Step by Step Guide to Get Started with Machine Learning

In this growing world, technical achievements play a vital role. We must have heard that technology has quickly solved some of the old unsolved problems. Machine Learning is one such technology capable of solving many problems across broader verticals. ML is revolutionizing industries by helping doctors in the medical sector, armies in defense, farmers in agriculture, scientists in their innovation, and many more.

Machine Learning and Artificial Intelligence are some of the hottest topics that can ensure a brighter future for any individual in the upcoming decade. As per Indeed's post, Machine Learning Engineer is the top-rated job among the best jobs of 2022. The average salary of an ML engineer is $146,085, which is significantly higher.

But technologies like AI and ML are new in industries; hence becoming an engineer in AI/ML is not so trivial. Taking the first steps in this field is challenging not because fewer resources are available over the internet but because most resources lack proper structure, and initial learners give up here. We need the requirement-centric steps to advance in this domain. So it's always necessary for us to first find the proper guidance that is structured so that it can keep us motivated throughout our journey.

In this article, we will be sharing six definite learning steps, followed by several industry professionals working in the ML industry. These steps may differ from conventional methods, but we can assure you that these steps work.

Step 0: Learn the Basics of Python

Python is the most commonly used language in machine learning and deep learning. More than 60% of professionals working in the ML domain prefer Python to develop their AI/ML solutions. Hence we recommend learners get familiar with the basics of the python programming language. To learn Python, we can pursue either of the three options :

Note: There are other programming languages like R, Scala, and Julia, but we are emphasizing Python because of six significant reasons shown in the image below.

6 reasons why we should go with python for machine learning

Step 1: Try to find answers to some basic questions related to Machine Learning.

To learn any new technology, we must know the answers to some basic questions.

  • What is that technology?
  • When that technology started?
  • Why that technology even exists?
  • Where can we use that technology?
  • How does that technology work?
  • What are the scopes of that technology in the coming decade?

We can find the answers to the first four questions in two blogs presented at EnjoyAlgorithms Comparison of ML with traditional programming and AI vs. ML. The fifth question is answered in the "How exactly does Machine Learns?" with an example. The more we dig, the more enriched our learning will be. This step is essential if we are interested in making our career in this domain.

Step 2: Get yourself familiar with the Basics of Mathematics required for Machine Learning

Note: One can keep this step parallel to later steps, as starting from maths may be time-consuming for initial learners. We believe in the philosophy of "Learning by Doing"; "ence we recommend learning the essential things while you encounter them in your journey.

To become an expert in Machine Learning, we should know the basics of some most frequently used maths inside Machine Learning. Concepts like Linear Algebra, Probability, Statistics, and Calculus can be ML'ML'sndamental building blocks. We need not master these concepts as only some parts of them will be helpful in problem-solving. Although we will be using in-built libraries in Python like Numpy, Pandas, etc., one should learn these concepts to understand what is happening inside.

Below are the links for the free courses which will introduce you with,

Python libraries that are very useful and must to explore are :

These libraries can implement complex mathematical equations within a few lines of code. Not only this, these libraries optimize the calculations so that our algorithms perform more than 10X faster.

Step 3: Playing with Data

We have divided this step into two parts where both the parts work in parallel,

Part 1: Get yourself familiar with the concept of Data, Analysis, Manipulation, Filtering, and Visualisation

This step is one of the most crucial steps in the field of Machine Learning. 
There is one famous saying,

Ability of extracting the useful information from the given dataset is the key to differentiate between an average and better Machine Learning engineer.

The machine learning model works best with pre-processed and meaningful data. In this step, we should also explore some python libraries specially designed to help us in the pre-processing steps.

Python Libraries that are helpful for this step would be :

We can go through some online courses to learn more about data-preprocessing things. Courses that we can have a look at to master the data pre-processing steps are :

Please keep in mind that the depth of this field is infinite. We will look at selective things which are required to solve our needs in the field of machine learning.

Part 2: Choose any open source dataset and apply the learnings of part 1 in parallel

As we said, there is no point in learning things if we do not practice that, So we recommend finding some open-source datasets that we can find here. One can download any dataset and try to implement the learnings of part 1. 

Task: For example, download the COVID-19 dataset, clean it, and find the geographical region with the maximum number of Covid cases.

Step 4: Kickstart ML Model Development

This step also includes two parts, where part 1 is again focused on learning, and part 2 is focused on testing the part 1 learning.

Part 1: Introduction to Machine Learning

We are ready to start our journey toward machine learning at this step. We can start going through any introductory course.

There are many other resources that we can find on the internet. Still, as we focus on the language python because of the tremendous community support, we recommend learning ML with scikit-learn. The benefits of that are: 

  1. Easy-to-use code modules
  2. In-built implementations of famous algorithms
  3. Tunable hyperparameter support for every algorithm
  4. Faster execution.

We should always try to be in the depth of the concept and find the answer to any idea or algorithm's What, Why, and How. Although the use of libraries or framework will block the authentic experience of implementing ml algorithms from scratch, our motive is not to "reinvent the wheel". It is advisable to understand the working of algorithms, but we can use frameworks while building them. 

Part 2: Decide a Problem Statement that we want to solve using Machine Learning

This step is a must if we want to be an expert in the area of Machine Learning. We have seen many professionals with good certificates who cannot solve problems through ML.
Hence we strongly recommend solving at least one problem statement available in the free domain. This would give us enough confidence to consider this tech a solution provider. It'It'st required to complete the entire course first and then try the problem. We can pick any problem of simple complexity and solve it step by step with the course going on in parallel.

One can find many such problem statements in Modules 5, 6, and 7 of the EnjoyAlgorithms ML course.

After following the above steps, one can become a sound ML engineer with hands-on experience. We can start with some moderate complexity problems and then the harder ones. If we want to become passionate about it, we can collect research papers on Machine Learning and try to implement them. It will make our experience better and make us confident enough.

complete process of machine learning works

The overall process for the machine learning model development can be seen in the above image. We could remove model deployment from the picture, but it's always advisable to build something entirely and offer it as a complete solution.

Step 5: Learn Deep-learning and Neural Networks

Repeat the same strategy of Step 4 and follow the philosophy of Learning by Doing.

Some good Deep-Learning course recommendations:

We also have a completely different way of learning the Deep-learning concept, which goes through the above courses. We would share that pathway in a separate blog.

Next Blog: How exactly Machines Learn?

Previous Blog: AI vs ML

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