Data Science With Generative AI Course

Become a Certified Data Scientist with PW Skills and harness the power of Machine learning, NLP and Generative AI. Learn industry-relevant skills that can help you excel in the growing field of data science.

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Job Assistance

For Pro Plan

19 Apr 2025

Date of Commencement

6 Months

Duration

Recorded

Delivery Mode

Hindi

Language

About Data Science With Generative AI Course

Discover your potential by learning the latest skills, using powerful tools, and gaining practical experience that can help you in the world of data science.

Industry Professional Led Sessions

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Project Portfolio

Start building a job-ready profile with a dynamic project portfolio

Career Assistance

Prepare for interviews with guidance and opportunities to showcase skills.

Dedicated Peer Network

Build connections with like-minded learners to exchange ideas and experiences.

Learn Industry Skills

Fast-track your upskilling journey with industry skills and personalized guidance

Certification

Attain your certificate upon course completion to showcase your capabilities.

Unlock Your Potential: Exclusive Course Offerings

Industry-Oriented Curriculum
Comprehensive Learning Content
Weekend Live Sessions
Capstone Project
Practice Exercises
Assignments and Projects
Certification of Completion
Career Guidance & Interview Preparation
Email Support
Peer to Peer Networking
SME Support Session

Your Guide To Upskilling: Our Curriculum

  1. Lecture 1 : Introduction to Python
  2. Lecture 2 : Python Objects, Number & Booleans, Strings.
  3. Lecture 3 : Container Objects, Mutability Of Objects
  4. Lecture 4 : Operators
  5. Lecture 5 : python Type Conversion
  6. Lecture 6 : Conditions (If Else, If-Elif-Else)
  7. Lecture 7 : Loops (While, For)
  8. Lecture 8 : Break And Continue Statement And Range Function
  9. Lecture 9 : python Namespace
  1. Lecture 1 : Basic Data Structure In Python
  2. Lecture 2 : String Object Basics
  3. Lecture 3 : String Inbuilt Methods
  4. Lecture 4 : Splitting And Joining Strings
  5. Lecture 5 : String Format Functions
  6. Lecture 6 : List Methods
  7. Lecture 7 : List As Stack And Queues
  8. Lecture 8 : List Comprehensions
  9. Lecture 9 : Tuples, Sets & Dictionary Object Methods
  10. Lecture 10 : Dictionary Comprehensions
  11. Lecture 11 : Dictionary View Objects
  12. Lecture 12 : Functions Basics, Parameter Passing, Iterators.
  13. Lecture 13 : Generator Functions
  14. Lecture 14 : Lambda Functions
  15. Lecture 15 : Map, Reduce, Filter Functions.
  1. Lecture 1 : OOPS Basic Concepts.
  2. Lecture 2 : Creating Classes
  3. Lecture 3 : Pillars Of OOPS
  4. Lecture 4 : Inheritance
  5. Lecture 5 : Polymorphism
  6. Lecture 6 : Encapsulation
  7. Lecture 7 : Abstraction
  8. Lecture 8 : Decorator
  9. Lecture 9 : Class Methods And Static Methods
  10. Lecture 10 : Special (Magic/Dunder) Methods
  11. Lecture 11 : Property Decorators - Getters, Setters, And Deletes
  1. Lecture 1 : Working With Files
  2. Lecture 2 : Reading And Writing Files
  3. Lecture 3 : Buffered Read And Write
  4. Lecture 4 : Other File Methods.
  5. Lecture 5 : Logging, Debugger
  6. Lecture 6 : Modules And Import Statements
  7. Lecture 7 : Exceptions Handling With Try-Except
  8. Lecture 8 : Custom Exception Handling
  9. Lecture 9 : List Of General Use Exception
  10. Lecture 10 : Best Practice Exception Handling
  1. Lecture 1 : Multithreading
  2. Lecture 2 : Multiprocessing
  3. Lecture 3 : Mysql
  4. Lecture 4 : Mongo Db
  5. Lecture 5 : What Is Web Api
  6. Lecture 6 : Difference B/W Api And Web Api
  7. Lecture 7 : Rest And Soap Architecture
  8. Lecture 8 : Restful Services
  1. Lecture 1 : Numpy
  2. Lecture 2 : Pandas
  3. Lecture 3 : Matplotlib
  4. Lecture 4 : Seaborn
  5. Lecture 5 : Plotly
  6. Lecture 6 : Bokeh
  1. Lecture 1 : Introduction To Basic Statistics Terms
  2. Lecture 2 : Types Of Statistics
  3. Lecture 3 : Types Of Data
  4. Lecture 4 : Levels Of Measurement
  5. Lecture 5 : Measures Of Central Tendency
  6. Lecture 6 : Measures Of Dispersion
  7. Lecture 7 : Random Variables
  8. Lecture 8 : Set
  9. Lecture 9 : Skewness
  10. Lecture 10 : Covariance And Correlation
  1. Lecture 1 : Probability Density/Distribution Function
  2. Lecture 2 : Types Of The Probability Distribution
  3. Lecture 3 : Binomial Distribution
  4. Lecture 4 : Poisson Distribution
  5. Lecture 5 : Normal Distribution (Gaussian Distribution)
  6. Lecture 6 : Probability Density Function And Mass Function
  7. Lecture 7 : Cumulative Density Function
  8. Lecture 8 : Examples Of Normal Distribution
  9. Lecture 9 : Bernoulli Distribution
  10. Lecture 10 : Uniform Distribution
  11. Lecture 11 : Z Stats
  12. Lecture 12 : Central Limit Theorem
  13. Lecture 13 : Estimation
  14. Lecture 14 : A Hypothesis
