Machine Learning (MCA)

Elective-5: supervised and unsupervised learning, regression, decision trees, neural networks, clustering and model evaluation.

6 महीनेअवधि 5Chapters26टॉपिक

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सिलेबस · पाठ्यक्रम

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01Unit 1: Introduction to Machine Learning 5 टॉपिक

What is learning; well-posed learning problems
Types of learning: supervised, unsupervised, reinforcement
Designing a learning system
Machine learning, AI and data science
Issues in machine learning: overfitting, data quality

02Unit 2: Regression and Classification 5 टॉपिक

Linear regression and least squares
Gradient descent
Logistic regression and the sigmoid function
Bayes theorem and Naive Bayes classifier
Support vector machines and kernels

03Unit 3: Decision Trees and Instance-based Learning 5 टॉपिक

Decision tree representation
Entropy and information gain; ID3 algorithm
Overfitting and pruning
k-nearest neighbour learning
Case-based reasoning

04Unit 4: Artificial Neural Networks 5 टॉपिक

Biological and artificial neurons
Perceptron and its limitations
Activation functions: step, sigmoid, ReLU
Multilayer networks and backpropagation
Introduction to deep learning and CNNs

05Unit 5: Unsupervised Learning and Model Evaluation 6 टॉपिक

k-means and hierarchical clustering
Dimensionality reduction with PCA
Train-test split and k-fold cross-validation
Confusion matrix, precision, recall and F1 score
Bias-variance trade-off; bagging, boosting and random forest
Introduction to reinforcement learning

परिचय

आप यह कर पाएँगे

Explain types of learning and design a well-posed learning problem
Build and interpret linear and logistic regression, Naive Bayes and SVM models
Construct decision trees using entropy and information gain and use k-nearest neighbours
Describe perceptrons, activation functions, backpropagation and basic deep learning
Apply k-means, hierarchical clustering and PCA to unlabelled data
Evaluate models with cross-validation, confusion matrix, precision, recall and F1 score
Explain ensemble methods and the basics of reinforcement learning

यह किसके लिए है

MCA students of Semester 4 (AKTU, Lucknow)
Students aiming for data science or ML engineer roles

आपको क्या चाहिए

Basic statistics, probability and linear algebra
Python programming

सवाल

Which syllabus does this Machine Learning course follow?

It follows the AKTU (Lucknow) MCA syllabus for Semester 4. If your own university's syllabus differs, the centre follows your university's syllabus and unit order.

This is an elective. What if my college offers a different elective?

The centre teaches the elective your college has allotted; this course covers the AKTU syllabus of this elective.

Will we code ML models?

Yes. Each algorithm is also shown in Python with scikit-learn on small data sets so students can see how it works.