So what is the real picture?
Machine Learning is actually one of the essential skills for working professionals to have. Regardless of what your background is and which field you work in, you will be able to advance in your career if you invest in learning Machine Learning techniques. With machine learning, you can revolutionize industries like healthcare, finance, technology, and more. It’s a skill that’s in high demand, leading to exciting career opportunities and the chance to work on cutting-edge projects. If you want to contribute to your job at this level, it is very important to be skilled in Machine Learning and data science.
Learning a new skill is difficult at first, but it becomes simpler after you grasp the fundamentals. It is simpler to study Machine Learning if you understand the requirements. Math, statistics, calculus, algebra, probability, Python, C++, Java, data structure, and many more subjects are required. It’s a bonus if you’re passionate about Machine Learning since it will help you overcome any obstacles and stay motivated. Simply study about them and reinforce your fundamentals and ML principles; otherwise, you will struggle to master ML.
Since so many people are talking about Machine Learning and learning Machine Learning, it cannot be an impossible task. On the other hand, it is also true that many people drop out of Machine Learning courses mid-way or are not able to complete their learning plan in Machine Learning because they get overwhelmed or find it too difficult. But Machine Learning is nothing to be afraid of. With the right approach and commitment, you can become an Machine Learning expert. Certainly, it is an entirely quantitative subject and is implemented using programming languages so you need to be thorough with both the theory and the coding side of it. But, the reason why I would still say that Machine Learning is actually a very manageable subject to learn is that it is closely linked with industry knowledge. You will have the complete context of how the various Machine Learning models work, how they are applied, and how to make sense of them when you can relate it at every step to your own background and experience.
How to learn Machine Learning from scratch
So let us break down the approach to learning Machine Learning into simple steps:
Step 1: Cover the theory
To begin, learn the fundamentals of machine learning.This refers to the various statistical techniques and concepts which form the base of data science and Machine Learning. Start with descriptive statistics and elementary mathematical techniques like linear algebra, analytical geometry, etc. Then you can slowly build your way up to inferential statistics and predictive models like regression, clustering, classification, etc.. These topics may be learned through many online platforms such as Udemy, Learnbay, Coursera, and others.
Step 2: Learn to program
Only studying theory will not help you understand anything. In practice, learning anything in machine learning is easy. Once you have learned these concepts thoroughly you should proceed with the second step which is to learn how to apply these ideas to an actual dataset using a coding platform like Python or R. This would also include learning how to preprocess data in SQL and visualize outcomes of the Machine Learning models in Tableau.
Making projects allows you to understand everything in-depth, allowing you to be industry-ready. However, you have to keep in mind that you will not be able to become an expert in programming in one shot. It needs constant practice and an open mind that helps you learn from your mistakes. With this mindset, you can certainly become comfortable with coding for Machine Learning.
Step 3: Match it with industry knowledge
This is the most important step, especially if you are keen on learning Machine Learning skills from a job perspective. It is naturally easy for us to learn quantitative techniques once we have already used them to solve some problems. It is the same with Machine Learning. It also allows you to bridge the gap between technical expertise and the real-world problems your industry faces. Your journey of learning Machine Learning will become very smooth if you complement it with project-based learning and domain specialization.
Why should you focus on project-based learning?
Simply knowing how to run some codes for some Machine Learning models is not enough to train you to face actual problems in the field. Project-based learning offers hands-on experience that reinforces theoretical knowledge. Projects provide a practical context for applying algorithms, data preprocessing, and model evaluation. By working on projects, you develop problem-solving skills, learn to handle real-world data complexities, and understand the iterative nature of model development. This can show your ability to choose appropriate algorithms, fine-tune hyperparameters, and address actual challenges that textbooks might not cover, which can impresss any recruiter.
Why should you focus on domain specialization?
Focusing on domain specialization in machine learning is like becoming a pro in a certain field. It helps you make super useful stuff that fits that field perfectly. You get to know all the cool tricks and special things about that field’s data and problems. This means your solutions are not just smart but also really helpful. You’ll be the go-to person for that field’s challenges and can explain things easily to everyone, both techy and non-techy folks. So, getting comfy with a specific area and mixing it with machine learning skills makes you a problem-solving superhero in that world.
In order to have a good learning experience with Machine Learning, make sure you choose the right Machine Learning course. It can also be learned through a data science certification as that encompasses both foundational data science concepts and Machine Learning modeling.
You can consider the following institutes for this:
1. Supervised Machine Learning: Regression and Classification – Coursera
In the first course of the Machine Learning Specialization, you will:
• Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.
• Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression
The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications.
This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.
This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012.
It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)
By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.
2. Harvard University: Data Science: Machine Learning – edX
In this course, you will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system.
This course is part of Data Science Professional Certificate Program, a foundational online program created in collaboration between Havard University and edX. You will learn about training data, and how to use a set of data to discover potentially predictive relationships. As you build the movie recommendation system, you will learn how to train algorithms using training data so you can predict the outcome for future datasets. You will also learn about overtraining and techniques to avoid it such as cross-validation. All of these skills are fundamental to machine learning.
You will learn about the basics of machine learning, how to perform cross-validation to avoid overtraining, several popular machine learning algorithms, how to build a recommendation system and get to know regularization.
This Specialization is taught by Rafael Irizarry, a professor of Biosatistics at Havard University.
You can participate in this course for free, but if you want to have a certificate with this track to advance your career, you need to pay $102 USD.
I hope you are convinced that if you are in the right program, then learning Machine Learning is really not difficult. Once you pass the beginning phase and understand the fundamental, I believe that your journey will be much easier.
Thank you.