Machine learning is a fast-changing sector. Are you interested in learning about practical projects? Are you self-motivated and driven? Will you seek and see beyond objectives? If so, you’re going to love machine learning. You will be able to solve some interesting problems, develop a beneficial career potential with fascinating algorithms. If you aim to become a data scientist, use ML algorithms to improve your business analysis toolbox, or add state-of-the-art skills, you can acquire applied master-learning skills far more quickly than you would expect.
However, there is still a little bit of uncertainty about Machine Learning and how to begin learning it? This article discusses the fundamentals of machine learning and how to become a full-fledged ML engineer, ultimately. Let’s start right now.
Now know what Machine Learning is?
Machine learning includes teaching computers how to learn from data to make decisions or predictions. The computer must be able to learn to recognize patterns without specifically programmed for real machine learning. Sitting at an intersection of statistics and computer science, it can still wear numerous masks. You may even hear a couple of other names.
Why do we need Machine Learning?
Data is growing every day, and you cannot understand all data at more incredible speed and accuracy. More than 80% of data, including audios, images, photographs, documents, and graphs, is unstructured. For human brains, it is difficult to find patterns in planet earth data. The data is extensive, the time to measure will increase, and machine learning is applied in a minimal time frame to support people with factual data.
Scope of Machine Learning
And thus the machine learning started! Machine learning is one of (if not the most!) most common career options in modern times. Significant businesses are currently investing in ML, and we see it transforming the planet. Indeed, machine learning engineer is the best job in 2019 with a growth of 344% and an estimated annual gross salary of $146,085 pa.
Best ways to begin your career in Machine Learning path
Step 1 – Understand the requirements
If you’re brilliant, you can start ML right away, but in general, you need to know those criteria, like linear algebra, multivariate calculus, statistics, and Python. And never be afraid if you don’t remember them! In these topics, you don’t need a Ph.D. degree, but you need a superficial understanding.
- Know Linear Algebra and Multivariate Calculus: Machine learning applies to linear algebra and multivariate calculus. However, your position as a data scientist depends on how much you need them. You would not be so dependent on mathematics, as many popular libraries are open when you concentrate more on application-intensive machine learning.
- Learn statistics: in machine learning, data plays an important role. As an ML professional, about 80 percent of your time is spent gathering and cleaning data. And statistics is a field in which data is collected, processed, and submitted. And you don’t have to master it, surprisingly.
- Learn Python: Some people tend to avoid linear algebra, multivariate calculus, and stats and learn them as trial and error go along. But you cannot skip learning Python.
Step 2 – Learn Various ML Concepts
You may now continue to learn ML after you are done with the prerequisites. Best, begin with the basics and then proceed to the more complex stuff. Some of the fundamental concepts in ML are:
- Machine Learning Terminology: Get familiar with model, feature, target, training, prediction.
- Machine Learning Types: Also know these terms – Supervised Learning, Supervised Learning, Semi-supervised Learning, and Reinforcement Learning.
Step 3 – How do I learn Machine Learning?
In reality, data collection, integration, cleaning, and preprocessing are the most time-consuming components in ML. So practice with this because high-quality data is needed, but large amounts of data are often dirty. That’s where the most significant part of your time goes. Learn different models and get practice more on real datasets. It helps you to get an insight into which models in various circumstances are suitable. Alongside these measures, the interpretation of the results obtained using multiple models is equally significant. It is easier to do when you understand different tuning parameters and regularisation methods on various models.
Step 4 – Choose Machine Learning courses
If taking a course is your style, you’re still in luck. Machine Learning is one of the best and most fascinating and fast-paced computer science areas, with deep origins in statistics. There is an infinite supply of industries and applications where you can use machine learning to make them more efficient and smarter.
Your aim should be to increase ML skills as much as possible. You can begin to learn by combining online courses and tutorials with the ML competition. Another solution is to get to a data science boot camp to accelerate the learning process if you have the time and the resources. After many years of following e-learning platforms and enrolling in multiple ML courses from various platforms, Coursera, Edx, Simplilearn, Udemy, and Udacity are the best machine learning courses currently available.
Various tools are available to begin learning ML techniques. I suggest you choose one of 2 ways according to your learning style:
- Option 1: You can take Simplilearn’s AI and Machine Learning courses if you want to learn in small steps and need more handheld: this is a good and easy to understand course for beginners. It’s impressive to make the challenging concepts so smoothly. This course includes both simple and advanced algorithms.
- Option 2: If you like to face challenges and fight challenging tasks, you can learn ML courses from Professors. They offer an excellent way of coping with the ideas behind machine learning. It also requires more knowledge of programming and is, in this sense, more advanced. This program is designed full of homework.
The field of machine learning is broad and rich. Almost every industry has applications. Machine learning makes learning and exploration extremely enjoyable and exciting. Now is the time to evaluate your learning skills and the right path to enter this thriving ML environment.