  15. Lecture 15 : Hypothesis Testing’S Mechanism
  16. Lecture 16 : P-Value
  17. Lecture 17 : T-Stats
  18. Lecture 18 : Student T Distribution
  19. Lecture 19 : T-Stats Vs. Z-Stats: Overview
  20. Lecture 20 : When To Use A T-Tests Vs. Z-Tests
  21. Lecture 21 : Type 1 & Type 2 Error
  22. Lecture 22 : Confidence Interval(Ci)
  23. Lecture 23 : Confidence Intervals And The Margin Of Error
  24. Lecture 24 : Interpreting Confidence Levels And Confidence Intervals
  25. Lecture 25 : Chi-Square Test
  26. Lecture 26 : Chi-Square Distribution Using Python
  27. Lecture 27 : Chi-Square For Goodness Of Fit Test
  28. Lecture 28 : When To Use Which Statistical Distribution?
  29. Lecture 29 : Analysis Of Variance (Anova)
  30. Lecture 30 : Anova Three Type
  31. Lecture 31 : Partitioning Of Variance In The Anova
  32. Lecture 32 : Calculating Using Python
  33. Lecture 33 : F-Distribution
  34. Lecture 34 : F-Test (Variance Ratio Test)
  35. Lecture 35 : Determining The Values Of F
  36. Lecture 36 : F Distribution Using Python
  1. Lecture 1 : AI vs ML vs DL vs DS
  2. Lecture 2 : Supervised, Unsupervised
  3. Lecture 3 : Semi-Supervised, Reinforcement Learning
  4. Lecture 4 : Train, Test, Validation Split
  5. Lecture 5 : Overfitting, Under Fitting
  6. Lecture 6 : Bias Vs Variance
  1. Lecture 1 : Handling Missing Data
  2. Lecture 2 : Handling Imbalanced Data
  3. Lecture 3 : Up-Sampling
  4. Lecture 4 : Down-Sampling
  5. Lecture 5 : Smote
  6. Lecture 6 : Data Interpolation
  7. Lecture 7 : Handling Outliers
  8. Lecture 8 : Filter Method
  9. Lecture 9 : Wrapper Method
  10. Lecture 10 : Wrapper Method
  11. Lecture 11 : Embedded Methods
  12. Lecture 12 : Feature Scaling
  13. Lecture 13 : Standardization
  14. Lecture 14 : Mean Normalization
  15. Lecture 15 : Min-Max Scaling
  16. Lecture 16 : Unit Vector
  17. Lecture 17 : Feature Extraction
  18. Lecture 18 : Pca (Principle Component Analysis)
  19. Lecture 19 : Data Encoding
  20. Lecture 20 : Nominal Encoding
  21. Lecture 21 : One Hot Encoding
  22. Lecture 22 : One Hot Encoding With Multiple Categories
  23. Lecture 23 : Mean Encoding
  24. Lecture 24 : Ordinal Encoding
  25. Lecture 25 : Label Encoding
  26. Lecture 26 : Target Guided Ordinal Encoding
  27. Lecture 27 : Covariance
  28. Lecture 28 : Correlation Check
  29. Lecture 29 : Pearson Correlation Coefficient
  30. Lecture 30 : Spearman’S Rank Correlation
  31. Lecture 31 : Vif
  32. Lecture 32 : Feature Selection
  33. Lecture 33 : Recursive Feature Elimination
  34. Lecture 34 : Backward Elimination
  35. Lecture 35 : Forward Elimination
  1. Lecture 1 : Feature Engineering And Selection.
  2. Lecture 2 : Analyzing Movie Reviews Sentiment.
  3. Lecture 3 : Customer Segmentation And Cross Selling Suggestions.
  4. Lecture 4 : Forecasting Stock And Commodity Prices
  1. Lecture 1 : Linear Regression
  2. Lecture 2 : Gradient Descent
  3. Lecture 3 : Multiple Linear Regression
  4. Lecture 4 : Polynomial Regression
  5. Lecture 5 : R Square And Adjusted R Square
  6. Lecture 6 : Rmse , Mse, Mae Comparison
  7. Lecture 7 : Regularized Linear Models
  8. Lecture 8 : Regularized Linear Models
  9. Lecture 9 : Ridge Regression
  10. Lecture 10 : Lasso Regression
  11. Lecture 11 : Lasso Regression
  12. Lecture 12 : Elastic Net
  13. Lecture 13 : Logistics Regression In-Depth Intuition
  14. Lecture 14 : In-Depth Mathematical Intuition
  15. Lecture 15 : In-Depth Geometrical Intuition
  16. Lecture 16 : Hyper Parameter Tuning
  17. Lecture 17 : Grid Search Cv
  18. Lecture 18 : Randomize Search Cv
  19. Lecture 19 : Data Leakage
  20. Lecture 20 : Confusion Matrix
  21. Lecture 21 : Explanation Precision,Recall,F1 Score ,Roc, Auc
  22. Lecture 22 : Best Metric Selection
  23. Lecture 23 : Multiclass Classification In Lr
  1. Lecture 1 : Decision Tree Classifier
  2. Lecture 2 : In-Depth Mathematical Intuition
  3. Lecture 3 : In-Depth Geometrical Intuition
  4. Lecture 4 : Confusion Matrix
  5. Lecture 5 : Best Metric Selection
  6. Lecture 6 : Decision Tree Regressor
  7. Lecture 7 : In-Depth Mathematical Intuition
  8. Lecture 8 : In-Depth Geometrical Intuition
  9. Lecture 9 : Performance Metrics
  10. Lecture 10 : Linear Svm Classification
  11. Lecture 11 : In-Depth Mathematical Intuition
  12. Lecture 12 : In-Depth Geometrical Intuition
  13. Lecture 13 : Soft Margin Classification
  14. Lecture 14 : Nonlinear Svm Classification
  15. Lecture 15 : Polynomial Kernel
  16. Lecture 16 : Gaussian, Rbf Kernel
  17. Lecture 17 : Data Leakage
  18. Lecture 18 : Confusion Matrix
  19. Lecture 19 : Precision, Recall,F1 Score ,Roc, Auc
  20. Lecture 20 : Best Metric Selection
  21. Lecture 21 : Svm Regression
  22. Lecture 22 : In-Depth Mathematical Intuition
  23. Lecture 23 : In-Depth Geometrical Intuition
  1. Lecture 1 : Bayes Theorem
  2. Lecture 2 : Multinomial Naïve Bayes
  3. Lecture 3 : Gaussian Naïve Bayes
  4. Lecture 4 : Various Type Of Bayes Theorem And Its Intuition
