The ultimate roadmap to learn data science and machine learning.

         In this article, we aren't just going to show you the roadmap to being a data scientist or an Ai engineer. Still, we will also provide you with some of our recommended courses from Harvard, Khan Academy, the University of California, and the University of Adelaide. You can complete them and earn your certificates, which may help and motivate you through your journey to achieve your goal (your new artificial intelligence/data science career). Also, you can find a lot of roadmaps on the internet, but KweeKnowledge tries hard to make things easier and friendlier; that's why KweeKnowledge is all you need.

It will be more efficient if you respect those learning steps but don't worry, it's not an obligation:

I. mathematics:

      As you may know, programming doesn't require a very high level of mathematics, but data science and artificial intelligence fields do. Still, it's not a big deal, because you have to master only 4 modules, which probably you know the basics of some:

• note: if you still have some problems with the mathematical basics, the best course we can provide to you is: MathTrackX: Polynomials, Functions, and Graphs. from the University of Adelaide.

    1 Linear algebra:

        Linear algebra is used in data preprocessing, data transformation, dimensionality reduction, and model evaluation in data science. And in machine learning, most of the time, we deal with scalars, vectors, and matrices most of the time.

      So we strongly recommend the course from Khan Academy, it looks like the most efficient in linear algebra: Khan Academy: Linear algebra.

    2 Calculus:

        Calculus plays an integral role in understanding the internal workings of machine learning algorithms. That's why we recommend this course from Khan Academy: Khan Academy: Differential calculus.

    3 Probability and Statistics:

        Statistics and probability may be necessary for many fields, but it's more than machine learning or data science. It's crucial. Probability and statistics are fields of mathematics that quantify uncertainty. It is undeniably a pillar of many machine learning, and many recommend it as a prerequisite subject to study before getting started.

        We recommend the course from Khan Academy: Khan Academy: Statistics and Probability one more time.

• Note: if you prefer one and only one course that may regroup all mathematical fields, here it is: Complete maths for machine learning.

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II. Programming:

     1 Basics of programming with Python:

        Python for machine learning is a great choice, as this language is very flexible: It offers an option to choose either to use OOPs or scripting. There's also no need to recompile the source code. Developers can implement any changes and quickly see the results.

       Also, Python may be the most efficient programming language to use for any Ai field, and we do not recommend other languages. This may motivate you because, 1st, Python is more manageable than many languages to learn. 2nd, the courses suggested here may help you master Python programming quickly. Python for everybody. from the University of Michigan.

    2 Linux and command-line:

        The fact remains that the command line successfully enables thousands of developers, researchers, system administrators, and data analysts to be more efficient and productive at work.

        For that recommended: Linux command-line course.

    3 Python for data analysis:

        To be a data scientist or an artificial intelligence engineer, you need to learn more than the basics. You need to know more about some specific libraries in Python, especially those for data analysis.

        We recommend Data analysis with pandas.

    4 SQL Server database:

        We don't need to spend time talking about databases. They are literally crucial for you as a future data scientist or machine learning engineer.

        For that, we recommend SQL specialization for data science.

III. Machine learning:

    1 Introduction to Machine Learning:

        Finally, we've finished with the fundamentals, and we will begin learning authentic machine learning. That is why we recommend the best YouTube playlist: Machine Learning | Andrew Ng.

    2 Classic Machine Learning:

        Here, you have to know some specific concepts, such as methods, use cases, and tools, but the good news is that the playlist may be efficient for learning those concepts. Still, if you want to have a dragon level in machine learning, we recommend some books such s: Understanding Machine Learning: From theory to algorithm

IV. Data Science:

        If you're only interested in machine learning, you can skip that step, but if the title of data scientist was one of your priorities, we recommend Complete data science BootCamp 2022.

V.  Deep learning:

        Through those steps, you may understand how deep learning is really funny and exciting, and that's what SPTria KweeKnowledge is looking for, that's why we tried hard to make your dream real, and we recommend you: A deep learning course.

• Note: after completing those steps, we can really say that you became an artificial intelligence expert, but still not enough. You need to practice and make your own machine learning projects, but with KweeKnowledge, there are no limits. We recommend the Kaggle website. Kaggle allows users to find and publish data sets, explore and build models in a web-based data-science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.