Key Features
Completion Certificate
Internship
Internship Certificate
7 Days Refund Policy
Expert Instructors
One-to-One Session
What Will You Learn?
Accelerate your learning journey with our comprehensive course designed to equip you with essential skills and practical knowledge in Data Science Pro 2025.
- Data Science Foundations
- Machine Learning Basics
- Deep Learning
- Generative AI Concepts
- OpenAI API Integration
- Building AI Models
Requirements
Before getting started with this course, it's beneficial to have the following:
- Laptop with good internet
- Passionate about learning
- No prior experience needed
- Willing to dedicate time
- Curious about emerging AI technologies
Course Completion
Yes
Curriculum
- Introduction to Python and Comparison with Other Programming Languages
- Python Objects: Numbers, Booleans, and Strings
- Container Objects and the Mutability of Objects
- Operators in Python: Arithmetic, Bitwise, Comparison, and Assignment Operators
- Operator Precedence and Associativity
- Conditional Statements (If, Else, Elif)
- Loops in Python: While and For
- Break and Continue Statements
- Range Function
- Introduction to Pandas Series
- Pandas DataFrames
- Pandas Panels
- Pandas Basic Functionalities
- Reading Data from Various Sources
- Reindexing in Pandas
- Iteration in Pandas
- Sorting in Pandas
- Text Data Handling in Pandas
- Customization Options in Pandas
- Indexing and Selection in Pandas
- Statistical Functions in Pandas
- Window Functions in Pandas
- Date Functionality in Pandas
- Categorical Data Handling in Pandas
- Data Visualization in Pandas
- Pandas Tools Overview
- Introduction to NumPy and Ndarray Objects
- Data Types in NumPy
- Array Attributes in NumPy
- Array Creation Techniques
- Numerical Ranges in NumPy
- Indexing and Slicing Arrays
- Advanced Indexing in NumPy
- Broadcasting in NumPy
- Iterating Over Arrays
- Array Manipulation Techniques
- Binary Operators in NumPy
- String Functions in NumPy
- Mathematical Functions in NumPy
- Arithmetic Operations in NumPy
- Statistical Functions in NumPy
- Sorting, Searching, and Counting in NumPy
- Byte Swapping in NumPy
- Views and Copies in NumPy
- Matrix Library in NumPy
- Linear Algebra in NumPy
- Handling Outliers
- Filter Method
- Wrapper Method
- Embedded Methods
- Feature Scaling
- Pca (Principle Component Analysis)
- Data Encoding
- Nominal Encoding
- One Hot Encoding
- One Hot Encoding With Multiple Categories
- Mean Encoding
- Ordinal Encoding
- Label Encoding
- Target Guided Ordinal Encoding
- Covariance
- Correlation Check
- Correlation Check Pearson Correlation Coefficient
- Spearman’s Rank Correlation
- Vif
- Feature Selection
- Recursive Feature Elimination
- Backward Elimination
- Forward Elimination
- Logistics Regression In-Depth Intuition
- In-Depth Mathematical Intuition
- In-Depth Geometrical Intuition
- Hyper Parameter Tuning
- Grid Search Cv
- Data Leakage
- Confusion Matrix
- Precision, Recall, F1 Score, Roc, Auc
- Best Metric Selection
- Multiclass Classification In Lr
- Complete End-To-End Project With Deployment In Multi-Cloud Platform
- Decision Tree Classifier
- In-Depth Mathematical Intuition
- In-Depth Geometrical Intuition
- Confusion Matrix
- Precision, Recall, F1 Score, Roc, Auc
- Best Metric Selection
- Decision Tree Repressor
- In-Depth Mathematical Intuition
- In-Depth Geometrical Intuition
- Performance Metrics
- Complete End-To-End Project With Deployment In Multi-Cloud Platform
- Linear Svm Classification
- In-Depth Mathematical Intuition
- In-Depth Geometrical Intuition
- Soft Margin Classification
- Nonlinear Svm Classification
- Polynomial Kernel
- Gaussian, Rbf Kernel
- Data Leakage
- Confusion Matrix
- Precision, Recall, F1 Score, Roc, Auc
- Best Metric Selection
- Svm Regression
- In-Depth Mathematical Intuition
- In-Depth Geometrical Intuition
- Complete End-To-End Project With Deployment
- Neural Network Overview And Its Use Case
- Detail Mathematical Explanation
- Various Neural Network Architect Overview
- Use Case Of Neural Network In NLP and Computer Vision
- Activation Function -All Name
- Multilayer Network
- Loss Functions. - All 10
- The Learning Mechanism
- Optimizers. - All 10
- Forward And Backward Propagation
- Weight Initialization Technique
- Vanishing Gradient Problem
- Exploding Gradient Problem
- Visualization Of Neural Network
- Lenet-5 Variants With Research Paper And Practical
- Alexnet Variants With Research Paper And Practical
- Googlenet Variants With Research Paper And Practical
- Transfer Learning
- Vggnet Variants With Research Paper And Practical
- Resnet Variants With Research Paper And Practical
- Inception Net Variants With Research Paper And Practical
- FASTER RCNN
- YOLO
- Talking About Business Intelligence
- Tools And Methodologies Used In Bi
- Why Visualization Is Getting More Popular
- Why Tableau?