  5. Lecture 5 : Confusion Matrix
  6. Lecture 6 : Best Metric Selection
  7. Lecture 7 : Definition Of Ensemble Techniques
  8. Lecture 8 : Bagging Technique
  9. Lecture 9 : Bootstrap Aggregation
  10. Lecture 10 : Random Forest (Bagging Technique)
  11. Lecture 11 : Random Forest Repressor
  12. Lecture 12 : Random Forest Classifier
  1. Lecture 1 : Boosting Technique
  2. Lecture 2 : Ada Boost
  3. Lecture 3 : Gradient Boost
  4. Lecture 4 : Xgboost
  5. Lecture 5 : Knn Classifier
  6. Lecture 6 : Knn Regressor
  7. Lecture 7 : Variants Of Knn
  8. Lecture 8 : Brute Force Knn
  9. Lecture 9 : K-Dimension Tree
  10. Lecture 10 : Ball Tree
  11. Lecture 11 : The Curse Of Dimensionality
  12. Lecture 12 : Dimensionality Reduction Technique
  13. Lecture 13 : Pca (Principle Component Analysis)
  14. Lecture 14 : Mathematics Behind Pca
  15. Lecture 15 : Scree Plots
  16. Lecture 16 : Eigen-Decomposition Approach
  17. Lecture 17 : Practicals
  1. Lecture 1 : Anomaly Detection Types
  2. Lecture 2 : Anomaly Detection Applications
  3. Lecture 3 : Isolation Forest Anomaly Detection Algorithm
  4. Lecture 4 : Density-Based Anomaly Detection (Local Outlier Factor) Algorithm
  5. Lecture 5 : Isolation Forest Anomaly Detection Algorithm
  6. Lecture 6 : Support Vector Machine Anomaly Detection Algorithm
  7. Lecture 7 : Dbscan Algorithm For Anomaly Detection
  8. Lecture 8 : What Is A Time Series?
  9. Lecture 9 : Old Techniques
  10. Lecture 10 : Arima
  11. Lecture 11 : Acf And Pacf
  12. Lecture 12 : Time-Dependent Seasonal Components.
  13. Lecture 13 : Autoregressive (Ar),
  14. Lecture 14 : Moving Average (Ma) And Mixed Arma- Modeler.
  1. Lecture 1 : Introduction to Deep Learning
  2. Lecture 2 : Neural Network Overview And Its Use Case.
  3. Lecture 3 : Detail Mathematical Explanation
  4. Lecture 4 : Various Neural Network Architect Overview.
  5. Lecture 5 : Various Neural Network Architect Overview.
  6. Lecture 6 : Use Case Of Neural Network In Nlp And Computer Vision.
  7. Lecture 7 : Activation Function -All Name
  8. Lecture 8 : Multilayer Network.
  9. Lecture 9 : Loss Functions. - All 10
  10. Lecture 10 : Forward And Backward Propagation.
  11. Lecture 11 : Optimizers. - All 10
  12. Lecture 12 : Forward And Backward Propagation.
  13. Lecture 13 : Vanishing Gradient Problem
  14. Lecture 14 : Weight Initialization Technique
  15. Lecture 15 : Exploding Gradient Problem
  16. Lecture 16 : Visualization Of Neural Network
  17. Lecture 17 : The Learning Mechanism
  1. Lecture 1 : Colab Pro Setup
  2. Lecture 2 : TensorFlow Installation 2.0 .
  3. Lecture 3 : TensorFlow 2.0 Function.
  4. Lecture 4 : TensorFlow 2.0 Neural Network Creation.
  5. Lecture 5 : Mini Project In TensorFlow.
  6. Lecture 6 : Tensor space
  7. Lecture 7 : Tensor board Integration
  8. Lecture 8 : TensorFlow Playground
  9. Lecture 9 : Netron
  10. Lecture 10 : Pytorch Installation
  11. Lecture 11 : Pytorch Functional Overview
  12. Lecture 12 : Pytorch Neural Network Creation
  1. Lecture 1 : Cnn Fundamentals
  2. Lecture 2 : Cnn Explained In Detail - Cnnexplainer, Tensor space
  3. Lecture 3 : Various Cnn Based Architecture
  4. Lecture 4 : Training Cnn From Scratch
  5. Lecture 5 : Building Webapps For Cnn
  6. Lecture 6 : Deployment In AWS
  1. Lecture 1 : Various Cnn Architecture With Research Paper And Mathematics
  2. Lecture 2 : Lenet-5 Variants With Research Paper And Practical
  3. Lecture 3 : Alexnet Variants With Research Paper And Practical
  4. Lecture 4 : Googlenet Variants With Research Paper And Practical
  5. Lecture 5 : Transfer Learning
  6. Lecture 6 : Vggnet Variants With Research Paper And Practical
  7. Lecture 7 : Resnet Variants With Research Paper And Practical
  8. Lecture 8 : Inception Net Variants With Research Paper And Practical
  1. Lecture 1 : Intro to RCNN
  2. Lecture 2 : Fast & Faster RCNN
  3. Lecture 3 : Overview of all previous version
  4. Lecture 4 : Introduction To Yolov9
  5. Lecture 5 : Installation Of Yolov9
  6. Lecture 6 : Data Annotation & Preparation
  7. Lecture 7 : Download Data & Configure Path
  8. Lecture 8 : Download & Configure Pretrained Weight
  9. Lecture 9 : Start Model Training
  10. Lecture 10 : Evaluation Curves Yolov9
  11. Lecture 11 : Inferencing Using Trained Model
  12. Lecture 12 : Evaluation Curves TFOD2
  13. Lecture 13 : Inferencing Using Trained Model
  1. Lecture 1 : Introduction To Detecron2
  2. Lecture 2 : Installation Of Detecron2
  3. Lecture 3 : Data Annotation & Preparation
  4. Lecture 4 : Download Data & Configure Path
  5. Lecture 5 : Download & Configure Pretrained Weight
  6. Lecture 6 : Start Model Training
  7. Lecture 7 : Evaluation Curves Detecron2
  8. Lecture 8 : Inferencing Using Trained Model
  1. Lecture 1 : Introduction To TFOD2
  2. Lecture 2 : Installation Of TFOD2
  3. Lecture 3 : Data Annotation & Preparation
  4. Lecture 4 : Download Data & Configure Path
