III-I CSE(AIML) ESSENTIALS OF MACHINE LEARNING

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 ESSENTIALS OF MACHINE LEARNING

👉Unit – I: Introduction to Machine Learning

What is machine learning, types of machine learning, applications of machine learning, preparing to model, modeling, and evaluation

      👉UNIT-1 (A) Introduction to Machine Learning Notes

👉UNIT-1 (A) Introduction to Machine Learning PPTs

👉UNIT-1 (B) Introduction to Machine Learning Notes

👉UNIT-1 (B) Introduction to Machine Learning PPTs

👉UNIT-1 Important Long Answer Questions

👉UNIT 1 Important Long Answer Question YouTube Link


👉Unit – II: Supervised Learning: Regression

Introduction to Regression, Example of Regression, Simple Linear Regression, Multiple Linear Regression, Assumptions in Regression Analysis, Improving the accuracy of the Linear Regression Model, Ridge Regression, and Lasso Regression.

       👉UNIT-II Supervised Learning: Regression Notes

 👉UNIT-II Supervised Learning: Regression PPTs

👉UNIT-2 Important Long Answer Questions

👉UNIT-2 Important Long Answer Questions YouTube Link



👉Unit – III: Supervised Learning: Classification

What is Classification, General Approach to Classification, K-Nearest Neighbour Algorithm, Logistic Regression, Decision Trees: Construction, Classification and Regression Trees, Example, Naive Bayesian Classifier, Support Vector Machines: Optimal Separation, Kernels, Algorithm.

         👉UNIT-II (A) Supervised Learning Classification Notes

       👉UNIT-III (A) Supervised Learning Classification PPTs

👉UNIT-3 (A) Important Long Answer Questions

👉UNIT-3 (A) Important Long Answer Questions YouTube Link 


👉Unit – IV: Unsupervised Learning and Dimensionality Reduction

Types of Unsupervised Learning, Challenges in Unsupervised Learning, Clustering Algorithms: K-Means, Agglomerative, and DBSCAN, and Comparing and Evaluating Clustering Algorithms. Dimensionality Reduction: Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA), Factor Analysis (FA).

 

👉Unit – V: Ensemble Learning and Reinforcement Learning

Ensemble Methods: Bagging, Boosting & Random Forests, Reinforcement Learning: Overview, Example: Getting Lost, Markov Decision Process, Values, Difference between SARSA and Q Learning, Uses of Reinforcement Learning

 

YouTube Links1




Text Books:

1.     Machine Learning, Saikat Dutt, Subramanian Chandramouli, and Amit Kumar Das, Pearson, 2018.

2.     Machine Learning: An Algorithmic Perspective by Stephen Marsland, CRC Press, 2009.

 

Reference Books:

1.     Introduction to Machine Learning (Adaptive Computation and Machine Learning), Ethem Alpaydin, The MIT Press 2004.

2.     Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition Aurélien Géron, 2019, O'Reilly Media, Inc. 




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