Loading course details...

Full-Stack Data Science with Generative AI - Learnees

Full-Stack Data Science with Generative AI

Have queries? Ask us

+91 9278097629

Live Interactive Classes

Full-Stack Data Science with Generative AI

This course is designed to provide a comprehensive, end-to-end understanding of data science, enhanced with the power of generative AI

Trainer :

LiveOnline ClassCourse started on:

Upto First 3 session - 100% Refund

1. Python & SQL Refresher

Topics:

Python Basics: Variables
Data Types
Loops
Conditionals
Functions
Data Structures: Lists
Tuples
Sets
Dictionaries
Object-Oriented Programming: Classes
Inheritance
Encapsulation
Exception Handling & Modules
NumPy & Pandas for data wrangling

Hands-on:

Skills You Will Learn:

2. Product Analytics

Topics:

Understanding Product Metrics: DAU MAU WAU Retention Churn
Funnels Cohort Analysis & Growth Metrics
Guesstimates & Product Thinking
Diagnostic Analysis: Event-based and Time-based Evaluation KPI Design Frameworks
Product Case Studies: Netflix Stripe Instagram Basics of A/B Testing
Data Visualization (Intro to Tableau or Power BI) for presenting product metrics

Hands-on:

Skills You Will Learn:

3. Mathematics & Statistics for ML

Topics:

Descriptive Statistics: Mean
Median
Mode
Variance
IQR
Probability Theory: Conditional Probability
Bayes Theorem
Probability Distributions: Normal
Binomial
Poisson
Central Limit Theorem & Law of Large Numbers
Inferential Statistics: Hypothesis Testing
Confidence
Intervals

Hands-on:

Skills You Will Learn:

4. Supervised Machine Learning

Topics:

Regression Models: Linear
Polynomial
Ridge
Lasso
Classification Models: Logistic Regression SVM Decision Trees
Ensemble Methods: Random Forest XGBoost Bagging Boosting
Model Validation: K-Fold Cross Validation
Performance Metrics: Confusion Matrix
ROC-AUC Precision
Recall F1 Score
Handling Imbalanced Data: Oversampling SMOTE
Hyperparameter Tuning: GridSearch Random Search
Model Interpretation using SHAP & LIME

Hands-on:

Skills You Will Learn:

5. Unsupervised Machine Learning

Topics:

Dimensionality Reduction: PCA t-SNE
Gaussian Mixture Models & Soft Clustering
Anomaly Detection using Isolation Forest Z-score
Visualization of clusters in high-dimensional space

Hands-on:

Skills You Will Learn:

6. Recommender Systems & Time Series Forecasting

Topics:

Forecasting Models: Moving Average ARIMA SARIMA
Facebook Prophet for trend prediction
Evaluation Metrics for Recommender Systems and Time Series

Hands-on:

Skills You Will Learn:

4.5

Google Reviews

4.6

G2 Reviews

4.7

Sitejabber Reviews

Choose Your Perfect Learning Schedule

Flexible batches designed to fit your lifestyle. Select the timing that works best for you.

Schedule Batches

ON DEMAND

FLEXIBLE TIMING

Online / Offline Training

Ask for convenient time

Class Type: On-Demand

SOLD OUT

SEP 29^th

Online

SAT & SUN

Dur: 6 months

19:00 PM to 22:00 PM (UTC)

Class Type: Weekend

👆 Click on any timing above to select your preferred batch

Price₹85000

₹22100.00

only

74% OFF, Save ₹62900.00

Limited time offer!

📅 Please select a batch timing to proceed with enrollment

Secure Transaction

416 Users Already Enrolled

Upto First 3 session - 100% Refund

Why Choose Full-Stack Data Science with Generative AI?

Why enroll for Full-Stack Data Science with Generative AI Certification?

Unlock your potential in cloud computing with Full-Stack Data Science with Generative AI certification

Most Popular Certification

Full-Stack Data Science with Generative AI certification is rated as one of the most popular and lucrative cloud certifications in IT globally.

Growing Market

The global Full-Stack Data Science with Generative AI market size is expected to grow at a CAGR of 21.2% from 2024 to 2030.

High Salary Potential

The estimated total pay for an Full-Stack Data Science with Generative AI is ₹7,09,000 per year, with an average salary of ₹6,09,000 per year - Glassdoor.