  5. Lecture 5 : Download & Configure Pretrained Weight
  6. Lecture 6 : Start Model Training
  7. Lecture 7 : Evaluation Curves TFOD2
  8. Lecture 8 : Inferencing Using Trained Model
  9. Lecture 9 : Scene Understanding
  10. Lecture 10 : More To Detection
  11. Lecture 11 : Need Accurate Results
  12. Lecture 12 : Segmentation
  13. Lecture 13 : Types Of Segmentation
  14. Lecture 14 : Understanding Masks
  15. Lecture 15 : Maskrcnn
  16. Lecture 16 : From Bounding Box To Polygon Masks
  17. Lecture 17 : Mask Rcnn Architecture
  1. Lecture 1 : What Is Object Tracking?
  2. Lecture 2 : Doing Annotations Or Labeling Data
  3. Lecture 3 : Registering Dataset For Training
  4. Lecture 4 : Selection Of Pretrained Model From Model Zoo
  5. Lecture 5 : Let's Start Training
  6. Lecture 6 : Stop Training Or Resume Training
  7. Lecture 7 : Inferencing Using The Custom Trained Model In Colab
  8. Lecture 8 : Evaluating The Model
  9. Lecture 9 : Kalman Filter
  10. Lecture 10 : Data Preprocessing
  11. Lecture 11 : Using Yolo For Detection
  12. Lecture 12 : Preparing Deep sort With Yolo
  13. Lecture 13 : Combining Pipelines For Tracking & Detection
  1. Lecture 1 : Introduction To Gans
  2. Lecture 2 : Gan Architecture
  3. Lecture 3 : Discriminator
  4. Lecture 4 : Generator
  5. Lecture 5 : Controllable Generation
  6. Lecture 6 : Wgans
  7. Lecture 7 : Dcgans
  8. Lecture 8 : Stylegans
  9. Lecture 9 : Gan Practical's Implementation
  1. Lecture 1 : Overview Computational Linguistic.
  2. Lecture 2 : History Of Nlp.
  3. Lecture 3 : Why Nlp
  4. Lecture 4 : Use Of Nlp
  1. Lecture 1 : Text Processing
  2. Lecture 2 : Understanding Regex
  3. Lecture 3 : Text Normalization
  4. Lecture 4 : Word Count.
  5. Lecture 5 : Frequency Distribution
  6. Lecture 6 : String Tokenization
  7. Lecture 7 : Annotator Creation
  8. Lecture 8 : Sentence Processing
  9. Lecture 9 : Lemmatization In Text Processing
  10. Lecture 10 : Word Embedding
  11. Lecture 11 : Co-Occurrence Vectors
  12. Lecture 12 : Word2Vec
  13. Lecture 13 : Doc2Vec
  1. Lecture 1 : Nltk
  2. Lecture 2 : Text Blob
  3. Lecture 3 : Recurrent Neural Networks.
  4. Lecture 4 : Long Short Term Memory (Lstm)
  5. Lecture 5 : Bi Lstm
  6. Lecture 6 : Stacked Lstm
  7. Lecture 7 : Gru Implementation
  1. Lecture 1 : Seq 2 Seq.
  2. Lecture 2 : Encoders And Decoders.
  3. Lecture 3 : Attention Mechanism.
  4. Lecture 4 : Self-Attention
  5. Lecture 5 : Introduction To Transformers.
  6. Lecture 6 : Bert Model.
  7. Lecture 7 : Gpt2 Model.
  1. Lecture 1 : Introduction of Generative AI and its use cases
  2. Lecture 2 : Probabilistic modeling and generative models
  1. Lecture 1 : Autoencoders and variational autoencoders
  2. Lecture 2 : RNN & LSTM
  3. Lecture 3 : Generative pre-trained transformers
  1. Lecture 1 : SMT & NMT
  2. Lecture 2 : Attention mechanisms in NMT
  3. Lecture 3 : Generative pre-trained transformers
  1. Lecture 1 : Poetry generation and music composition using generative AI
  2. Lecture 2 : Creative writing and storytelling with generative AI
  3. Lecture 3 : Ethical considerations and biases in creative content generation
  1. Lecture 1 : Reinforcement learning in generative AI for NLP
  2. Lecture 2 : Multimodal generative models for NLP
  3. Lecture 3 : Generative AI for natural language understanding (NLU)
  1. Lecture 1 : Introduction to Large Language Models (LLMs)
  2. Lecture 2 : Exploring the diverse applications of LLMs
  3. Lecture 3 : Understanding the potential of LLMs to transform various industries
  1. Lecture 1 : Navigating the Hugging Face Hub and its vast repository of pre-trained models
  2. Lecture 2 : Leveraging Hugging Face Hub's tools and resources for generative AI tasks
  3. Lecture 3 : Exploring real-world applications built upon Hugging Face Hub's models
  1. Lecture 1 : Understanding the role of prompts in guiding LLMs
  2. Lecture 2 : Crafting effective prompts to generate desired outputs
  3. Lecture 3 : Exploring advanced prompt engineering techniques
  1. Lecture 1 : Introduction to the Retrieval Augmented Generation (RAG)
  2. Lecture 2 : Discuss about the frameworks to achieve RAG
  3. Lecture 3 : Utilizing RAG for various generative AI tasks
  4. Lecture 4 : Understanding the advantages and limitations of RAG
  1. Lecture 1 : Delving into the concept of fine-tuning LLMs
  2. Lecture 2 : Understanding the different approaches to fine-tuning LLMs
  3. Lecture 3 : Exploring real-world examples of fine-tuning for specific applications
  1. Lecture 1 : Exploring the OpenAI library in python
  2. Lecture 2 : Image Generation with openai library
  3. Lecture 3 : Image captioning with openai library
  1. Lecture 1 : Exploring LangChain and chainlit
  2. Lecture 2 : Creative Chatbot using LangChain and Chainlit
  1. Lecture 1 : Alexa like Assistant
  1. Lecture 1 : A custom chat bot which can create images based on the given input

