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Data Analytics with AI – 250906

About the Course

Welcome to TechVidvan

Important Note

250906 – DSMLDA Live Session

Getting Started with Data Science Sample Lesson

Introduction to Data Analytics

Notes – Introduction to Data Analytics

Interview Questions – Introduction to Data Analytics

Notes – Data Analytics vs Machine Learning vs Data Science

Data Science Career Opportunities

Notes – Data Analytics Job Roles

Notes – Data Analytics Use Cases

Exploratory Data Analysis Unlocking Insights

Introduction to Exploratory Data Analysis

Notes – Exploratory Data Analysis

Interview Questions – Exploratory Data Analysis

Notes – Numerical vs Categorical Data in Data Science

Notes – Continuous vs. Discrete Data in Data Science

Notes – Feature Engineering in Data Science

Notes – Handling Missing Values in Data Science

Notes – Handling Outliers in Data Science

Notes – Univariate, Bivariate, Multivariate Analysis in Data Science

Notes – Correlation in Data Science

Notes – Data Cleaning Essentials in Data Science

Notes – Business Insights from EDA in Data Science

EDA Introduction to Machine Learning

Regression in Machine Learning

Notes – Introduction to Regression

Data Science Interview Question – Introduction to Regression

Notes – Regression Real-World Use Cases

Notes – Types of Regression Problem

Notes – Linear Regression

Data Science Interview Question – Linear Regression

Notes – Common Challenges with Regression

Notes – Linear vs Polynomial vs Ridge vs Lasso Regression

Notes – Regression Industry Applications

Regression Assignments

Classification – Regression

Regression Practical Implementation

Notes – Scikit-learn Introduction

Notes – What is Classification?

Data Science Interview Questions – What is Classification?

Notes – Regression vs. Classification

Data Science Interview Questions – Regression vs. Classification

Notes – Classification Real-World Applications

Notes – Types of Classification Problems

Data Science Interview Questions – Popular Classification Algorithms

Notes – R2 Score

Notes – Root Mean Squared Error (RMSE)

Logistic Regression

What is Logistic Regression

Notes – Logistic Regression

Data Science Interview Questions – Logistic Regression

Notes – Confusion Matrix in Classification

Notes – Precision, Recall, F1-Score in Classification

Logistic Regression Assignments

Dive into KNN

KNN in Machine Learning

Notes – What is k-NN?

Data Science Interview Questions – What is k-NN?

Notes – KNN Real-World Applications

Data Science Interview Questions – KNN Real-World Applications

Notes – How k-NN Works

Data Science Interview Questions – How k-NN Works

Notes – KNN Distance Metrics

Notes – Choosing the Right ‘k’ in KNN

Notes – Data Preparation for KNN

Notes – Categorical Features Handling in KNN

Notes – Strengths and Limitations of KNN

Notes – Evaluation Metrics in KNN

Notes – KNN Model Tuning

Notes – Cross-Validation in KNN for Optimal ‘k’

Decision Tree

Decision Tree in Machine Learning

Notes – Introduction to Decision Tree

Data Science Interview Questions – Introduction to Decision Tree

Notes – Decision Tree Real-World Use Cases

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

Notes – Decision Tree Evaluation Metrics

Notes – Decision Tree Hyperparameter Tuning

Notes – Decision Tree Cross-Validation

Random Forest Algorithm

Random Forest in ML

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

Unsupervised Learning

Introduction to Unsupervised Learning

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

K-means Clustering in Machine Learning

K-means Clustering in ML

Notes – K-means Clustering

Data Science Interview Questions – K-means Clustering

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

Notes – Elbow Method in K-means Clustering

Dimensioanlity Reduction in ML

Notes – Hierarchical Clustering

Data Science Interview Questions – Hierarchical Clustering

Notes – Agglomerative vs Divisive Approach in Hierarchical Clustering

Notes – Hierarchical Clustering Real-world Industry Use Cases

Notes – Dimensionality Reduction in Data Science

Start with Deep Learning

Deep Learning Introduction

Notes – Deep Learning vs Machine Learning

Data Science Interview Questions – Deep Learning vs Machine Learning

Notes – Deep Learning Introduction

Data Science Interview Questions – Deep Learning Introduction

Notes – Deep Learning Case Studies in Industry

Notes – Why Deep Learning?

Notes – Need for Deep Learning in Industry

Notes – Why Deep Learning is in Demand?

Notes – Key Deep Learning Terminologies

Gen AI for Data Science 250906

Video – Gen AI in Data Science 250906

Video – Agentic AI in Data Science 250906

Projects

Data Analytics Projects

Learner Profile Form

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Data Analytics with AI – 250906
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