III-I CSE(AIML) ESSENTIALS OF MACHINE LEARNING

-

 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-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


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. 




Comments

Post a Comment

Popular posts from this blog

VERSION SPACES AND THE CANDIDATE-ELIMINATION ALGORITHM

-
Image
  VERSION SPACES AND THE CANDIDATE-ELIMINATION ALGORITHM   •         The key idea in the CANDIDATE-ELIMINATION algorithm is to output •         a description of the set of all hypotheses consistent with the training examples Note difference between definitions of consistent and satisfies ·        An example x is said to satisfy hypothesis h when h(x) = 1, regardless of whether x is a positive or negative example of the target concept . ·        An example x is said to consistent with hypothesis h iff h(x) = c(x) The LIST-THEN-ELIMINATION algorithm The LIST-THEN-ELIMINATE algorithm first initializes the version space to contain all hypotheses in H and then eliminates any hypothesis found inconsistent with any training example . ___________________________________________________________________________ 1. VersionSpace c...
Machine Learning

Support Vector Machine (SVM) - ML Program

-
Image
  SVM Classifier for IRIS Data Set Steps: Import the library files Read the dataset (Iris Dataset) and analyze the data Preprocessing the data Divide the data into Training and Testing Build the model - SVM Classifier with different types of kernels Model Evaluation 1.Import the library files 2. Read the dataset (Iris Dataset) and analyze the data 3. Preprocessing the data 4. Divide the data into Training and Testing 5. Build the model -  SVM Classifier with different types of kernels Support vector machines (SVMs)  are a set of supervised learning methods used for  classification ,  regression  and  outliers detection . The advantages of support vector machines are: Effective in high dimensional spaces. Still effective in cases where number of dimensions is greater than the number of samples. Uses a subset of training points in the decision function (called support vectors), so it is also memory efficient. Versatile: different  Kernel functions ...
Machine Learning

Support Vector Machines

-
Image
  Predicting Date Fruit Varieties with Support Vector Machines Context Objective is to leverage advanced machine learning techniques to predict the variety of date fruits, empowering farmers and agricultural stakeholders to improve classification accuracy and streamline post-harvest processes. Your role is to analyze various morphological, colorimetric, and textural attributes of date fruits to build predictive models that distinguish different varieties effectively. Dataset Description You have been provided with a comprehensive dataset containing morphological and colorimetric features of different varieties of date fruits. The dataset includes the following attributes: Morphological Attributes: AREA:  Surface area of the date fruit. PERIMETER:  Perimeter measurement around the fruit. MAJOR_AXIS:  Length of the major axis of the date fruit. MINOR_AXIS:  Length of the minor axis of the date fruit. ECCENTRICITY:  Ratio descri...
Machine Learning