40 Days of AI

Artificial intelligence (AI) used to be thought of as an idealistic technology that can only exist in sci-fi movies. Contrasting to the belief, today the technology has become so mature in a short tenure that all major companies are using it to their advantage.

AI has become a major catalyst in driving the next revolution. The rate of progress in the field of artificial intelligence is exponentially increasing and impacting our lives like never before. From self-driving cars to robots replacing our jobs, everything seemed like a distant reality is within our vision.

For all those reasons( and more ), many get interested in learning more about AI, but there are some barriers to learning this shiny topic. Information overload and too much noise are major reasons that I decided to start this series.

I am going to share some easy-to-understand free resources and guidelines to help those who are new to this topic. No one can become an AI expert in 40 days, yet by following the pathway and keep practicing you will gain have enough knowledge and skills to become successful.

This article is going to be the repository of my posts and will be updated daily, please like and share if you found it helpful.

Day 1, the kick-off live broadcast. I just announce my plans and talked a bit about different roles in the AI world. ( Data Analyst - Data Scientist - Data engineer - Machine learning Engineer)

Day 2, we are starting with some important stuff. The foundation, the legendary PYTHON.

Day 3, from a coder to a software (AI) engineer you need to know more than the Syntax. Data structure and Algorithms are crucial down the line.

Day 4, 4 libraries essential to start with Python:

Day 5, Object-Oriented Programming?(OOP) in python relies on the concept of classes and?objects.

Day 6, ?some other fundamental materials which are not as popular but are as important! Maths and Stats!

Day 7, ?some top GitHub repos for you to explore some projects, read and replicate some codes.

Day 8, there are around 10 types of databases based on the application requirements. I would recommend you to learn and practice using at least two major types of databases ( Relational and NoSQL ).

Day 9, data visualization can be essential to strategic communication: it helps us interpret available data; detect patterns, trends, and anomalies; make decisions, and analyze inherent processes.

Day 10, still about Data Visualization and Storytelling. Top 4 python data visualization libraries:

Day 11, 5 types of Machine Learning Algorithms!

Day 12, live about the "40 Days of AI".

Day 13, supervised and Unsupervised Machine Learning Algorithms!

Day 14, Some refresher cheat sheets :

Algebra and Calculus:

Probabilities and statistics:

Day 15, Machine Learning Cheatsheets:

Supervised Learning:

Unsupervised Learning:

Tips:

Day16, some advanced Machine Learning topics!

Day 17, ?Implementation of different machine learning algorithms written in Python.

Day 18, ?the accuracy is not accurate!

Day 19, Reinforcement learning!

Day 20, Self-supervised learning!

Day 21, 10 Distance Measures in Data Science

Day 22, Excel is the one I use the most among the 4!

Day 23, a comprehensive VIP Matplotlib cheatsheet.

Day 24, Federated learning!

Day 25, Machine Learning Course by Microsoft

Day 26, Deep Learning by Andrew Ng

Day 27, One of the most comprehensive Deep Learning books in 2021

Day 28, Convolutional Neural Networks.

Day 29, Some Deep Learning Cheatsheets:

Deep Learning:

Convolutional Neural Networks:

Deep Learning Tips and Tricks:

Day 30: Natural Language Processing

Day 31: ?Interviews with Data Science Professionals

Day 32: Generative Adversarial Networks

Day 33: Knowing and using cloud services are crucial for all Data professionals.

Day 34: MLOps is the framework to operationalize the ML model development process to have a continuous delivery cycle of models.

Day 35: MLOps: CI/CD and Automated Pipelines in Machine Learning

Day 36:Top 20 MLOps Tools (coming soon)

Day 37: Full-stack Deep Learning: from training machine learning models to deploying AI systems in the real world.

Day 38:Curated papers, articles, and blogs on?data science & machine learning in production from companies like Google, LinkedIn, Uber, Facebook Twitter, Airbnb, and ...

Day 39: Data-Centric VS Model-Centric Approach

Day 40: 800+ Data Science Questions and Answers

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