- Gartner Magic Quadrant Of Market Leaders
- Future Business Impact Of Bi
- Tableau Products
- Tableau Architecture
- Bi Project Execution
- Tableau Installation In Local System
- Introduction To Tableau Prep
- Tableau Prep Builder User Interface
- Data Preparation Techniques Using Tableau Prep Builder Tool
- How To Connect Tableau With Different Data Sources
- Visual Segments
- Visual Analytics In Depth
- Filters, Parameters & Sets
- Filters, Parameters & Sets
- Filters, Parameters & Sets
- Tableau Calculations Using Functions
- Tableau Joins
- Working With Multiple Data Source (Data Blending)
- Building Predictive Models
- Dynamic Dashboards And Stories
- Sharing Your Reports
- Tableau Server
- User Security
- Scheduling
- Power Bi Introduction And Overview
- Key Benefits Of Power Bi
- Power Bi Architecture
- Power Bi Process
- Components Of Power Bi
- Power Bi - Building Blocks
- Power Bi Vs Other Bi Tools
- Power Installation
- Overview Of Power Bi Desktop
- Data Sources In Power BI Desktop
- Connecting To A Data Sources
- Query Editor In Power Bi
- Views In Power Bi
- Field Pane
- Visual Pane
- Custom Visual Option
- Filters
- Introduction To Using Excel Data In Power BI
- Exploring Live Connections To Data With Power Bi
- Connecting Directly To Sql Azure, HD Spark, SQL Server Analysis Services/ My SQL
- Import Power View And Power Pivot To Power Bi
- Power Bi Publisher For Excel
- Content Packs
- Introducing Power Bi Mobile
- Power Query Introduction
- Query Editor Interface
- Clean And Transform Your Data With Query Editor
- Data Type
- Column Transformations Vs Adding Columns
- Text Transformations
- Cleaning Irregularly Formatted Data -Transpose
- Date And Time Calculations
- Advance Editor: Use Case
- Query Level Parameters
- Combining Data – Merging And Appending
- Data Modelling
- Calculated Columns
- Measures/New Quick Measures
- Calculated Tables
- Optimizing Data Models
- Row Context Vs Set Context
- Cross Filter Direction
- Manage Data Relationship
- Why Is Dax Important?
- Advanced Calculations Using Calculate Functions
- Dax Queries
- In-depth intuition of Transformer-Attention all your need Paper
- Guide to complete transformer tree
- When to use which transformer architecture
- Application and use cases of LLMs
- Transfer learning in NLP
- How to use pre-trained transformer-based models
- How to perform finetuning of pre-trained transformer-based models
- Mask language modeling
- BERT- Google
- GPT- OpenAI
- T5- Google
- Megatron- NVIDIA
- Evaluations Matrixs of LLMs models
- GPT-3 and 3.5 Turbo use cases
- Learn how Chatgpt trained
- Introduction to Chatgpt- 4
- Introduction to OpenAI
- Installation of OpenAI package
- Experiment in the OpenAI playground
- How to setup your local development environment
- Different templates for prompting
- OpenAI Models GPT-3.5 Turbo DALL-E 2, Whisper, Clip, Davinci and GPT-4 with practical implementation
- OpenAI Embeddings and Moderation with Practical Implementation
- Implementation of Chat completion API, Functional calling and Completion API
- How to manage the Tokens
- Different Tactics for getting an Optimize result
- Image Generation with OpenAI LLM model
- Speech to text with openAI
- Use of Moderation for content complies with OpenAI
- Understand rate limits, error codes in OpenAPI
- OpenAI plugins connect ChatGPT to third-party applications.
