Machine learning and CSPT - ACE JNTUH

 Machine learning (Room No: 2405)

 

  

 

 

 

AM502PC: MACHINE LEARNING

B.Tech. III Year I Sem. L T P C
3 0 0 3

Course Objectives:

To introduce students to the basic concepts and techniques of Machine Learning.

To have a thorough understanding of the Supervised and Unsupervised learning techniques

To study the various probability-based learning techniques

Course Outcomes:

Distinguish between, supervised, unsupervised and semi-supervised learning

Understand algorithms for building classifiers applied on datasets of non-linearly separable classes

Understand the principles of evolutionary computing algorithms

Design an ensembler to increase the classification accuracy

UNIT - I

Learning - Types of Machine Learning - Supervised Learning - The Brain and the Neuron - Design a Learning System - Perspectives and Issues in Machine Learning - Concept Learning Task - Concept Learning as Search - Finding a Maximally Specific Hypothesis - Version Spaces and the Candidate Elimination Algorithm - Linear Discriminants: - Perceptron - Linear Separability - Linear Regression.

UNIT - II

Multi-layer Perceptron- Going Forwards - Going Backwards: Back Propagation Error - Multi-layer Perceptron in Practice - Examples of using the MLP - Overview - Deriving Back-Propagation - Radial Basis Functions and Splines - Concepts - RBF Network - Curse of Dimensionality - Interpolations and Basis Functions – Support Vector Machines

UNIT - III

Learning with Trees - Decision Trees - Constructing Decision Trees - Classification and Regression Trees - Ensemble Learning - Boosting - Bagging - Different ways to Combine Classifiers - Basic Statistics - Gaussian Mixture Models - Nearest Neighbor Methods - Unsupervised Learning - K means Algorithms

UNIT - IV

Dimensionality Reduction - Linear Discriminant Analysis - Principal Component Analysis - Factor Analysis - Independent Component Analysis - Locally Linear Embedding - Isomap - Least Squares Optimization

Evolutionary Learning - Genetic algorithms - Genetic Offspring: - Genetic Operators - Using Genetic Algorithms

UNIT - V

Reinforcement Learning - Overview - Getting Lost Example

Markov Chain Monte Carlo Methods - Sampling - Proposal Distribution - Markov Chain Monte Carlo - Graphical Models - Bayesian Networks - Markov Random Fields - Hidden Markov Models - Tracking Methods

TEXT BOOKS:

1. Stephen Marsland, Machine Learning – An Algorithmic Perspective, Second Edition, Chapman and Hall/CRC Machine Learning and Pattern Recognition Series, 2014.

 

 

 

 

ML Lab programs

 

 

 

 

 

CSPT training program

 

3.  Machine Learning

 

Instructor: J. Bhargavi

3.1  Technology Overview

Machine Learning (ML) is central to modern intelligent systems across healthcare diagnostics, fraud detection, recommendation engines, and autonomous vehicles. This course builds a rigorous foundation in supervised and unsupervised learning, model evaluation, feature engineering, and cloud-based model deployment. Students use Python's scikit-learn ecosystem alongside AWS SageMaker and Google Cloud Vertex AI. The curriculum progresses from classical algorithms through gradient-boosted ensembles, NLP fundamentals, and time-series forecasting, culminating in production-ready ML pipelines with MLOps practices.

 

3.2  Industry Certifications (FAANG / Top-Tier Only)

 

#	Certification	Issuing Body	Exam Code / Notes
1	AWS Certified Machine Learning – Specialty	Amazon Web Services	MLS-C01
2	Google Cloud Professional Machine Learning Engineer	Google Cloud	Professional ML Engineer
3	Microsoft Certified: Azure Data Scientist Associate	Microsoft	DP-100
4	Google Cloud Professional Data Engineer	Google Cloud	Professional Data Engineer

 

3.3  Trending Technologies & Capstone Projects

 

Project Title	Description	Tech Stack
House Price Predictor	End-to-end regression pipeline: feature engineering, Ridge/Lasso, XGBoost, SHAP explanations, Streamlit UI	scikit-learn, XGBoost, SHAP, Streamlit
Credit Card Fraud Detector	Imbalanced classification with SMOTE, Random Forest, precision-recall optimisation	scikit-learn, imbalanced-learn, Pandas
Customer Churn Prediction	Logistic Regression & Gradient Boost; deployed as REST API on SageMaker endpoint	AWS SageMaker, Flask, Docker
Image Classifier – Vertex AI	AutoML Vision model on custom dataset; REST API endpoint; explainability dashboard	Google Vertex AI, AutoML Vision
NLP Sentiment Pipeline	TF-IDF + Logistic Regression on product reviews; BERT embedding comparison	Python, HuggingFace Transformers, AWS

 

3.4  Complete Lesson Plan  (18 Weeks × 4 Sessions × 3 Hours)

 

Wk	Sess.	Topics Covered	Hands-On / Lab Activity
1	1–4	Python ML ecosystem: NumPy, Pandas, Matplotlib, scikit-learn API, pipelines	Setup conda; full EDA on Iris & Boston datasets
2	5–8	Supervised Learning I: Linear & Logistic Regression, gradient descent, cost functions	House price regressor; spam classifier
3	9–12	Supervised Learning II: Decision Trees, Random Forests, Bagging, feature importance	Titanic survival prediction with feature analysis
4	13–16	Boosting Algorithms: AdaBoost, XGBoost, LightGBM, CatBoost, early stopping	Kaggle tabular competition starter notebook
5	17–20	Model Evaluation: cross-validation, bias-variance trade-off, ROC-AUC, PR curve	Comprehensive evaluation report for churn model
6	21–24	Unsupervised Learning: K-Means, DBSCAN, hierarchical clustering, PCA, t-SNE	Customer segmentation with Plotly 3D visualisation
7	25–28	Feature Engineering: encoding, scaling, imputation, feature selection (RFE, SHAP)	Full pipeline for credit-risk dataset
8	29–32	Hyperparameter Tuning: GridSearchCV, RandomSearch, Optuna, early stopping	Tune XGBoost to maximise F1-score
9	33–36	AWS SageMaker I: Studio setup, built-in algorithms, training jobs, S3 integration	Train XGBoost in SageMaker Studio
10	37–40	AWS SageMaker II: real-time endpoints, batch transform, A/B testing, Model Monitor	Deploy & monitor a SageMaker churn endpoint
11	41–44	AWS MLS-C01 Prep: domain-by-domain review, exam strategies, sample questions	Two full timed MLS-C01 mock exams
12	45–48	Google Vertex AI: Workbench notebooks, AutoML, custom training, Model Registry	Train AutoML image model; deploy endpoint
13	49–52	MLOps fundamentals: MLflow experiment tracking, DVC, model versioning, CI/CD	Set up MLflow; version a dataset with DVC
14	53–56	NLP Fundamentals: text preprocessing, TF-IDF, Word2Vec, GloVe, BERT basics	Sentiment analysis pipeline on Amazon reviews
15	57–60	Time-Series Forecasting: ARIMA, SARIMA, Prophet, LSTM overview	Forecast retail demand with Prophet; evaluate MAE
16	61–64	Explainability & Ethics: SHAP, LIME, fairness metrics, bias auditing	SHAP waterfall & beeswarm plots on credit model
17	65–68	Capstone Project: full ML pipeline, SageMaker or Vertex deployment, documentation	Team ML project; demo to industry evaluator
18	69–72	Capstone Presentations; MLS-C01 & GCP ML Engineer mock exams	Panel review; certification registration guidance