Data Science With Generative AI- Hinglish

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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For Pro Plan

22 Apr 2025

Date of Commencement

6 Months

Duration

Live + Recorded

Delivery Mode

Hindi + English

Language

Unlock Your Potential: Exclusive Course Offerings

Comprehensive Learning Content
Practice Sessions
Live Doubt Resolution Sessions
Industry Oriented Curriculum
Certification of Completion
Capstone Projects
Assignments and Projects
Email Support

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
  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
  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
  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
  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 : Installation Of Yolov9
  7. Lecture 7 : Data Annotation & Preparation
  8. Lecture 8 : Download Data & Configure Path
  9. Lecture 9 : Download & Configure Pretrained Weight
  10. Lecture 10 : Start Model Training
  11. Lecture 11 : Evaluation Curves Yolov9
  12. Lecture 12 : Inferencing Using Trained Model
  13. Lecture 13 : Evaluation Curves TFOD2
  14. Lecture 14 : 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

A 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.

Ekta Negi

Fractal | Deloitte | Capgemini

  • 5+ Years

    Work Experience

  • 3+ Years

    Teaching Experience

Data Scientist - Fractal | Capgemini, Deloitte | Business Insights | ML & Analysis Specialist

Ajay Kumar Gupta

Synopsis

  • 4+ Years

    Work Experience

  • 3+ Years

    Teaching Experience

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

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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. .

Pavan Jahagirdar

Business analyst

Data Science Consultant: Projects & networking shaped career. .

Ashish Dhakad

Decision Scientist

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

Vaibhav Joshi

Consultant

Support, dedication, guidance for achieving dreams. .

Payel Tarafder

System Engineer

Under the mentorship of our esteemed faculty, I've developed invaluable skills, seamlessly integrating theory with hands-on course curriculum. .

Jyotsna Dusane

Senior Data Engineer

I owe my success as a ML engineer at Cognizant to the training I received at PW Skills. The faculty's expertise and the well-structured course curriculum gave me a solid foundation in system engineering, enabling me to excel in my role. .

Binit Kumar Jha

ML Engineer

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Frequently Asked Questions

The course spans six months from the start date.

Yes, you will receive a certificate upon successful completion of the course. To qualify, you must complete at least 60% of the lectures and assignments and finish at least one medium-level difficulty project on the experience portal.

To clarify your doubt you can join our doubt session happening daily at 4PM to 10 PM.

No, you just need a reasonably configured machine (laptop or desktop). Any required software installations will be communicated through the recorded lectures, with detailed instructions provided on the dashboard to simplify the process. Additionally, you can use our PW Labs for in-browser IDE support if your system cannot support the installation of any required software.

There are no major prerequisites for the course. You should be familiar with basic mathematical concepts (equivalent to 12th grade) and proficient in basic English to understand the lectures.