Real-World Projects: Apply What You Learn

GeneAi - An Alexa Like Assistant

A customized alexa like assistant with chat and voice command compatibilities. Useful for day-to-day tasks like web searching, knowledge extraction from documents or music recommendation

Named Entity Recognition

Utilizing Transformer models, NER project accurately identifies and extracts named entities from text.

On Prompt Image & Caption Generator

Aiming at image generation and caption generation, this project helps the user to generate contents or topics and thumbnails with ease.

Customised Chat Bot

Customised chatbot with langchain and Chainlit to generate a Question Answering system or RAG system that extracts information from various sources of documents or simple web searching.

Guidance By Professionals: Our Esteemed Faculties

Experience excellence in mentorship from industry-leading professional.

Saurabh Moharikar

PhysicsWallah

  • 8+ Years

    Work Experience

  • 5+ Years

    Teaching Experience

With 13 years of expertise in Data Analytics , Saurabh is passionate towards teaching.

Ajay Kumar Gupta

  • 4+ Years

    Work Experience

  • 3+ Years

    Teaching Experience

Senior Data Scientist, Synopsys, Ex-Novartis, 4 years of experience

Saurabh Moharikar

  • 8+ Years

    Work Experience

  • 5+ Years

    Teaching Experience

With 13 years of expertise in Data Analytics , Saurabh is passionate towards teaching.

