AIDA – 260214

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Course Curriculum:

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Course Content

Lesson Content

0% Complete 0/6 Steps

Video – DS Introduction 260214

Notes – Data Science Introduction

Notes – Data Science History

Notes – Data Science Case Studies in Industry

Notes – Data Science Applications in Real World

Data Science Interview Questions – Introduction

Lesson Content

0% Complete 0/4 Steps

Video – EDA Part-1 in DS 260214

Video – EDA Part-2 in DS 260214

Notes – Exploratory Data Analysis

Interview Questions – Exploratory Data Analysis

Lesson Content

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Video – Regression Part-1 in ML 260214

Notes – Introduction to Regression

Notes – Regression Real-World Use Cases

Data Science Interview Question – Introduction to Regression

Video – Regression Projects in ML 260214260110

Regression Assignments

Lesson Content

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Video – Classification in ML Part-1 260214260110

Notes – What is Classification?

Data Science Interview Questions – What is Classification?

Notes – Classification Real-World Applications

Notes – Regression vs. Classification

Data Science Interview Questions – Regression vs. Classification

Lesson Content

0% Complete 0/4 Steps

Video – Logistic Regression Part-1 in ML 260214260110

Video – Logistic Regression Part-2 in ML 260214260110

Notes – Logistic Regression

Data Science Interview Questions – Logistic Regression

Lesson Content

0% Complete 0/6 Steps

Video – KNN in ML 260214260110

Notes – What is k-NN?

Data Science Interview Questions – What is k-NN?

Notes – How k-NN Works

Data Science Interview Questions – How k-NN Works

Notes – KNN Real-World Applications

Lesson Content

0% Complete 0/12 Steps

Video – Decision Tree in ML 260214260110

Notes – Introduction to Decision Tree

Data Science Interview Questions – Introduction to Decision Tree

Notes – Decision Tree Real-World Use Cases

Video – Decision Tree Part-2 in ML 260214260110

Notes – Types of Decision Trees

Notes – Components of a Decision Tree

Notes – Decision Tree Splitting Criteria

Notes – Gini Impurity in Decision Tree

Notes – Entropy & Information Gain in Decision Tree

Notes – Advantages and Limitations of Decision Tree

Notes – Pre-Pruning and Post-Pruning in Decision Tree

Lesson Content

0% Complete 0/10 Steps

Video – Random Forest in ML 260214260110

Notes – Introduction to Random Forest

Data Science Interview Questions – Introduction to Random Forest

Notes – Why use Random Forest over a Single Tree?

Notes – Random Forest Real-World Applications

Notes – Concept Behind Random Forest

Notes – How Random Forest Works

Notes – Random Forest Hyperparameters

Notes – Random Forest Advantages and Limitations

Notes – Random Forest Use Cases

Lesson Content

0% Complete 0/9 Steps

Video – Unsupervised Learning in ML 260214260110

Notes – What is Unsupervised Learning?

Data Science Interview Questions – What is Unsupervised Learning?

Notes – Supervised vs Unsupervised Learning

Data Science Interview Questions – Supervised vs Unsupervised Learning

Notes – Where to use Unsupervised Learning

Notes – Application of Unsupervised Learning

Notes – Real world use cases of Unsupervised learnering

Lesson Content

0% Complete 0/5 Steps

Video – K-means Clustering in ML 260214260110

Notes – K-means Clustering

Notes – K-means Clustering Real-world industry use cases

Notes – Elbow Method in K-means Clustering

Data Science Interview Questions – K-means Clustering

Lesson Content

0% Complete 0/2 Steps

Video – Dimensionality Reduction in ML 260214260110

Notes – Dimensionality Reduction in Data Science

Lesson Content

0% Complete 0/3 Steps

Video – Gen AI & Agentic AI 260214260110

Notes – Gen AI

Notes – Agentic AI