Skip to content
Data Analytics with AI – 250906
About the Course
Expand
About the Course
2 Topics
Welcome to TechVidvan
Important Note
250906 – DSMLDA Live Session
Getting Started with Data Science
Sample Lesson
Collapse
Getting Started with Data Science
7 Topics
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
Expand
Exploratory Data Analysis Unlocking Insights
13 Topics
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
Expand
Regression in Machine Learning
11 Topics
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
Expand
Classification – Regression
12 Topics
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
Notes – Popular Classification Algorithms
Data Science Interview Questions – Popular Classification Algorithms
Notes – R2 Score
Notes – Root Mean Squared Error (RMSE)
Logistic Regression
Expand
Logistic Regression
6 Topics
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
Expand
Dive into KNN
15 Topics
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
Expand
Decision Tree
14 Topics
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
Expand
Random Forest Algorithm
10 Topics
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
Expand
Unsupervised Learning
9 Topics
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 – Popular Algorithms in Unsupervised Learning
Notes – Real world use cases of Unsupervised learnering
K-means Clustering in Machine Learning
Expand
K-means Clustering in Machine Learning
11 Topics
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
Expand
Start with Deep Learning
10 Topics
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
Expand
Gen AI for Data Science 250906
2 Topics
Video – Gen AI in Data Science 250906
Video – Agentic AI in Data Science 250906
Projects
Expand
Projects
1 Topic
Data Analytics Projects
Learner Profile Form
Previous Lesson
Next Topic
Getting Started with Data Science
LMS
Data Analytics with AI – 250906
Getting Started with Data Science
Lesson Content
0% Complete
0/7 Steps
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
Previous Lesson
Back to Course
Next Topic
Fill the form to proceed
Upskill & get ready for AI-era
Please leave this field empty.
Δ
Our executives will contact you shortly
X
Have Questions?
Talk directly to the Instructor
Please leave this field empty.
Δ
Our executives will arrange a call with the Instructor shortly
X
Career Counselling
Please leave this field empty.
Δ
X
Login
Accessing this course requires a login. Please enter your credentials below!
Username or Email Address
Password
Remember Me
Signup / Login with
Google
Signup / Login with
X
Lost Your Password?
Register
Don't have an account? Register one!
Register an Account