- How to do fine-tuning with custom data
- Project: Finetuning of GPT-3 model for text classification
- Project: Telegram bot using OpenAI API with GPT-3.5 turbo
- Project: Generating YouTube Transcript with Whishper
- Project: Image generation with DALL-E
- Introduction to vector database
- Vector database foundation
- Vector database use cases
- Text embedding
- Vector similarity search
- SQLite database
- Storing and retrieving vector data in SQLite
- Chromadb local vector database part1 setup and data insertion
- Query vector data
- Fetch data by vector id
- Database operation: crate, update, retrieve, deletion, insert and update
- Application in semantic search
- Building AI chat agent with langchain and openai
- Weviate Vector Database
- Pinecone Vector Database
- Introduction to langchain
- How Does LangChain Work
- Installation and setup of langchain in local env
- Hello world of LangChain application - Chaining a simple prompt
- Components of langchain like Schema, Model I/O, Prompts, Indexes, Memory, Chains, Agents, Callbacks
- Understanding prompts, language model and Output parser
- Concept of async API, fake LLM human input, LLM Caching
- Implementation of Chat models with human input chat model, chain, prompt and streaming
- Implementation of output parser with json parser, XML parser, and list parser
- Implement retrieval with document loader document transformer text embedding and vector store
- Implement memory with chat messages, with the conversational knowledge base, and with vector store
- Text summarization with langchain
- Question Answern with langchain
- Chatbot with langchain
- Langchain streaming
- Embeddings and Vector Data Stores in langchain
- Understanding PromptTemplate + LLM + OutputParser
- Langchain expression language
- Bind runtime args
- Configurable alternatives
- Add fallback
- Run arbitrary functions
- Use RunnableParallel/RunnableMap
- Route between multiple Runnables
- Document Loaders
- CSV, PDF, and JSON file analysis using Langchain
- Prompt Templating and Prompt Management
- Retrieval-augmented generation chain
- multiple chains
- Querying a SQL DB
- How to add in moderation around your LLM application.
- Hugging face Models with langchain
- Falcon 7B fine-tune on custom dataset
- Mistral 7B - Finetune and Inference for Custom Usecase
- Langchain with Google PaLM2 Model
- Langchain with Facebook Llama2 Model
- Langchain webapp with Streamlit and flask
- Project: MCQ Quiz Creator Application
- Project: Youtube video summarizer and youtube script writing
- Project: Custom Chatbot for any website
- Project: Auto Recrutier
- Introduction to LlamaIndex
- Difference between langchain and LlamaIndex
- Difference between Llama and LlamaIndex
- Setup of LlamaIndex in our local env
- How to use LLMs with LlamaIndex
- Exploring Llamahub
- How to connect with external Data
- What is in Context Learning & Fine Tuning
- Why indexing required in LLM apps
- Persist indexes
- How to index our data
- Creating documents objects
- Different Documents Loader
- How to verify sources of the response
- How to connect with different documents like csv, txt, pdf, etc
- document management
- Recursive file processing from directory sub directory
- Building apps with LlamaIndex
- Customization LLM Models in Application
- Integration with endpoint flask and streamlit
- Enable Streaming response
- Chat engine: Condense mode
- Chat engine: React mode
- Customizing Prompt
- How to use vector databases like ChromaDB and Weviate with LlamaIndex
- Token Prediction & Cost Analysis
- Integrations with OpenAI, Hugging Face
- Project: Financial Stock Analysis using LlamaIndex
- Project: Chat with Books and PDF Files with Llama 2