Journey with Artificial Intelligence 💡
This repo consists of all the resources that can be referred during one's Journey with Artificial Intelligence. But who am I? I am a Learning Enthusiast, who is fascinated by the world of Artificial Intelligence (AI). The resources include 👇
Probable Beneficiaries
- Trying to have a Glimpse of AI
- Beginners in the world of AI, like me
- Looking to learn the ins & outs of AI, again like me
- Planning to Revisit some topics
- Stuck in a Challenging Problem and need some Help
- Preparing for Jobs/Internships based on various roles in AI
Course Repositories
- Applied Artificial Intelligence
- Artificial-Neural-Network-Regression
- Deep-Learning-Specialization
- Generative-Adversarial-Networks-Specialization
- Kaggle - Intro to Game AI and Reinforcement Learning
- Kaggle - Time Series
- Linux-Bootcamp
- Logistic-Regression-Pratical-Case-Study
- Machine-Learning-A-Z
- Machine-Learning-Specialization
- MySQL-Bootcamp
- Natural-Language-Processing-BERT
- Natural-Language-Processing-Specialization
- Python3-Bootcamp
- Reinforcement-Learning-Specialization
Case Studies
Resource Repositories
Project Repositories
Tools & Libraries for AI
- Albumentations: Fast & Flexible Image Augmentations
- CatBoost: Gradient Boosting on Decision Trees
- Category Encoders: For encoding Categorical Variables
- CausalML: Uplift Modeling & Causal Inteference Methods
- Dask: Parallel Computing in Python
- fast.ai: Making neural nets uncool again
- Folium: Manipulate and Visualize data in Python
- Gensim: Topic Modelling for Humans
- Gymnasium: Standard API for Reinforcement Learning
- h5py: Pythonic Interface to the HDF5 Binary Data Format
- Hugging Face: Build, train and deploy state-of-the-art models
- Imbalanced Learn: For classification with imbalanced classes
- JAX: High-Performance Array Computing
- Keras: An API for Deep Learning
- LightAutoML: For Automated Machine Learning
- LightGBM: A Gradient Boosting Framework
- Matplotlib: Visualization with Python
- NLTK: A Natural Language Toolkit
- NumPy: Scientific Computing with Python
- OpenCV: A Library with focus on Real-time applications
- Pandas: Data Analysis & Manipulation in Python
- Pillow: Python Imaging Library
- PyTorch: A Machine Learning Framework
- Roboflow: Build and Deploy Computer Vision Models
- Scikit-Learn: Machine Learning in Python
- Scikit-Multilearn: For Multi-label classification
- Scipy: Fundamental algorithms for Scientific Computing
- Seaborn: Statistical Data Visualization
- Spacy: Industrial-Strength Natural Language Processing
- Stable Baselines: RL algorithms based on OpenAI Baselines
- Surprise: Build and Analyze Recommender Systems for explicit rating data
- Tensorflow: A Machine Learning Platform
- XGBoost: A Distributed Gradient-Boosting Library
Recommended Articles, Blog Posts and Podcasts
- BBC Reith Lectures 2021: Living with Artificial Intelligence
- Deconvolution and Checkerboard Artifacts
- Frechet Inception Distance
- From GAN to WGAN
- GAN - How to measure GAN performance?
- GAN - StyleGAN & StyleGAN2
- Machine Bias
- Machine Learning Glossary: Fairness
- The Great Debate: Is it Linux or GNU/Linux?
- The Strange Birth and Long Life of UNIX
- What a Machine Learning tool that turns Obama white can (and can’t) tell us about AI bias
- What is a Vector Database?
My Blogs
- AlphaTensor: DeepMind’s Ingenious Phenom
- Artificial Intelligence vs Covid-19
- Ansoff Matrix - Boosting your Startup, and Case-Study of Lenskart
- Black Box vs White Box approaches to develop AI models
- Demystifying Spectral Embedding
- Dimensionality Reduction
- India, Tesla's Next Stop
- Is India cultivating the path for SNBL Startups?
- Linux VS Windows, Your Choice?
