ML
🚀 Machine Learning Roadmap
Welcome to the Machine Learning Roadmap! This guide will help you navigate through the essential topics required to become proficient in Machine Learning. Click on any topic below to explore learning resources.
📌 Roadmap Overview
📖 Table of Contents
- 📊 Mathematics
- 🛠️ Data Science Tools
- 🐍 Python Playlist
- 📊 Power BI, Tableau
- 🗄️SQL Playlist
- 📚 Data Science Libraries
- 📖 Machine Learning Concepts
- 🧠 Deep Learning
- 👀 Computer Vision
- 📜 NLP (Natural Language Processing)
- 🤖 LLMs (Large Language Models)
- 🎨 Generative AI using LangChain
📊 Mathematics
Mathematics is the backbone of Machine Learning. Below are essential topics to learn:
🔢 Topics Covered:
- Linear Algebra: Understand matrices, vectors, eigenvalues, eigenvectors, and matrix operations.
- Probability & Statistics: Know probability distributions, statistical inference, random variables, and common distributions like normal, binomial, and Poisson.
- Calculus & Optimization: Basic understanding of derivatives, gradients, and integrals. Especially helpful for optimization.
📚 Resources:
- 🔢 Linear Algebra - Mathematics for ML
- 📊 Probability & Statistics - Mathematics for ML
- 📈 Calculus & Optimization - Mathematics for ML
- 📄 Download Notes 🔽
🛠️ Data Science Tools
A collection of tools used in Data Science.
📚 Resources:
- 📺 Jupyter Notebook Guide
- 📺 Kaggle for Beginners Part-1
- 📺 Kaggle for Beginners Part-2
- 📺 Google Colab Tutorial
- 📺 Integration of Data Science Tools
🐍 Python Playlist
- Python: Core programming language used in machine learning.
📚 Python Resources:
- 📺 Python Hindi Playlist (Basic to Advance)
- 📺 Python English Playlist (Basic to Advance)
- 📄 Download PDF - Python
📊 Power BI, Tableau
For data analysis and visualization.
📚 Resources:
🗄️ SQL Playlist
SQL is crucial for handling structured data.
📚 Resources:
- 📺 SQL for Data Science
- 📺 SQL (Interview Series)
- 📄 SQL Book
- 📄 SQL Leetcode Questions PDF
- 🔗 SQL Leetcode Top 50 Problems
- 🔗 SQL Interview Questions (DataLemur)
Practice Problems based on SQL
| Sr. No. | Problem | DIFFICULTY |
|---|---|---|
| 1 | Combine Two Tables | Easy |
| 2 | Duplicate Emails | Easy |
| 3 | Customers Who Never Order | Easy |
| 4 | Delete Duplicate Emails | Easy |
| 5 | Rising Temperature | Easy |
| 6 | Find Customer Referee | Easy |
| 7 | Big Countries | Easy |
| 8 | Classes More Than 5 Students | Easy |
| 9 | Sales Person | Easy |
| 10 | Triangle Judgement | Easy |
| 11 | Biggest Single Number | Easy |
| 12 | Not Boring Movies | Easy |
| 13 | Swap Salary | Easy |
| 14 | Product Sales Analysis 1 | Easy |
| 15 | Project Employees 1 | Easy |
| 16 | Sales Analysis III | Easy |
📚 Data Science Libraries
- Libraries: Familiarity with libraries such as:
NumPyfor numerical computationsPandasfor data manipulationMatplotlibandSeabornfor data visualizationScikit-learnfor classical ML algorithmsTensorFloworPyTorchfor deep learning
📚 NumPy Resources:
You can follow any of the playlist which ever you like
📚 Pandas Resources:
You can follow any of the playlist which ever you like
📚 Matplotlib Resources:
You can follow any of the playlist which ever you like
📚 Seaborn Resources:
You can follow any of the playlist which ever you like
📚 Scikit-learn Resources:
Learn it along with Machine learning Algorithms
📚 Tensorflow Resources:
Learn it along with Deep Learning
📚 PyTorch Resources:
Learn it along with Deep Learning
📖 Machine Learning Concepts
A structured ML course.
📚 Resources:
🧠 Deep Learning
Understanding Neural Networks.
📚 Resources:
👀 Computer Vision
Computer vision enables machines to interpret, analyze, and understand visual information from images or videos to make intelligent decisions.
📚 Resources:
📜 NLP (Natural Language Processing)
Working with text data.
📚 Resources:
- 📺 NLP with Python
- You can also refer below playlist for better understanding of some topics
- 📺 NLP Playlist-2
- 📺 NLP Project Guide
🤖 LLMs (Large Language Models)
Introduction to models like GPT, BERT.
📚 Resources:
🎨 Generative AI Using LangChain
AI that creates new content.
📚 Resources:
Happy Learning! 🚀