The Ultimate Job Ready Data Science Course
About Course
This is a to-the-point, ShamsulX style Data Science course! This all-in-one Job-Ready Data Science Course is designed for beginners and intermediate learners who want to master data science skills and become industry-ready with hands-on experience.
What Will You Learn?
- Master Python programming from a data science perspective
- Create stunning data visualizations with Matplotlib and Seaborn
- Clean and preprocess real-world datasets for accurate insights
- Use Jupyter Notebooks for data-driven development
- Perform powerful data analysis using Pandas and NumPy
- Understand and apply core statistics and probability concepts
- Work on real-life projects
- Many Developer Tools like Quadratic AI (Free with this course)
Course Content
Introduction to Data Science
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What is Data Science
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How to Download the Handbook
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The Data Science Lifecycle
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Data Science Tools: VS Code, Jupyter, Pycharm & More
Understand the Conda Environment
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Installing Conda
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What is Anaconda | Anaconda vs Miniconda
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Adding Conda to Environment Variables
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Understanding Conda Workflow
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Anaconda Navigator: Quick Tour
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Jupyterlab and Jupyter notebook Tutorial
Python Refresher ( for data Science)
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Why Choose Pyhton for Data Science
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Variables , Data Types and Typecasting
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String & String Methods in Python
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Operators in Python
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Taking Input from the user
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Operator Precedence
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If else Conditionals & Functions in Python
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Match Case in Python
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String Formatting and f-string
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Loops in Python
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List and LIst Methods
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Tuple and Tuple Methods
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Sets and Set Methods
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Dictionary & Dictionary Methods
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File Handling
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Json Module
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Object Oriented Programming
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List comprehensions
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Lambda functions
Claim your free Developer Tools
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Quadratic AI – Your AI Data Analyst
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Jetbrains Pycharm Pro- IDE Packed with AI
Project 1: Coders of Delhi
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Welcome to CodeBook- Your Data Science Internship Begins
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Cleaning and Structuring the Data
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Finding “People You May Know”
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Finding “Pages Your Might Like”
Project 2: Coders of Bangalore
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Welcome Sam altman to Banglore
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Data Collection in Indiranagar
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Parsing Data in Pure Python
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Finding People with Max Posts and Followers
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Finding People with Max Posts,Followers and Categories
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Analyzing the Final Collected Data
Data Analysis Using Numpy
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Why use NumPy
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Creating NumPy Arrays
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Indexing and Slicing
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Multidimensional Indexing and Axis
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Data Types in NumPy
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Broadcasting in NumPy
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Built-in Mathematical Functions in NumPy
Data Analysis Using Panda
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Getting Started with Pandas
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Core Data Structures in Pandas
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Creating DataFrames
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Data Selection & Filtering
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Data Cleaning & Preprocessing
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Data Transformation
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Melt and Pivot
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Aggregation & Grouping
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Merging & Joining Data
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Working with CSVs
Data Visualization using Matplotlib and Seaborn
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Introduction to Data Visualization
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Plotting Charts using Matplotlib
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Bar Charts in Matplotlib
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Pie Charts in Matplotlib
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Stack Plots in Matplotlib
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Histogram in Matplotlib
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Scatter Plots in Matplotlib
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Subplots in Matplotlilb
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Introduction to Seaborn
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Basic Plot Types in Seaborn
Data Collection Techniques
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Data Collection Techniques- Overview
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Introduction to Web Scrapping
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HTML for Web Scrapping
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Using requests module for Data Collection
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Using Beautiful Soup for Data Collection
SQL for Data Science
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Introducton to Databases
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Installing MySQL
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Creating a Database
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Creating a Table
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Modifying a Table
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Inserting Data into a Table
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Selecting Data from a Table
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Updating Data in a Table
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Deleting Data from a Table
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Transactions in MySQL
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Getting Current Date and Time
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Deep Dive into Constraints
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Foreign Keys
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Joins in MySQL
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Union in MySQL
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Functions in MySQL
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Views in MySQL
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Indexes in MySQL
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SubQueries in MySQL
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Group BY in MySQL
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Stored Procedures in MySQL
Probability
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Introduction to Probability
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Types of Experiments in Probability
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Probability Practice Set 1
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Basic Rules of Probability
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Practice Questions on Probability
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Conditional Probability
