According to various online surveys, the role of a Machine Learning Engineer was rated as the top job for 2022, with a substantial average salary. However, starting Machine Learning can be challenging for beginners due to the lack of proper guidance and quality resources. This is not because of a shortage of resources online, but rather, the large number of available resources lack clear structure and depth.
It is essential to have guidance and well-designed learning plan to start Machine Learning journey. In this article, we have outlined some critical steps to get started with Machine Learning.
Python is the most widely used language in machine learning and deep learning. Over 60% of professionals in machine learning prefer to use Python. So we first recommend learners to get familiar with the basics of Python programming.
Some tips to get started with learning Python for Machine Learning:
To learn Python, you can follow these resources:
Note: There are other programming languages such as R, Scala, and Julia, however, we are focusing on Python due to six significant reasons highlighted in the image below.
Developing a long-term motivation to learn machine learning is crucial. Here are some tips:
Before starting the journey, you should also explore these concepts:
Many Machine Learning algorithms and models rely on mathematical concepts and principles. These concepts work as the backbone for creating and training models, making predictions, and evaluating their precision.
So, it is crucial to have an understanding of mathematical concepts such as linear algebra, calculus, probability, and statistics. This knowledge will help you understand how ML algorithms work and make informed decisions on which algorithms to use and how to improve them. While it is not required to be an expert in all these concepts, having a strong foundation in some of them will help in starting your Machine Learning journey.
We have divided this step into two parts where both the parts work in parallel.
This step is one of the most crucial steps in Machine Learning. There is one famous saying: "Ability to extract useful information from the given dataset is the key to differentiating between an average and better Machine Learning engineer".
From another perspective, the performance of a machine learning model is heavily dependent on the quality of input data. In the pre-processing step, it's important to clean and transform the data into a format that can be easily used by the model. Additionally, there are several libraries in Python specifically designed to assist with pre-processing tasks. By utilizing these tools, you can streamline pre-processing workflow and ensure that your model has the best possible input data.
Python libraries that are helpful for this step would be:
You can go through some online courses to learn more about data-preprocessing things. Here are courses that you can have a look at to master the data pre-processing steps.
Please keep in mind that the depth of this field is infinite. We should look at selective things which are required to solve our needs in the field of machine learning.
Practice and hands-on with data are crucial for understanding machine learning concepts. So we recommend learners to find open-source datasets to work with. A great resource for finding datasets is Kaggle. Here is a task for you: Download COVID-19 dataset and apply what you've learned in Part 1. The task would be to clean the dataset and determine the geographical region with the highest number of Covid cases.
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.
Now we have an understanding of basic concepts, it's time to start our journey in machine learning. There are many resources available online, including introductory courses, to help us get started.
Since Python is the preferred language for machine learning due to its strong community support, we recommend learning with the scikit-learn library. Scikit-learn provides many benefits, including:
While it's important to understand the underlying concepts and workings of each algorithm, there's no need to reinvent the wheel. Instead, we can utilize frameworks to build our models while still having a good understanding of the algorithms. It's crucial to have a thorough understanding of each concept and be able to answer the questions of "what," "why," and "how."
This step is vital if you aspire to become an expert in Machine Learning. The truth is: A good understanding of concepts is not sufficient to ensure success in solving real-world problems using machine learning. To build confidence and proficiency, it's important to implement at least one problem statement available in the public domain. You can begin with simple problems and gradually progress to more complicated ones, as this will help you grasp the concepts better through practice.
You can find numerous problem statements in Modules 5, 6, and 7 of the EnjoyAlgorithms ML course. This course offers a comprehensive guide to implementing ML projects. To further enhance your skills, you can study research papers and try to implement them. This will broaden your understanding and boost your confidence.
Repeat the same strategy of Step 4 and follow the philosophy of learning by doing. Deep-Learning course recommendations: Deep-learning Specialization by Andrew Ng
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.
Enjoy machine learning, Enjoy algorithms!