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, Bayesian Learning and SVMUnit 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 Networks and Deep LearningUnit 5: Reinforcement Learning and Genetic Algorithms
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Unit tests & assignmentsयूनिट टेस्टRegular tests in the university / board pattern.
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Semester 5 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.
सिलेबस · पाठ्यक्रम
1 subject · 5 chapters · 30 topics
01Unit 1: Introduction to Machine Learning
6 टॉपिक+
- Learning and types of learning
- Well-defined learning problems and designing a learning system
- History of machine learning
- Overview of ML approaches: ANN, clustering, RL, decision trees, Bayesian networks, SVM, GA
- Issues in machine learning; data science vs machine learning
- Overfitting, underfitting and bias-variance
02Unit 2: Regression, Bayesian Learning and SVM
6 टॉपिक+
- Linear regression
- Logistic regression
- Bayes theorem, concept learning and Bayes optimal classifier
- Naive Bayes and Bayesian belief networks
- EM algorithm
- SVM: hyperplane, margin, kernels (linear, polynomial, Gaussian)
03Unit 3: Decision Trees and Instance-based Learning
6 टॉपिक+
- Decision tree learning and inductive bias
- Entropy and information gain, ID3
- Issues in decision trees: overfitting, pruning
- k-nearest neighbour learning
- Locally weighted regression and radial basis function networks
- Case-based learning
04Unit 4: Artificial Neural Networks and Deep Learning
6 टॉपिक+
- Perceptron and multilayer perceptron
- Gradient descent and delta rule
- Back-propagation
- Self-organising maps
- Deep learning and CNN: convolution, activation, pooling, fully connected layers
- Applications of CNN
05Unit 5: Reinforcement Learning and Genetic Algorithms
6 टॉपिक+
- Reinforcement learning task and examples
- Markov decision process
- Q-learning function and algorithm
- Genetic algorithm components and cycle
- Selection, crossover and mutation
- Genetic programming and applications
About this course
आप यह कर पाएँगे
- Define well-posed learning problems and compare supervised, unsupervised and reinforcement learning
- Apply linear and logistic regression, naive Bayes and support vector machines
- Build decision trees with ID3 using entropy and information gain and apply k-nearest neighbour
- Explain perceptrons, back-propagation and the layers of a convolutional neural network
- Explain Markov decision processes and Q-learning
- Explain genetic algorithms: selection, crossover and mutation
यह किसके लिए है
- B.Tech Computer Science & Engineering students of Semester 5 (AKTU)
- Students of other universities studying the same subject who want unit-wise coaching
आपको क्या चाहिए
- Engineering mathematics (linear algebra, probability)
- Python programming basics
Online tests & result
Practice and unit tests online; marks and result show in your student login and at the centre.
Student login →
सवाल
Which syllabus does the Machine Learning Techniques course follow?
It follows the AKTU (Dr. A.P.J. Abdul Kalam Technical University, Lucknow) B.Tech syllabus for Machine Learning Techniques. If your own university's syllabus differs, the centre follows your university syllabus.
Will this help in the university examination?
Yes. Teaching is unit-wise as per the AKTU scheme, with previous-year question practice, MCQ tests and doubt sessions before the semester exam.
Is coding taught in this theory elective?
Concepts and numericals are taught for the exam, and each algorithm is also demonstrated in Python with scikit-learn.
