This repo consists of all the resources that I followed during my Journey with Artificial Intelligence. But who am I? I am a Novice Learner, in the world of Machine Learning, Deep Learning and Data Science, or simply Artificial Intelligence. The resources include 👇
- Kaggle Competitions
- Course Repositories
- Case Studies
- Resource Repositories like this
- Project Repositories
- Tools & Libraries for AI
- Articles, Blog Posts and Podcasts
- E-books for references
- Resources for Beginners
- Resources for Job Interviews
- Guides for references
- Notes for references
- Cheat Sheets for a quick revision
- Research Papers that I read for a better perspective of how things work
- Links to the Courses
- Youtube Videos and Playlists that I followed
- Developer's Surveys and Trends
- Interviews and Live Talks that motivated me
- Miscellaneous resources
- 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
- Kaggle Competitions are a great way to break into the world of Data Science and Machine Learning.
- Here, you can find out the the various resources for all the Kaggle Competitions, that I have participated in till now.
- These resources include Articles, Blog Posts, Kaggle Discussions, Kaggle Kernels, and whatever else, I find to be apt for any given competition.
- Applied Artificial Intelligence
- Artificial-Neural-Network-Regression
- Kaggle - Intro to Game AI and Reinforcement Learning
- Kaggle - Time Series
- Linux-Bootcamp
- Logistic-Regression-Pratical-Case-Study
- Machine-Learning-A-Z
- MySQL-Bootcamp
- Natural-Language-Processing-BERT
- Python3-Bootcamp
- Deep Learning Specialization
- Generative Adversarial Networks (GANs) Specialization
- App-Behaviour-Analysis
- Breast-Cancer-Classification
- Churn Rate Minimization
- Credit-Card-Fraud-Detection
- eSigning-Classification
- Fashion Class Classification
- Open CV | Virtual Paint | Document Scanner | Number Plate Detector
- OpenCV | Web-Cam Paint
- Smart Agriculture
- CatBoost: Gradient Boosting on Decision Trees
- Category Encoders: For encoding Categorical Variables
- Matplotlib: Visualization with Python
- NumPy: Scientific Computing with Python
- OpenCV: A Library with focus on Real-time applications
- Pandas: Data Analysis & Manipulation in Python
- PyTorch: A Machine Learning Framework
- Scikit-Learn: Machine Learning in Python
- Seaborn: Statistical Data Visualization
- Tensorflow: A Machine Learning Platform
- XGBoost: A Distributed Gradient-Boosting Library
- 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
- Artificial Intelligence vs Covid-19
- Ansoff Matrix - Boosting your Startup, and Case-Study of Lenskart
- Demystifying Spectral Embedding
- Dimensionality Reduction
- India, Tesla's Next Stop
- Linux VS Windows, Your Choice?
- Living with Artificial Intelligence | Part 1
- Living with Artificial Intelligence | Part 2
- 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
- Dive Into Deep Learning
- Evaluating Machine Learning Models
- Introduction to Probability for Data Science
- Learning SQL
- Machine Learning Yearning: Andrew NG
- Mathematics for Machine Learning
- Neo4j Graph Algorithms
- Object Oriented Programming with Python
- Pattern Recognition and Machine Learning
- Statistics for Machine Learning
- The Cartoon Guide to Statistics
- The Elements of Statistical Learning
- Understanding Machine Learning: From Theory to Algorithms
- 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
- 100 NLP Questions
- 164 Data Science Questions & Answers
- Big Data Engineering: Interview Questions & Answers
- 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
- 10 Data Visualizations
- A Brief Guide to Data Cleaning
- A Brief Guide to ML and DS
- Executive Guide to Data Science and 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
- Algorithms Notes
- C++ Notes
- Git Notes
- LaTeX Notes
- Linux Notes
- MySQL Notes
- Python Notes
- R Notes
- SQL Notes
- AI, Neural Networks, Machine Learning, Deep Learrning & Big Data
- Cheat CODE: A Workbook to get you started with DSA
- Excel Cheat Sheet
- Git Cheat Sheet
- Machine Learning Cheat Sheet
- Python Cheat Sheet
- Python Cheat Sheet
- 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
- 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
- Deep Learning Specialization
- Generative Adversarial Networks (GANs) Specialization
- AI Research Talks
- Data Structures
- Database Management System (DBMS)
- Dynamic Programming | Algorithm & Interview Questions
- Graph Theory | Part 1
- Graph Theory | Part 2
- Machine Learning & Computer Vision Tutorials
- Operating System
- Probabilistic Machine Learning
- Recursion | Algorithm & Interview Questions
- AI Index Report 2021
- Computer Vision News 2021
- Tech Trends Report 2021
- The Future of AI in Australia
- The Future of Analytics
- The Future of Jobs in the Era of AI
- 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
- My Journey Learning ML and AI through Self Study - Sachi Parikh