Jobandeep Singh

  • 5+ Years

    Work Experience

  • 4+ Years

    Teaching Experience

Software Engineer working on cutting-edge web technologies. With a passion to solve complex problems. Currently working as a full stack developer at Amazon working on Artifical General Intelligence Data Services Org (AGI-DS), previously I have contributing to the Microsoft Azure Backup and Disaster Recovery team.

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You will be able to generate the certificate for course of completion:

  • After watching 60% of videos
  • After completing 60% in Quiz & Assignment
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Champions of Change: Alumni Experiences

Unlock the potential within our alumni's experiences and witness the transformative power of upscaling on their careers and lives

"Lost my job during the pandemic but transitioned from sales to analytics with PW Skills help. Now a Business Analyst at Oracle with a 100% hike." .

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Business Analyst

"Data Science Consultant: Projects & networking shaped career." .

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Decision Scientist

"ML Engineer: DS Course equipped me, grateful for curriculum!" .

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Consultant

Data Science With Generative AI Course

PW Skills offers a comprehensive learning platform designed to help individuals build technical expertise and stay ahead in the evolving tech industry. Learners can explore cutting-edge technologies and gain industry-relevant skills to prepare for career opportunities in various domains. The Data Science Course provides structured training, allowing learners to understand and apply artificial intelligence in real-world scenarios.

Courses at PW Skills are accessible to anyone looking to upskill, with a blended learning format that includes recorded lessons, live sessions, doubt resolution, study materials, hands-on assessments, and certification. The focus is on practical learning and career advancement, ensuring learners receive the guidance and resources needed to excel. Enroll in PW Skills and take the next step toward a transformative educational journey that prioritizes skill development, industry relevance, and accessible learning opportunities.

Data Science Course with Gen AI by PW Skills 

Data Science is an advanced, cutting-edge technology that can easily create diverse new and original content in less time. With the help of Generative AI, you can easily create an engaging and innovative way of interacting with your audience.

At PW Skills, we integrate the power of artificial intelligence to deliver you the knowledge of the most popular cutting-edge technologies to upskill yourself. You can choose our course, data science course to learn and harness the power of AI. Learn advanced AI tools such as DALL.E, ChatGPT, Eleven Labs, Luma AI, Wonder Studio and more.

Many of our courses are now powered by artificial intelligence to help boost your productivity and learn integrative AI with tools to make it more powerful and effective. 

Our data science course offers the affordable and interactive way of learning technologies online. We provide dedicated faculty to help you throughout your learning journey. Get peer-to-peer doubt-solving sessions to help collaborative learning and track your progress while learning.

Data Science Certification Course with Gen AI

Enrol in our Data Science Certification course and get industry-recognized certifications to help you showcase your abilities and skills in front of your recruiters. Many of our courses provide you with certification after you complete your learning, along with assignments, assessments and more.

Types of Data Science Course On PW Skills

PW Skills is now offering course modules especially dedicated to individuals based on their knowledge and expertise in the field. Choose the plan which is better inclined with your goals in your career.

Basic Course 

This is a beginners program type where candidates can get outcome driven results from short duration courses for data science. This beginner friendly course has the following benefits as mentioned below.

Name of the Course PW Skills Data Science with Generative AI (Basic)
Delivery Mode Pre-Recorded Lectures
Duration  6 Months
Type/Positioning Job Readiness Course
Certification Yes
Support(Email/Dashboard doubt support/ community Channel) Yes
Live Doubt Session Every Week (Once)
Industry Based Projects Yes
Job Readiness  Yes
Certification Criteria 
  • More than 60% of score in quiz
  • More than 60% video completion

Pro Version 

The pro version provides you with complete live and recorded tutorials within the course curriculum to help you better prepare and interact throughout the course.

Name of the Course PW Skills Data Science Course(Pro)
Delivery Mode Pre-Recorded Lectures + Live 
Duration 6 Months
Type/Positioning Job Readiness Course + Live Sessions (Weekly 1 Live)
Certification Yes
Support(Email/Dashboard doubt support/ community Channel) Yes
Live Doubt Session Every Week (Once)
Industry Based Projects Yes
Job Readiness  Yes
Assignment with Written and Video Solutions Yes
Resume Building Support  Yes (Pre-Recorded)
Ace Interview Live Training  Yes
Ace Interview Practice Sessions Yes
Job assistance  Yes
Certification Criteria 
  • More than 60% of score in quiz
  • More than 60% video completion
  • Internship Completion

Pro+ Version 

This advanced course program offers you the complete preparation beginning from scratch to mastering all major concepts and frameworks of Data Science along with generative AI. This course can be of duration 6 or more months.

Name of the Course PW Skills Data Science with Generative AI(Pro+)
Delivery Mode Pre-Recorded Lectures + Live Sessions 
Duration 6 Months
Type/Positioning Job Readiness Course + Live Sessions (Weekly 1 Live)
Certification Yes
Daily Live Doubt Sessions Daily
1-1 Expert Doubt Support  Yes 
Support(Email/Dashboard doubt support/ community Channel) Yes
Live Doubt Session Every Week (Once)
Industry Based Projects Yes
Job Readiness  Yes
Industry Mentor Webinar Yes
Assignment with Written and Video Solutions Yes
Resume Building Support  Yes
Ace Interview Live Training  Yes
Ace Interview Practice Sessions Yes
Job assistance  Yes
Certification Criteria 
  • More than 60% of score in quiz and Assignments
  • More than 60% video completion
  • Internship Completion

PW Skills Data Science Course Online

Explore the vast potential of data science with PW Skills. The Data Science Course is designed to provide a structured learning experience, equipping learners with expertise in Machine Learning, Python, Statistics, NLP, Deep Learning, and Generative AI. Gain hands-on experience integrating AI with data science, enhancing productivity and efficiency in real-world applications.

