Artificial Intelligence, Machine Learning, and Data Science were the hot buzzwords that revolved around the past decade especially in the last few years. Following is a machine learning tutorial & resource guide that will help you start with ML concepts.
With the emergence of these technologies, many people around the globe are trying to get into this field from engineers to entrepreneurs
But What is Machine Learning?
Machine learning is the study of computer algorithms that improve automatically through experience and by the use of data, often discuss as subsection of Artificial Intelligence.
Though this term is prevalent for more than half a century, it has been gaining momentum only since the last decade due to the rise of automation in the tech industry and also recent reports show these jobs have a high growth factor and pay as well.
The above image from indeed 2019 shows that ML Engineer has a highest pay
The most important thing to keep in mind with learning these technologies is, these technologies are evolving rapidly. Still, so many colleges don’t have these technologies in their curriculum.
So in this article I will walk you through on how to master these technologies as of now in 2021 which will not only help you in short term but also for a long term growth
Before starting into Machine Learning tutorial make sure you know about these topics
- Roles in Machine Learning
- Skills required for Machine Learning Jobs
- Resources to learn Machine Learning
- Skills to stand out from crowd
Let’s dive in
Different Roles in Machine Learning
Generally the words Artificial Intelligence, Machine Learning and Data Science have been used Interchangeably, so the job descriptions might contain very high number of skills which you might not even heard of before, so to begin start learning make sure you know what position you want to apply for and what work you would like to do, which would be helpful later when you start applying for jobs
Below are the major positions in Industry which needs Machine Learning Technologies
1. Data Analyst
This role is closely related to the Data Scientist role but involves very less Machine Learning but more of a Data Analysis and Data cleaning.
As a Data Analyst, your job is to perform ETL(Extract, Transform, Load) tasks from Data Warehouses and Relational Databases, Create Interactive dashboards, perform statistical tests.
The essential skill required for this position is SQL,Python,Tableau/PowerBi and Statistics
2. Data Scientist
This role requires both of Machine Learning and Data Analytics knowledge, Data Scientist performs analytics to drive better decisions which will help in Modelling
As a Data Scientist, your job is to perform predictive analytics which will help the user needs and also some analytics of the dataset that will help to drive proper business decisions
The essential skills required for this position is Python, SQL, Machine Learning Libraries and Statistics
3. Machine Learning Engineer
ML Engineer is a person who is responsible for developing Machine Learning systems and infrastructure required for Machine Learning
As an ML Engineer your job is to develop Machine Learning algorithms, deploy and monitor Machine Learning models
The essential skills required is Python with Tensorflow/Pytorch, Java/Scala, C, Go, Docker, Jenkins, etc
4. Machine Learning Research Scientist
Research Scientist are the people who work continuously on researching new Machine Learning techniques that will help in increasing efficiency of ML Algorithms, This role is very different from other roles since it requires more math skills than software engineering skills
As a Research Scientist, your job is to research new and existing machine learning techniques and publish State of the Art methods, later used in Industry and Academia
The essential knowledge required is Python with ML libraries and Heavy Mathematics
While having more roles can be confusing, these help us to clarify the things and allow people to work upon their things.
These roles and descriptions are very general and some companies might have a different type of role as well, some startups might hire a person for a data scientist role and the work would be end to end, So keep in mind that roles and titles don’t matter but skills and deliverables are!!
Skills Required for Machine Learning
With the diversity of roles in Machine Learning, it can be overwhelming to learn Machine Learning. There are hundreds of courses available online to learn Machine Learning. In this section, we will look at what are all the skills that are required for a Machine Learning tutorial and where to learn them.
Generally the skills required for ML can be divided into two,
- Programming skills
- Math and Statistics skills
While you can skip Math at beginning you must not skip programming since most of the work would be on programming
When it comes to programming skills that are required for Machine Learning, the essential programming languages required are Python and R. Through Python vs R is a debatable topic.
I would prefer Python because of its community support and its ecosystem of libraries, The most important Python libraries that are used in Machine Learning are Numpy, Pandas, Matplotlib, Scikit-Learn, Tensorflow, Pytorch and the list goes on
2. Maths and Statistics
Maths is very essential in Machine Learning, Machine Learning Algorithms are nothing but a combination of Linear Algebra formulas though you can skip Machine Learning at first, It is very essential to learn Math, The math skills that are required are Linear Algebra, Calculus and Probability and Statistics.
