Azure AI Services: Azure ML Studio, Language Services
• Model Deployment: Flask/FastAPI, Dockerize ML Model, Deploy to AWS EC2 or Azure App Service

• Build and deploy a sentiment analysis model
• Containerize and deploy AI app on AWS or Azure
• Work with AWS Rekognition and Azure Language Studio for ML APIs

• Deploy an end-to-end AI application (e.g., Sentiment Analyzer or Face Recognition Tool)

Month 6: Advanced AI, Projects, Interview & Career Prep

• Introduction to Reinforcement Learning and GANs
• Ethics in AI and ML model interpretability (SHAP, LIME)
• MLOps overview: CI/CD for ML, Model Monitoring
• Capstone Project Planning and Execution

• Build and present a full-stack AI project: Data ingestion, modeling, deployment, monitoring
• Example: AI-Powered Resume Screener, Fraud Detection System, or Healthcare Diagnosis Tool

• Resume Building: Tailored for AI/ML roles with technical keywords
• GitHub Projects Portfolio with Documentation
• 3 Mock Interviews: Coding, ML theory, System Design
• LinkedIn Profile Enhancement + Interview Strategies

• Strong foundation in Data Science, Machine Learning, Deep Learning, and AI deployment
• Hands-on experience across Python, TensorFlow, and Cloud AI platforms (AWS/Azure)
• Job readiness for roles like AI Engineer, ML Engineer, Data Scientist