Your roadmap · आपका रोडमैप
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Admission & first meetingप्रवेश और पहली मुलाकातEnquire at your nearest NDSS centre, meet the teacher, fix the batch time and get your study material.
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Part 1 of the syllabusपाठ्यक्रम भाग 1Unit-wise coaching with notes and doubt classes.
Unit 1: Introduction to Machine LearningUnit 2: Regression and ClassificationUnit 3: Decision Trees and Instance-based Learning
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Part 2 of the syllabusपाठ्यक्रम भाग 2Unit-wise coaching with notes and doubt classes.
Unit 4: Artificial Neural NetworksUnit 5: Unsupervised Learning and Model Evaluation
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Unit tests & assignmentsयूनिट टेस्टRegular tests in the university / board pattern.
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Semester 4 exam (university / board)विश्वविद्यालय / बोर्ड परीक्षाPrevious papers and full revision before the exam.
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Online tests & resultऑनलाइन टेस्ट और रिज़ल्टTake the tests online; your marks and result show in your student login and at the centre.
Syllabus · पाठ्यक्रम
1 subject · 5 chapters · 26 topics
01Unit 1: Introduction to Machine Learning
5 topics+
- 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 topics+
- 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 topics+
- 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 topics+
- 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 topics+
- 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
About this course
You will be able to
- 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
Who it is for
- MCA students of Semester 4 (AKTU, Lucknow)
- Students aiming for data science or ML engineer roles
What you need
- Basic statistics, probability and linear algebra
- Python programming
Online tests & result
Practice and unit tests online; marks and result show in your student login and at the centre.
Student login →
Questions
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.