The course covers essential tools such as NumPy, LangChain, Scikit-Learn, ChatGPT, Jupyter, Flask, Matplotlib, and more. Learners benefit from interactive coursework, mentor-led sessions, and dedicated doubt-solving support throughout their learning journey.

Data Science Full Course with Generative AI

Upon successful completion, learners receive an industry-recognized certification from PW Skills, validating their data science expertise and enhancing career opportunities. The comprehensive course structure ensures learners develop the skills required to excel in data-driven roles.

✔ Hands-On Projects – Work on industry-relevant projects to build a strong portfolio.
✔ Career-Centric Curriculum – Learn essential skills for job-ready profiles.
✔ Interactive Learning Dashboards – Access Q&A forums, revision sessions, and career mentorship.
✔ Certification Upon Completion – Reinforce learning with quizzes and assessments after every module.

Data Science Course in India by PW Skills

PW Skills offers a certification course in data science, designed to help learners develop practical expertise and prepare for industry roles.

✔ Work on real-world projects covering PyTorch, ChatGPT, NLP, Generative AI, YOLO, and more.
✔ Enhance learning with weekly revision sessions for concept reinforcement.
✔ Structured curriculum catering to beginners and experienced professionals.

Why Enroll in the Data Science Course?

✔ Comprehensive Study Materials – Access learning resources, practice questions, assessments, and quizzes.
✔ Live Classes & 1:1 Doubt Support – Get professional mentorship and personalized guidance.
✔ Career Growth Opportunities – Gain exposure to industry-relevant skills and job prospects.
✔ Certification & Job Assistance – Strengthen career prospects with an industry-recognized credential.

The Data Science Course at PW Skills provides a practical learning experience, helping learners build a strong foundation in data science and artificial intelligence.

Data Science Course Syllabus 

The career-centric data scientist course syllabus of our data science certification course is carefully prepared by professionals to help you learn crucial concepts and technologies. The data science course syllabus provides weekly revisions along with the scheduled data science online classes.

Topic Description
Python Basics
Advance Python
  • Handling errors and exceptions
  • Logging event details
  • Python module creation
Databases and Web API
  • DBMS: MySQL (SQL) & MongoDB (No-SQL)
  • Creating API connections using Python
Statistics
  • Basics of statistics
Advanced Statistics
  • Statistical impact on business ideas implementation
Machine Learning (ML) - 1
  • Fundamental terminologies in Data Science domain
Feature Engineering
  • Statistical impact on business ideas implementation
Exploratory Data Analysis (EDA)
  • Using statistics to explore data and find insights
ML Algorithms
  • Regression Models
  • Support Vector Machines (SVM)
  • Clustering Algorithms
  • Decision Trees & Ensemble Techniques
  • Bagging & Boosting Techniques
Deep Learning
  • ANN (Artificial Neural Networks)
  • Using TensorFlow & PyTorch
  • CNN (Convolutional Neural Networks)
  • Building and using CNN models
Natural Language Processing (NLP)
  • Text data preparation for NLP
  • Basics of RNN & advanced NLP models
Generative AI
  • AI tools & techniques for data analysis and synthesis
  • Technologies: Hugging Face, LLMs, BERT, GPT, Image Generation
Projects
  • Build ML models and deploy on the cloud
  • Applications: Spam Detection, Climate Visibility
  • Build a Chatbot using Langchain & Chainlit
  • Face Detection Project
  • Mini Project in TensorFlow

Data Science Courses in India Duration

Our Data Science Courses in India is helps you unlock the power of Generative AI with Data Science. This online data science course duration is 6 months, with more than 200+ hours of learning. The course is covered in blended mode, with 24x7 support throughout the data scientist programs online duration.

  • Doubt Clearing Sessions
  • Dashboard Q&A Forum
  • Career Guidance Sessions
  • Peer Connections
  • Email Support

Data Science Courses Fees (Basic/Pro/Pro+)

Check the data science course fees for all course modules available to students. This way you can select the most suitable course based on your needs and knowledge.

At PW Skills, our main aim is to develop upskilling and affordable courses to help students from different backgrounds easily enroll and learn from the best mentors, resources, and study materials. Students can also avail EMI benefits with the course. Check the fee of all types of Data Science Courses below.

Data Science Course Fees
Name of the course Duration Course fee 
Data Science Course (Basic) 6 Months INR 7,000
Data Science Course (Pro) 6 Months INR 15,000
Data Science Course (Pro+) 6 Months INR 20,000

Machine Learning Course 

AI & ML Course with PW Skills

Data Science and Machine Learning are trending hot topics in the tech world. With so many advancements with the introduction of artificial intelligence and machine learning algorithms, applications and tools are becoming more advanced and powerful. Machine learning is considered a subset of artificial intelligence. It can analyze vast amounts of data and make data-driven decisions based on the data provided.