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Bayes Theorem
Probability Distribution and Central LImit Theorem
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Introduction to Probability distributions
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Uniform Distribution
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Binomial Distribution
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Normal Distribution
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Central Limit Theorem
Machine Learning for Data Scientists
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Introduction to Machine Learning
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How Machines Learn
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Installing Scikit Learn
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Our first ML Model using Scikit-learn
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Using different ML Models
Types of ML Algirithms
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History of Machine Learning
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Supervised Learning
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Unsupervised Learning
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Reinforcement Learning
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Which ML Technique to choose
Practical ML using Scikit-learn
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Solving Real Worlds ML Problems
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Datasets for Machine Learning
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Training ML Algorithm on Iris Dataset
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Measuring Accuracy of our Predictions
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Predicting Gurgaon City House Prices
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Revisiting Steps to solve this problem
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Measuring Errors(RMSE & MAE)
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Analyzing the Data(EDA)
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Creating a Test Set
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Stratified Shuffle Split
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Visualizing the Data
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Further Preprocessing & Handling Missing Data
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Scikit-learn’s Design Principle
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Handling Categorical values
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Feature Scaling in sklearn
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Constructing Pipelines in sklearn
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Column Transformer and Consolidating the Pipeline
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Training ML Algorithms on Preprocessed data
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Model Persistence and Inference with Joblib
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Conclusion and Wrapping up House Pricing Project
Deep Learning & Neural Networks
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Introduction to Deep Learning and Neural Networks
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Perception- The Simplest Neural Network
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Deep Learning – Common Terminology
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Training a Perceptron using Scikit-learn
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Pytorch vs Tensorflow vs Keras
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Installing Tensorflow
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Understanding and visualizing MNIST Dataset
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Training a Neural Network using Tensorflow on MNIST Dataset
Web Development for Data Scientists
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Introduction to Web Development for Data Scientists
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HTML for Data Scientists
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CSS for Data Scientists
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Introduction to Flask
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Static and Templates folder
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Changing Static Path and Static Folders
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Handling Forms in Flask
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Jinja Templates in Flask
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Template Inheritance in Flask
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Message Flashing in Flask
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Query Parameters in Flask
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Creating an API in Flask
Large Language Models
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Introduction to LLMs
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History of LLMs
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How LLMs work
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Retrieval Augmented Generation(RAG)
Leveraging AI as a Data Scientist
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Using AI for Data Science
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Cursor AI Tutorial
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Claude Code Tutorial
Git for Data Science
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Introduction to Git and GitHub
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Understanding the Git Workflow
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Practically Implementing Git Workflow
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Viewing All Commits
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Advanced Tracking and Managing
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Branches in Git
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Essential Git Branch Commands
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Merge Conflicts and How to resolve them
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Introduction to GitHub
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Essential Remote Repository Commands
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Installing and Using GitHub Desktop
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.gitignore in Git
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Git Stash
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Using Git in VSCode
Using Google Colab for Data Science
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Introduction to Google Colab
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Creating Our First Colab Notebook
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Mounting drive to Google Colab
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Terminal, Variables & Snippets in Colab
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Running Whisper on Google Colab
Project 3- RAG based AI Teaching Assistant
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What we will Build in This Project
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Steps to Create our AI Assistant
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Video to Text with Timestamps Using Whisper
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Converting Videos to mp3 for Whisper
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Using Whisper to Translate and Transcribe mp3
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Writing Sample mp3 chunks to a json file
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Chunking all video files with metadata
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Video Chunking Progress After 8 Hours
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Creating Embeddings for our Video Chunks
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Bulk Embeddings & Chunk Loading with Pandas
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Pulling top Matching Chunks
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Using Joblib to save dataframe
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Introspecting top results
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Creating prompt for our RAG based System
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Getting response from the LLM
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Conclusion and Creating a Readme file
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Bonus Video 1: Plan for improving the existing RAG Pipeline
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Bonus Video 2: Replacing Local LLM with GPT-5 to improve quality
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Bonus Video 3: Reducing no. of Chunks to preserve context and meaning
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Bonus Video 4:Building better embeddings from updated chunks
Conclusion
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Conclusion
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