Harvard University offers a ton of FREE online courses.
From Computer Science to Artificial Intelligence.
Here are 10 FREE courses you don't want to miss

1. Introduction to Computer Science
An introduction to the intellectual enterprises of computer science and the art of programming.
Check here 👇
https://pll.harvard.edu/course/cs50-introduction-computer-science?delta=0


2. Web Programming with Python and JavaScript
This course takes you deeply into the design and implementation of web apps with Python, JavaScript, and SQL using frameworks like Django, React, and Bootstrap.
Check here 👇
https://pll.harvard.edu/course/cs50s-web-programming-python-and-javascript?delta=0

3. Introduction to Programming with Scratch

A gentle introduction to programming that prepares you for subsequent courses in coding.
Check here 👇
https://pll.harvard.edu/course/cs50s-introduction-programming-scratch?delta=0

4. Introduction to Programming with Python
An introduction to programming using Python, a popular language for general-purpose programming, data science, web programming, and more.
Check here 👇
https://edx.org/course/cs50s-introduction-to-programming-with-python



5. Understanding Technology
This is CS50’s introduction to technology for students who don’t (yet!) consider themselves computer persons.
Check here 👇
https://pll.harvard.edu/course/cs50s-understanding-technology-0?delta=0

6. Introduction to Artificial Intelligence with Python
Learn to use machine learning in Python in this introductory course on artificial intelligence.
Check here 👇
https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python?delta=0


7. Introduction to Game Development
Learn about the development of 2D and 3D interactive games in this hands-on course, as you explore the design of games such as Super Mario Bros., Pokémon, Angry Birds, and more.
Check here 👇
https://pll.harvard.edu/course/cs50s-introduction-game-development?delta=0

8. CS50's Computer Science for Business Professionals
This is CS50’s introduction to computer science for business professionals.
Check here 👇
https://pll.harvard.edu/course/cs50s-computer-science-business-professionals-0?delta=0


9. Mobile App Development with React Native
Learn about mobile app development with React Native, a popular framework maintained by Facebook that enables cross-platform native apps using JavaScript without Java or Swift.
Check here 👇
https://pll.harvard.edu/course/cs50s-mobile-app-development-react-native?delta=0

10. Introduction to Data Science with Python
Join Harvard University instructor Pavlos Protopapas in this online course to learn how to use Python to harness and analyze data.
Check here 👇
https://pll.harvard.edu/course/introduction-data-science-python?delta=0

Artificial Neural Networks with Java
Igor Livshin, 2019

Artificial Neural Networks with Java
Igor Livshin, 2019

Some helpful Data science projects for beginners

https://www.kaggle.com/c/house-prices-advanced-regression-techniques

https://www.kaggle.com/c/digit-recognizer

https://www.kaggle.com/c/titanic

BEST RESOURCES TO LEARN DATA SCIENCE AND MACHINE LEARNING FOR FREE

https://developers.google.com/machine-learning/crash-course

https://www.kaggle.com/learn/overview

https://forums.fast.ai/t/recommended-python-learning-resources/26888

https://www.fast.ai/

https://imp.i115008.net/JrBjZR

https://ern.li/OP/1qvkxbfaxqj

ENJOY LEARNING 👍👍

Machine Learning in Microservices
Mohamed Abouahmed, 2023

Where to get data for your next machine learning project?

An overview of 8 amazing resources to accelerate your next project with data!

📌 Google Datasets
Easy to search Datasets on Google Dataset Search engine as it is to search for anything on Google Search! You just enter the topic on which you need to find a Dataset.

📌 Papers with Code Datasets
An exclusive collection of 4053 machine learning datasets with a supreme search and a good composition of datasets .

📌 Kaggle Dataset
Explore, analyze, and share quality data.

📌 Big Bad NLP Datasets
One of the best sources for sophisticated Natural Language Processing datasets

📌 Hugging Face Datasets
Well known for NLP but good news hugging face is expanding and they can add datasets for machine learning soon, they have 921 datasets.

📌 Open Data on AWS
This registry exists to help people discover and share datasets that are available via AWS resources

📌 Awesome Public Datasets
A topic-centric list of HQ open datasets.

📌 Azure public data sets
This one has public data sets for testing and prototyping.

📌 Carnegie Mellon University
A listing of 750 databases, datasets, and research support tools.

Bonus: This articles on Kdnuggets covers around 80 datasets sources of the datasets. Enjoy machine learning.

MACHINE LANGUAGE

The instructions in binary form, which can be directly understood by the computer (CPU) without translating them, is called a machine language or machine code.

Machine language is also known as first generation of programming language. Machine language is the fundamental language of the computer and the program instructions in this language is in the binary form (that is 0's and 1's).

This language is different for different computers.

It is not easy to learn the machine language.

ADVANTAGE OF MACHINE LANGUAGE

The only advantage of machine language is that the program of machine language runs very fast because no translation program is required for the CPU.

DISADVANTAGE OF MACHINE LANGUAGE

Here are some of the main disadvantages of machine languages:

• Machine Dependent - the internal design of every computer is different from every other type of computer, machine language also differs from one computer to another. Hence, after becoming proficient in the machine language of one type of computer, if a company decides to change to another type, then its programmer will have to learn a new machine language and would have to rewrite all existing program.

• Difficult to Modify - it is difficult to correct or modify this language. Checking machine instructions to locate errors is very difficult and time consuming.

• Difficult to Program - a computer executes machine language program directly and efficiently, it is difficult to program in machine language. A machine language programming must be knowledgeable about the hardware structure of the computer.