Dr. Rambabu Pemula

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-4 (A) Unsupervised Learning Clustering PPT

Unit – IV Unsupervised Learning          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            👉  UNIT-4 (A) Unsupervised Learning Clustering PPTs

  WEEK 9:      Write a program to implement SVM algorithm to classify the iris data set. Print both correct and wrong predictions. 👉 Student Handout 👉 Program 👉 Colab 👉 YouTube Link

  MACHINE LEARNING USING PYTHON LAB Course Outcomes: At the end of the course the student should be able to: 1.      Select data, model selection, model complexity and identify the trends. 2.      Understand a range of machine learning algorithms along with their strengths and weaknesses. 3.      Build predictive models from data and analyze their performance. List of Experiments WEEK 1 : a)      Write a program using scikit-learn to implement K-means Clustering b)      Program to calculate the entropy and the information gain c)      Program to implement perceptron. WEEK 2: ·        Implement and demonstrate the FIND-S algorithm for finding the most specific hypothesis based on a given set of training data samples. Read the training data from a .CSV file. WEEK 3: ·        Fo...

  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 Question s 👉 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 Import...

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-5 (A) Ensemble Learning PPT    👉  UNIT-5 (A) Ensemble Learning Notes 👉  UNIT-5(B) Reinforcement Learning PPTs  👉 UNIT-5 (B) Reinforcement Learning Notes 👉  Unit – V Ensemble Learning and Reinforcement Learning Important Questions  

  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 – IV Unsupervised Learning Clustering PPT 👉 Unit – IV Unsupervised Learning and Dimensionality Reduction Important Questions