JNTUK R20 B.Tech CSE 3-2 Machine Learning Material/ Notes PDF Download

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JNTUK R20 B.Tech CSE 3-2 Machine Learning Material/ Notes PDF Download: Looking for comprehensive study material of JNTUK R20 B.Tech CSE 3-2 Machine Learning Material. You’re in the right place! Our extensive resources cover all aspects of Machine Learning, including identifying problems suitable for Artificial Neural Network (ANN) methods and formalizing problems using various ANN frameworks such as search problems, constraint satisfaction problems, planning problems, and Markov decision processes. With our expertly crafted notes, you’ll gain a solid understanding of Machine Learning concepts and their practical applications.

Additionally, we provide downloadable PDF versions of our materials for convenient access anytime, anywhere. Get ready to excel in your Machine Learning course with our tailored resources designed specifically for JNTUK R20 B.Tech CSE students. Download our material now and elevate your understanding of Machine Learning to new heights!

JNTUK R20 B.Tech CSE 3-2 Machine Learning Material – Units

Unit 1 Syllabus PDF Download | JNTUK R20 B.Tech CSE ML Material

Introduction: Artificial Intelligence, Machine Learning, Deep Learning, Types of Machine Learning Systems, Main Challenges of Machine Learning.

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 2 Syllabus PDF Download | JNTUK R20 B.Tech CSE ML Material

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: Multiclass/Structured outputs, MNIST, Ranking.

Unit 3 Syllabus PDF Download | JNTUK R20 B.Tech CSE ML Material

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, Naive Bayes Classifiers.

Unit 4 Syllabus PDF Download | JNTUK R20 B.Tech CSE ML Material

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 5 Syllabus PDF Download | JNTUK R20 B.Tech CSE ML Material

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

JNTUK R20 B Tech Machine Learning Material – Outcomes

• Understand the core purpose of Machine Learning systems: Explore how Machine Learning is utilized to teach computers to learn from data and make predictions or decisions without being explicitly programmed.
• Explore different regression techniques: Dive into various methods such as linear regression, polynomial regression, and logistic regression, used in Machine Learning to predict continuous outcomes or estimate relationships between variables.
• Analyze Ensemble Learning methods: Investigate the concept of Ensemble Learning, where multiple models are combined to improve prediction accuracy or robustness, including techniques like bagging, boosting, and stacking.
• Illustrate Clustering Techniques and Dimensionality Reduction Models: Examine clustering algorithms like K-means and hierarchical clustering, which group similar data points together, and dimensionality reduction methods like PCA and t-SNE, which reduce the number of features while retaining essential information.
• Discuss Neural Network Models and Deep Learning fundamentals: Delve into the basics of Neural Networks, including perceptrons, activation functions, and layers, and explore Deep Learning concepts like convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which enable computers to learn from large amounts of unstructured data.

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