Machine Learning

Teacher
MIT
Category:
Information Technology

Introduction

Machine learning (ML) is a type of artificial intelligence (AI) that allows computers to learn without being explicitly programmed. It involves feeding data into algorithms that can then identify patterns and make predictions on new data. Machine learning is used in a wide variety of applications, including image and speech recognition, natural language processing, and recommender systems. In this course include Introduction to Machine Learning, Regression and their type, exploratory Data Analysis (EDA),Introduction to Overfitting and underfitting, Introduction to Logistic Regression, Naive Bayes Classifier, Introduction to KNN, Introduction to SVM, Decision Tree classifier, Introduction to Ensemble learning, Unsupervised Machine Learning Algorithm, Project deployment using Flask Framework.

1. Introduction to Machine Learning

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1.Into to ML, Types of ML, label and unlabel data,Regression and classification

2. EDA

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Understanding data, handling nan values, categorical and numerical analysis, one hot encoding, label encoding, feature scaling

3. Linear Regression

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3.1 Introduction to linear regression and It’s type.
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3.2 Terminologies of linear regression for practical
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3.3 practical implementation of linear regression
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3.4 Error function and polynomial regression

4. KNN

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4. Introduction to KNN, working of knn, distance matrix, confusion matrix(recall,precision,f1score,tpr,fpr,tnr,fnr)

5. Overfitting

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5. Introduction to overfitting,underfittinf and good fit model with explation, Trande-off bias variance diagram, sweet point, idea to prevent overfitting (regularaization and crossvalidation)

6. Regularization and cross validation

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6. Explanation of Regularaization and cross validation

7. Logistical Regression

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7.1 Introduction to Logistic Regression, sigmoid function and working of logistic regression
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7.2 Logistic Regression practical

8. Decision Tree

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8. Explanation of Decision Tree and practical

9. SVM

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9.1 Introduction to svm, Terminologies of svm(support vector,decision line,hyperplane,margin) , working with non-linear data using kernel trick, c and gamma parameter
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9.2 practical of svm algorithm

10. Ensemble Learning

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10. Introduction to single learning approch and multiple learning approch, bagging and boosting techniques with practical

11. k means clustring

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11.1 Intro to clustring, intitution behind clustring, customer segemnetation, working of clustring.
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11.2 K means clustring practical

12. Association Rule

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12. Introduction and practical implementation of association rule

13 NLP

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13.1 NLP pipeline
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13. 2 NLP Text processing using nltk
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13.3 spam ham classification using naive bayes

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Enrolled: 580 students
Duration: 6 weeks
Lectures: 21
Level: Intermediate

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