JNTUK B.Tech 3-2 R20 Machine Learning Material for all 5 units are now available. The materials provided here is prepared in an easy for read and understating.
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Unit I:
Introduction- Artificial Intelligence, Machine Learning, Deep learning, Types of Machine Learning
Systems, Main Challenges of Machine Learning.
UNIT-I Chapter-I Download Here
Statistical Learning: Introduction, Supervised and Unsupervised Learning, Training and Test Loss,
Tradeoffs in Statistical Learning, Estimating Risk Statistics, Sampling distribution of an estimator,
Empirical Risk Minimization
Unit II
Supervised Learning(Regression/Classification):Basic Methods: Distance based Methods, Nearest
Neighbours, Decision Trees, Naive Bayes
Linear Models: Linear Regression, Logistic Regression, Generalized Linear Models, Support Vector Machines
Binary Classification: Multi class/Structured outputs, MNIST, Ranking.
Unit III
Ensemble Learning and Random Forests: Introduction, Voting Classifiers, Bagging and Pasting,
Random Forests, Boosting, Stacking.
Support Vector Machine: Linear SVM Classification, Nonlinear SVM Classification SVM Regression,
Naïve Bayes Classifiers.
Unit IV
Unsupervised Learning Techniques: Clustering, K-Means, Limits of K-Means, Using Clustering for
Image Segmentation, Using Clustering for Preprocessing, Using Clustering for Semi-Supervised
Learning, DBSCAN, Gaussian Mixtures.
Dimensionality Reduction: The Curse of Dimensionality, Main Approaches for Dimensionality
Reduction, PCA, Using Scikit-Learn, Randomized PCA, Kernel PCA
Unit V
Neural Networks and Deep Learning: Introduction to Artificial Neural Networks with Keras,
Implementing MLPs with Keras, Installing TensorFlow 2, Loading and Preprocessing Data with
TensorFlow.