Neural networks

CNNs

RNNs using TensorFlow and PyTorch

Supervised and unsupervised algorithms

model evaluation

Descriptive stats

distributions

hypothesis testing

Descriptive stats

distributions

hypothesis testing

Curriculum Designed by Experts

1. Python & SQL Refresher

Topics:

Python Basics: Variables
Data Types
Loops
Conditionals
Functions
Data Structures: Lists
Tuples
Sets
Dictionaries
Object-Oriented Programming: Classes
Inheritance
Encapsulation
Exception Handling & Modules
NumPy & Pandas for data wrangling

Hands-on:

Skills You Will Learn:

2. Product Analytics

3. Mathematics & Statistics for ML

4. Supervised Machine Learning

5. Unsupervised Machine Learning

6. Recommender Systems & Time Series Forecasting

Free Career Counselling

We are happy to help you 24/7

1. Python & SQL Refresher

Topics:

Python Basics: Variables
Data Types
Loops
Conditionals
Functions
Data Structures: Lists
Tuples
Sets
Dictionaries
Object-Oriented Programming: Classes
Inheritance
Encapsulation
Exception Handling & Modules
NumPy & Pandas for data wrangling

Hands-on:

Skills You Will Learn:

2. Product Analytics

Topics:

Understanding Product Metrics: DAU MAU WAU Retention Churn
Funnels Cohort Analysis & Growth Metrics
Guesstimates & Product Thinking
Diagnostic Analysis: Event-based and Time-based Evaluation KPI Design Frameworks
Product Case Studies: Netflix Stripe Instagram Basics of A/B Testing
Data Visualization (Intro to Tableau or Power BI) for presenting product metrics

Hands-on:

Skills You Will Learn:

3. Mathematics & Statistics for ML

Topics:

Descriptive Statistics: Mean
Median
Mode
Variance
IQR
Probability Theory: Conditional Probability
Bayes Theorem
Probability Distributions: Normal
Binomial
Poisson
Central Limit Theorem & Law of Large Numbers
Inferential Statistics: Hypothesis Testing
Confidence
Intervals

Hands-on:

Skills You Will Learn:

4. Supervised Machine Learning

Topics:

Regression Models: Linear
Polynomial
Ridge
Lasso
Classification Models: Logistic Regression SVM Decision Trees
Ensemble Methods: Random Forest XGBoost Bagging Boosting
Model Validation: K-Fold Cross Validation
Performance Metrics: Confusion Matrix
ROC-AUC Precision
Recall F1 Score
Handling Imbalanced Data: Oversampling SMOTE
Hyperparameter Tuning: GridSearch Random Search
Model Interpretation using SHAP & LIME

Hands-on:

Skills You Will Learn:

5. Unsupervised Machine Learning

Topics:

Dimensionality Reduction: PCA t-SNE
Gaussian Mixture Models & Soft Clustering
Anomaly Detection using Isolation Forest Z-score
Visualization of clusters in high-dimensional space

Hands-on:

Skills You Will Learn:

6. Recommender Systems & Time Series Forecasting

Topics:

Forecasting Models: Moving Average ARIMA SARIMA
Facebook Prophet for trend prediction
Evaluation Metrics for Recommender Systems and Time Series

Hands-on:

Skills You Will Learn:

Career Opportunities

Job roles are being prepared for this course. Please check back later.

Why Choose CloudyML

Discover the unique advantages that make our learning platform stand out

24x7 Support

One-On-One Learning Assistance
Help Desk Support
Resolve Doubts in Real-time

Hands-On Project Based Learning

Industry-Relevant Projects
Course Demo Dataset & Files
Quizzes & Assignments

Industry Recognised Certification

Learnees Training Certificate
Graded Performance Certificate
Certificate of Completion

Course Overview

Everything you need to know about Full-Stack Data Science with Generative AI

The objective of the Full Stack Data Science with Generative AI course is to equip learners with a comprehensive understanding of the entire data science pipeline, from data collection and preprocessing to model deployment and monitoring. By integrating generative AI into the curriculum, the course aims to empower students to build intelligent systems capable of generating text, images, and other content using advanced techniques like transformers and diffusion models