Lecture Notes || SNS Courseware

Subject Details

Dept : AIML

Sem : 6

Regul : 2019

Faculty : Ms M Poornima Devi

phone : 7397164031

E-mail : poornima.m.aiml@snsct.org

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Lecture Notes

UNIT 1:

Introduction to Data Science

Popular Data Science Packages in Python

Advanced Functions

Data Manipulation and Analysis with Pandas

Data Visualization with Matplotlib

Random Variables & Statistical Inferences

Statistical Distributions & Hypothesis Testing

Exploratory Data Analysis

UNIT 2:

Real-world use cases of Machine Learning

Introduction to SciKit-Learn

Machine learning Life Cycle

Implement a multi-variable regression problem with the sciKit-learn library

UNIT 3:

Understanding cost function and gradient descent

K-Nearest Neighbours: - Classification and Regression

Overfitting and Underfitting

Linear Regression

Least Squares

Ridge - Lasso

Polynominal Regression

Logistic Regression: - SVM and Hyperparameter tuning

Implementing SVM using scikit-learn

UNIT 4:

Reason to evaluate models

Model Evaluation and selection methods

Precision-Recall

ROC Curves

Confusion Matrices

Regression Evaluation

Optimizing Classifiers for Different Evaluation Metrics

UNIT 5:

Naive Bayes Classifiers

Decision Tree

Training and Visualizing a Decision Tree

Entropy and The CART Training Algorithm

Random Forests

Implement Random Forest with a real-world use case and understand the basics of random forest

Boosting - AdaBoost and Gradient Boosting

Capstone Project

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