So How to learn all these, which one to learn first!? Should I study Programming or Math first!?
Though there are multiples of roadmaps to learn Machine Learning, I would highly encourage you to create your own path. Learning Programming and Math side by side will help you a lot to improve skills and save time. Below are some resources that will help you to learn these skills
Essential Resources For Programming Skills
Following are the important programs that you can employ to master the skill of programming and upgrade your knowledge in machine learning.
Python is an essential language for learning ML, to start with Python you can try out Guvi’s advance Python course. Get your hands dirty on essential concepts of programming such as OOP, try-catch exceptions as well.
2. Pandas, Numpy, Matplotlib and Scikit-learn
These are the essential libraries that are used in any data science project. To learn these libraries I would highly recommend taking Kaggle’s micro-courses. These courses are short, concise, and upto the point this will help you to get started with Machine Learning Tutorial.
3. Tensorflow, Pytorch
Tensorflow and Pytorch are the two major frameworks that is highly used in deep learning community, to get started with Tensorflow you can check out this coursera course on tensorflow certification which provides you hands on tensorflow practices, for learning pytorch its documentation is a best place to start with, It contains tutorials on all levels from beginner to intermediate
4. Git, API and Cloud
If I had written this blog before 2020, there won’t be this section but things have changed in this field and companies often require people who are more end to end, i.e who can do things starting from fetching datasets to deploying the model, Git and Github are version control platforms that will help to track changes in your codebase
Nowadays Cloud technologies are highly used in deploying and monitoring Machine Learning models.
The major cloud providers in tech are AWS from Amazon, GCP from Google and Azure from Microsoft.
To start off choose anyone from above and each website has its own tutorials which are better than any online courses.
Essential Resources For Mathematics and Statistics
Following are the resources that can help you build your knowledge base in mathematics and statistics which is the foundation of machine learning.
1. Get Your Prerequisites Right
As I said earlier, if you don’t know the prerequisites of Math used in Machine Learning, you can’t understand the high-level stuff, To start with learning high school maths such as Linear Algebra, Calculus and Statistics, To get Started I highly recommend 3blue1brown channel on Youtube which teaches you the most essential mathematics in a very intuitive way with graphics and animations, For learning statistics check out Joshua Starmer’s channel, Statquest the concepts provided in the channel are more than enough to understand the essentials.
Machine Learning Course by Andrew NG
This is one of the fundamental course in Machine Learning, taught by Andrew Ng, it is a watered-down version of CS-229, which is taught at Stanford University, you can access this course both on Youtube and Coursera, It is a great course and it teaches you the basics of Machine Learning — Regression, classification, various ML algorithms, etc…Though this course was released in 2011, it is one of the greatest courses of all time.
2. Deep Learning Specialization
In this five-course specialization taught by Andrew Ng, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. The course material will cover all the maths behind deep learning.
The programming assignments are structured from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow
Skills to get a job in Machine Learning
The field of Software Engineering, particularly in Machine Learning is different from any other fields since the competition is getting increased daily and the new technologies are also emerging, you need to stand out from the crowd, The certificates from the online courses won’t have any impact on getting a job, You need to have a special Below are some tips and tricks that will help you to stand out from crowd and get a job
1. Personal Projects
Projects are the most important thing when it comes to evaluating your skills, make sure you do any one project which involves evaluating your skill, the project should not be taken from existing github repos or kaggle kernels, do it on your own though you can refer to those existing methodologies make sure you know what you do!!
2. Participate in Competitions
Competitions are the best way to evaluate our skills. External Assessments can help us harness the skills required.
Moreover, It also helps you with networking opportunities where you meet new people who have similar interests as yours!
Below are some of the famous competition websites for Machine Learning,
Apart from this I encourage you to participate on other portals that you find intriguing.
These are the most important things that you must know before starting to learn Machine Learning.
Make sure to give more importance to your personal projects and improving your programming skills and remember this is a long journey, so take some breaks and have some faith in yourself, Have fun!.