Dr. Rambabu Pemula

  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 – 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 – III:  Supervised Learning: Classification What is Classification, General Approach to Classification, K-...

Unit – V Ensemble Learning and Reinforcement Learning 1.      Ensemble Methods: a.       Bagging b.      Boosting  c.       Random Forests 2.      Reinforcement Learning: a.       Overview b.      Example c.       Getting Lost d.      Markov Decision Process e.       Values f.       difference between SARSA and Q Learning g.      Uses of Reinforcement Learning  

  Unit – IV Unsupervised Learning and Dimensionality Reduction 1.     Types of Unsupervised Learning, 2.     Challenges in Unsupervised Learning, 3.     Clustering Algorithms: a.      K-Means, b.     Agglomerative, c.      DBSCAN, d.     Comparing and Evaluating Clustering Algorithms. 4.     Dimensionality Reduction: a.      Linear Discriminant Analysis (LDA), b.     Principal Component Analysis (PCA), c.      Factor Analysis (FA).

  Unit – III Supervised Learning: Classification 1.     What is Classification 2.     General Approach to Classification 3.     K-Nearest Neighbour Algorithm 4.     Logistic Regression 5.     Decision Trees: a.      Construction b.     classification and regression trees c.      example 6.     Naive Bayesian Classifier 7.     Support Vector Machines: a.      Optimal Separation b.     Kernels c.      Algorithm 👉 UNIT-II (A) Supervised Learning Classification Notes 👉 UNIT-III (A) Supervised Learning Classification

  Unit – II Supervised Learning: Regression 1.     Introduction to Regression 2.     Example of Regression 3.     Simple Linear Regression 4.     Multiple Linear Regression 5.     Assumptions in Regression Analysis 6.     Improving the accuracy of the Linear Regression Model 7.     Ridge Regression 8.     Lasso Regression 👉 UNIT-II Supervised Learning: Regression Notes 👉 UNIT-II Supervised Learning: Regression PPTs

  Unit – I: Introduction to Machine Learning 1.      What is Machine Learning 2.      Types of Machine Learning 3.      Applications of Machine learning 4.      Preparing to Model 5.      Modelling 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

  ESSENTIALS OF MACHINE LEARNING Course Outcomes: At the end of this course, students will be able to: 1. Understand the Concepts of Machine Learning. 2. Develop Simple Regression Models. 3. Build various classification algorithms. 4. Analyse the need of ensemble learning and dimension reduction. 5. Apply the Clustering algorithms for developing applications. Unit – I:  Introduction to Machine Learning What is Machine Learning, Types of Machine Learning, Applications of Machine learning, preparing to Model, Modelling and Evaluation 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, Lasso Regression. Unit – III:  Supervised Learning: Classification What is Classification, General Approach to Classification, K-Nearest Neighbour Algorithm, L...