- Living with Artificial Intelligence | Part 1
- Living with Artificial Intelligence | Part 2
- The Story of Modular
E-Books
- A Course in Machine Learning
- A Programmer's Guide to Data Mining
- AI Crash Course
- Alan Turing: The Enigma
- An Introduction to Statistical Learning
- Data Cleaning by Ihab F. Ilyas and Xu Chu
- Deep Learning
- Dive Into Deep Learning
- Evaluating Machine Learning Models
- Everything You Always Wanted to Know About Mathematics.pdf
- Introduction to Probability for Data Science
- Learning SQL
- Machine Learning Yearning: Andrew NG
- Machines Who Think
- Mathematics for Machine Learning
- Natural Language Processing with Python
- Neo4j Graph Algorithms
- Object Oriented Programming with Python
- Pattern Recognition and Machine Learning
- Patterns, Predictions and Actions: A Story about Machine Learning
- Statistics for Machine Learning
- The Book of Why
- The Cartoon Guide to Statistics
- The Elements of Statistical Learning
- Understanding Machine Learning: From Theory to Algorithms
Resources for Beginners
- A Brief Guide to Data Cleaning
- Beginner's Guide to Analytics
- Beginner's Guide to Mathematics of Neural Networks
- Beginner's Guide to Tensorflow
- Evaluating Machine Learning Models: A Beginner's Guide to Key Concepts and Pitfalls
- Introducing Data Science
- Intro to Deep Learning
- Machine Learning for Everyone
- Machine Learning with Python
- R for Beginners
- Tableau Visual Guide
- The Data Engineering Cookbook
- The Data Science Booklet
- The Natural Language Processing Cookbook
- Writing Code for NLP
Resources for Job Interviews
- 100 NLP Questions
- 120 Data Science Questions
- 164 Data Science Questions & Answers
- Big Data Engineering: Interview Questions & Answers
- Data Science Interview Questions
- Data Science Questions & Answers
- Interview Preparation DP Questions
- Resumes and Cover Letters
- System Design Interview Textbook
- Top 100 Python Interview Questions
- Ultimate Guide to DS Interviews
Guides
- 10 Data Visualizations
- 20 AWS Services for ML Engineers
- A Brief Guide to ML and DS
- Basics of Prompt Engineering
- Executive Guide to Data Science and AI
- How to Build a Career in AI
- Linux Guide
- List of AI Resources
- MLOps: From Model-centric AI to Data-centric AI
- Pet Project
- Power BI for Intermediates
- Practitioner's Guide to MLOps
- Tableau Tips
- The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists
- Which chart or graph is right for you?
- The Ultimate Guide to Effective Data Collection
Notes
Cheat Sheets
Research Papers Read
- A Survey on Bias & Fairness in Machine Learning
- Adam: A Method for Stochastic Optimization
- AlexNet: Image Classification using Deep CNNs
- Does Object Recognition work for everyone
- Fairness Definitions Explained
- How to Read a Paper
- PReLU (Parametric Rectified Linear Unit) & He-et-al Initialization
- ResNet: Deep Residual Learning for Image Recognition
Courses
- Applied Artificial Intelligence
- Artificial Neural Network for Regression
- Kaggle - Intro to Game AI and Reinforcement Learning
- Kaggle - Time Series
- Logistic Regression Practical Case Study
- Machine Learning A-Z
- Machine Learning Practical: 6 Real World Applications
- Natural Language Processing with BERT
- The Linux Command Line Bootcamp
- The Modern Python 3 Bootcamp
- The Ultimate MySQL Bootcamp
- Machine Learning Specialization
- Deep Learning Specialization
- Generative Adversarial Networks (GANs) Specialization
- Natural Language Processing (NLP) Specialization
- Reinforcement Learning Specialization
Youtube Videos & Playlists
- AI Research & Journey Talks
- Data Structures
- Database Management System (DBMS)
- Dynamic Programming | Algorithm & Interview Questions
- Graph Theory | Part 1
- Graph Theory | Part 2
- Introduction to Data-Centric AI, MIT IAP 2023
- Machine Learning & Computer Vision Tutorials
- Operating System
- Probabilistic Machine Learning
- Recursion | Algorithm & Interview Questions
- The Age of A.I.
Developer's Surveys and Trends
Interviews & Live Talks
- Heroes of Deep Learning: Andrew Ng interviews Andrej Karpathy
- Heroes of Deep Learning: Andrew Ng interviews Geoffrey Hinton
- Heroes of Deep Learning: Andrew Ng interviews Ian Goodfellow
- Heroes of Deep Learning: Andrew Ng interviews Pieter Abbeel
- Heroes of Deep Learning: Andrew Ng interviews Ruslan Salakhutdinov
- Heroes of Deep Learning: Andrew Ng interviews Yann LeCun
- Heroes of Deep Learning: Andrew Ng interviews Yoshua Bengio
- Heroes of Deep Learning: Andrew Ng interviews Yuanqing Lin
- Heroes of NLP: Chris Manning
- Heroes of NLP: Kathleen McKeown
- Heroes of NLP: Oren Etzioni
- Heroes of NLP: Quoc Le
- My Journey Learning ML and AI through Self Study - Sachi Parikh
- Yann LeCun and Andrew Ng: Why the 6-month AI Pause is a Bad Idea
