Actually, I am not big fan of big articles and big roadmap, I like to go step by step and I prefer a smaller, more focused roadmap and lucky I got this one.
This is one of the most clear and simplest and easiest Machine Learning RoadMap you can get and that’s why I am sharing this with you along with the resources where you can learn all the skills mentioned in this RoadMap.
1. Machine Learning Roadmap Starts with Python and R
Learning this fabulous programming language is not just mandatory to start your journey in machine learning. Still, it is an investment in yourself that you may need all your life because you can even shift your career to another one and still use python in that new industry.
So first of all, focus all of your energy on this language, and I will mention one of the best resources that will help you a lot in learning python:
1.1. Python for Everybody Specialization:
This is almost the most popular course among python developers which will help you learn the basics of this language and use the Python built-in data structure, accessing the web, which will be very useful when you are trying to get the data from the web, and using python with the database.
The course has more than a million students with a 4.8 rating score which is an excellent resource.
Though, if you are fan of Udemy courses, you can join Angela Yu’s 100 Days Of Python Bootcamp course and if you need more choices, you can also checkout this list of best Udemy courses to learn Python online.
Alternatively, you can start your Machine Learning Career with R programming language. R is also a well designed language for Mathematical and Statistical computing but quite popular on ML space.
With the addition of R studio and R’s excellent data visualization support, many Machine Learning Engineer prefer to learn R programming language.
If you want to start with R then . R Programming A-Z™: R For Data Science With Real Exercises! courses on Udemy is a good starting point. You can join this course to not just learn R but how to use R programming language in Data Science and ML space.
This course is created by Kirill Eremenko, one of the most popular Udemy instructor, a founder and data science and ML expert.
And, if you need more choices, you can also checkout his list of best R programming courses for Data Scientists and ML engineers to start with.
2. Data Pre-Processing & Visualization
After you learn the python language, you need to know some packages used in machine learning which are data visualization tools and libraries. It will help you understand the problem before creating your machine learning model and pre-processing your data.
2.1. Numpy:
This is one of the best libraries for mathematical calculation and is used a lot when working with arrays and changing the shape of your data. It’s also one of the essential Python library every Machine Learning Engineer and Data Scientist should learn. If you need resources, you can checkout this free NumPy courses and paid NumPy courses to start with.
2.2. Pandas:
This is again an important Python library and it is also the most used library for data processing and importing your data from different resources and has a lot of active community contributions, which made it a necessity to learn if you are a data scientist or machine learning engineer.
If you want to learn Pandas in 2024, you can check out these best Pandas online courses for Data Scientist and Machine Learning engineer.
And, if you don’t mind learning from free resources then you can also see this list of free Pandas online courses to start with.
2.3. Matplotlib:
This is a data visualization Python library used among data scientists, data analysts, and machine learning engineers who can make handy charts by typing simple commands. As a Python developer and aspiring Data Scientist and ML Engineer you should learn Matplotlib in 2024 along with NumPy and Pandas. If you need resources, you can see this list of best Matplotlib courses and tutorials to start with.
3. Machine Learning Types
After you are comfortable with using the python language, data processing, and data visualization, it is time to start spending your time on machine learning, and you need to learn first the three different types of machine learning:
3.1. Supervised Learning
This is a type of machine learning that uses labeled data to train the model and then produces an output when you feed it later with unseen data. This kind of learning also has two types which are:
3.1.1. Classification
This kind of learning is used to classify between labels such as spam email or not spam email or to separate cats pictures from dog pictures.
3.1.2. Regression:
This kind of learning is used to predict continuous data, such as the stock market price or the house prices based on labeled data.
3.2. Unsupervised Learning
This is another type of machine learning similar to supervised learning, but it will be trained on unlabeled data. Hence, it needs to identify a pattern in the data and separate them. This kind of learning also has two types:
3.2.1. Clustering: is used to divide the data points population into several groups, and the data in the same groups are very similar.
3.2.2. Association: is used to check the dependency of an item in a group of data to another item in another item, meaning it tries to find a relation between variables in the same dataset.
3.3. Reinforcement Learning:
This is the last type of machine learning in which we make the agent try to find its path it should take in a specific situation and learn through trial and error. This kind of learning also has two types:
3.3.1. Positive: is a reward for doing something good and also defined when an event or something occurs due to a particular agent behavior.
3.3.2. Negative: This is described as strengthening behavior because a negative or lousy condition that happens due to an unfavorable condition should be avoided or stopped.
There are a lot of machine learning resources available online, and you could follow the roadmap that I’ve mentioned above or just take a course that will help you learn them. Still, you can also take this course called Machine Learning from Coursera, which will help you understand more about this industry.
Conclusion
That’s all in this The 2024 Machine Learning Engineer RoadMap. Being a machine learning engineer is a promising career. It has a lot of opportunities to either find a job or even create your own business that uses machine learning, but you need to be patient in learning this topic.
I have tried to share as many resources as possible but if you ever need a resource to learn anything related to Machine Learning then feel free to ask.
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