Learn Machine Learning with our interactive and affordable ai & ml course at PW Skills. Get hands-on learning experiences and professional mentors with industry-level experience. 

AI & Machine Learning Courses for Beginners

Learn Fundamental AI & machine learning courses trending in tech industries, such as Regression models, SVM, Clustering algorithms, Decision trees, Bagging and boosting techniques, etc. 

Learn how statistics are important behind any business idea to be implemented, along with data analysis. Learn about implementing machine learning in the Data Science domain. Also, learn to build a product using machine learning models and deploy it on the cloud to access it from anywhere. 

If you are a beginner looking for an interactive and affordable ai and machine learning courses for beginners, then we are your last stop. Our courses are prepared especially keeping in mind the young graduates or beginners who want to pursue their career or switch their profiles for good opportunities.

AI Certificate Course | AI and ML Courses Online

Master the infinite possibilities with the integration of Machine learning with Data Science and the power of artificial intelligence, all contained in a single course. Machine learning's powerful algorithms and models are revolutionising the way we interact with technology. These cutting-edge technologies are changing the way we interact. Today, AI and machine learning technologies are transforming industries, automating repetitive tasks with self-trained models, and augmenting human capabilities. 

The ai certificate course will help you learn the fundamentals of machine learning with hands-on practical learning and interactive tutorials. This six-month certification course helps you get various opportunities in the relevant field. Get all-round development with complete Machine Learning, Data Science, and generative AI all together in the ai ml online courses, with more than industry-relevant projects. 

Get data science certificate after completing the course video assignments and assessments. 

  • Must watch 70% of the course videos.
  • Must score 70% on quiz and assignment

Data Science with Python Course

Python is a widely used programming language by developers around the world due to its easier syntax, compatibility, and extensive library support. Python can easily integrate with technologies to empower it and develop complex algorithms. 

In this data science with python course, we will learn some crucial Python libraries such as NumPy, Pandas, Matplotlib, Scikit Learn, etc. These packages help to streamline the data preparation and model development processes and make them more productive. 

We will help you learn and practice complete Data Science and Machine learning with Python. For the first six weeks, our faculties will help you learn and practice different industry-relevant problems in Python from scratch. 

Data Science and AI course

Artificial Intelligence is transforming many industries to promote automation and smart technology to push creative boundaries across the field in a new technological era. Data Scientists are important members of an organisation who employ various technologies and tools to extract crucial insights from large, unstructured data to help businesses make informed decisions. 

Data Science is a career with high potential for growth, competitive pay and several other benefits. Recent studies predict that by 2025, nearly 463 exabytes of data will be generated daily, which will require skilled data scientists to extract useful insights from large and complex unstructured data. 

In our data science and artificial intelligence online courses, we offer a complete data science and AI course from the beginner level with the power of AI. Keeping up with technological advancements is important for a data scientist. With the help of machine learning algorithms, many powerful modules can be prepared to automate tools and technologies and increase efficiency and productivity. Let us look at some of the major program highlights of our machine learning and data science course.

  • 200+ Learning Hours
  • One to one Doubt Support
  • Industry Relevant Projects
  • Practice Exercises
  • Profile Building Session
  • Resume Building Session
  • Seamless Support 24x7

Python Machine Learning Course

Artificial Intelligence and Machine Learning have become two of the most sought and promising skills of the future. If you want to be in the wave of automation and transformation, then enrol in our python machine learning course. 

Due to the increasing need for professionals having AI and Machine learning skills, acquiring certifications in these domains can help you open up new opportunities. This python machine learning program will help you develop your logical thinking and problem-solving skills. You can get faster success with data scientist certification course.

Within the course, we will work on several smart projects, such as a customised chatbot, climate visibility, name entity recognition, Gen AI (Alex assistant), On Prompt image and caption generation, and much more. Get multiple benefits with our course, such as

  • Learn about new-age technologies such as chatGPT, Dalle, OpenAI, and more.
  • Engage conversations in real-time
  • Q&A Dashboard support sessions
  • Learn with the industry professionals
  • Certification of program completion
  • Hands-on practical learning

Still Confused?

Get Connected to our experts and know what's best for you. Achieve your dreams!

Frequently Asked Questions

This course is a comprehensive journey through the foundational concepts, tools, and advanced techniques required for modern data science. It combines theory with practical implementation to help learners master Python, statistics, machine learning, deep learning, NLP, and Generative AI, preparing them for a wide range of data-driven roles. It’s ideal for beginners, freshers, or working professionals looking to build or switch careers in data science.

Learners should have a basic understanding of programming and mathematics. Familiarity with Python is beneficial but not mandatory, as foundational concepts are covered in the program.

Yes, the course is designed for beginners and gradually introduces advanced concepts.

You’ll learn Python, NumPy, Pandas, Matplotlib, Seaborn, SQL, MongoDB, TensorFlow, PyTorch, NLTK, Using Hugging Face, BERT, GPT, LangChain and more.

The course lasts 6 months, with a recommended commitment of 8-10 hours per week.