How to Learn Machine Learning
Although the concept may seem advanced, we have all experienced machine learning. For example, when you finish watching a TV series online and start seeing advertisements for merchandise from that series. This is a basic example of how machine learning affects our daily lives. Your browser learns your preferences and personalizes your Internet surfing accordingly.
This is only a very basic type of machine learning. The possible applications for this technology are extensive. It is utilized in nearly every app and service that we frequent and plays a major role in making our lives more efficient.
If you want to become a machine learning engineer but don’t know where to start, then let us help you out. Below, we have compiled a detailed guide to answer your questions about what machine learning is, and how you can pursue a career in the field.
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What Is Machine Learning?
Machine learning is a subset of artificial intelligence (AI) that uses mathematical models to find solutions. The main focus of machine learning is on developing computer programs that can use data to learn on their own.
This means the machine is behaving as a human would. When fed data and figures in the form of observations and real-world examples, the machine will learn autonomously over time.
The goal of this field is to teach machines through data and experience so that they can be of greater use to humans.
Different Types of Machine Learning
Machine learning is evolving quickly. New techniques and methods are introduced often and sometimes result in entirely new machine learning processes.
These processes fit into three areas: supervised machine learning, unsupervised machine learning, and reinforcement machine learning.
Supervised Machine Learning
Supervised learning is where a model learns from past data that is input into the program, then predicts new outcomes in accordance with that data. The dataset is split into training and test data. The training data is used to teach the program, then the test data is used to help the machine learn.
This type of machine learning is used to classify data.
Unsupervised Machine Learning
Unlike supervised machine learning, in unsupervised machine learning, there is no output that can be used to correct the model. In this technique, the machine trains itself based on the initial data provided.
Reinforcement Machine Learning
Reinforcement machine learning is a technique where an algorithm allows a machine and software to automatically make choices. It uses unlabelled datasets and has no output, so the machine must perform tasks and learn strictly based on observation.
What Is a Machine Learning Engineer?
The main job of a machine learning engineer is to create a model that can train itself in line with the data set provided. A professional in this field must have excellent skills in statistics and programming to effectively fulfill their duties.
The ultimate goal of a machine learning engineer is to create successful self-learning applications. Machine learning engineers should have a strong understanding of computer science and possess exceptional mathematical skills.
The job of a machine learning engineer is not very different from that of a data scientist. Both have to manage a large amount of data and make models to replicate specific tasks. The difference is that a machine learning engineer will then use their digital data sets to train a computer to perform a function.
Duties of a Machine Learning Engineer
The roles and responsibilities of a machine learning engineer are primarily confined to training or retraining models. However, they do have other duties, several of which are listed below.
- Experimenting with and implementing machine learning algorithms to devise new techniques to improve the efficiency of systems.
- Developing and modifying machine learning applications based on clients’ requests.
- Running machine learning tests and trials.
- Selecting suitable methods for representing datasets.
- Keeping up with developments in AI.
- Transforming unstructured data into useful datasets to train models by using techniques like text-to-speech conversion or image auto-tagging.
- Creating reports on the processes used in training machines.
Machine Learning Engineer Job Opportunities
Accurately analyzing data is an important key to success for most modern businesses. But processing large amounts of data manually is nearly impossible. For an organization to utilize its data to its full potential, it must hire machine learning engineers.
Once you have the skills to become a machine learning engineer, you can seek employment at whatever company interests you the most. You can choose to seek work at a start-up, or help advance machine learning at a higher level by working for a major tech company such as Google or Apple.
Machine Learning Engineer Salary
According to Payscale, the average salary of a machine learning engineer is $112,448 per year, depending on their skills and experience. A basic machine learning engineer is likely to be paid a starting salary of around $75,000 per year.
Learning Machine Learning
Many people dread machine learning because of the math involved, but in reality, it is not too difficult. Only high school algebra and some calculus are needed.
Some education institutions offer math courses specifically tailored to machine learning. With the amount of free and paid online resources out there, you can learn the basics of machine learning in a matter of months.
How Long Does It Take to Learn Machine Learning Engineering?
As is in any job, the learning process never ends. The amount of time it takes will also depend on how you are learning. But to nail down the basics of machine learning, such as linear regression algorithms, logistic regression, and neural networks, it will take three months.
How to Become a Machine Learning Engineer: Step-by-Step
A machine learning engineer needs to focus on the foundational concepts of the study to fully grasp the mediocre command over this subject. Step by Step approach to learning machine learning is given:
- Learn Python. To get started in machine learning, you should study Python and learn basic software development skills.
- Study data science. Before you get into the specifics of machine learning, brush up on your knowledge of statistics and data science.
- Get familiar with machine learning frameworks. Now it’s time to start exploring machine learning frameworks and theory.
- Experiment. At this stage, you can start experimenting by testing your own datasets.
- Learn the big data framework. Once you are comfortable with some of your basic experiments, you can expand your skills by learning the big data framework.
- Study deep learning. Finally, you will be ready to immerse yourself in deep learning frameworks, which are used to help build models more quickly.
Machine Learning Certifications
Certificates can make all the difference in advancing your career as a machine learning engineer. Many of them require that you have a few years of experience with developing machine learning applications on each respective platform in order to qualify. Some of the best machine learning certifications are mentioned below.
Microsoft Azure provides associate-level certifications for AI engineers and data scientists. This certification is for applicants with both technical and non-technical backgrounds who want to demonstrate their understanding of common machine learning and AI using Azure. The only prerequisite for this course is general programming knowledge.
Amazon Web Services
This Amazon Web Services certified machine learning certification is aimed at individuals who work in data science. With this certificate, you will learn to create, implement, deploy, modify, and maintain machine learning systems to meet a client’s requirements.
This certification is more thorough than the one provided by Azure. It allows applicants to explore machine learning in-depth and has specific paths for both machine learning and part analytics.
Online Machine Learning Resources
There are many resources such as blogs, online guides, courses, and tutorials available to anyone who wants to learn about machine learning. Choose the educational mode that you prefer, and explore some of the options below.
This Coursera course offered via Stanford University is the best way to get started in machine learning. It will cover all the basics of machine learning and requires little to no prior knowledge of computer programming or difficult math concepts. In this course, you will also learn the programming language Octave, which is an industry-standard.
This is the quintessential machine learning text. Written by Microsoft research lab director Christopher Bishop, this book will give you a thorough understanding of the basic pattern recognition concepts that laid the foundation for machine learning, as well as groundbreaking developments in the field.
This is a textbook and accompanying lecture series by Stanford University professors Trevor Hastie and Rob Tibshirani. It covers all aspects of machine learning through 15 hours of detailed videos and text on topics including statistical learning, linear regression, and more detailed machine learning methods.
Mathematics for Machine Learning will get you up to speed on all the basics of machine learning. The book is currently available for free on Github. The math concepts explained in the text include linear algebra, analytic geometry, and vector calculus, among others.
Should You Learn Machine Learning?
You should study machine learning if you are interested in how data can be used to boost human productivity. With every passing year, the tools we use every day are becoming more automated, thanks in part to developments in machine learning.
If you have a technical background and want to breathe new life into your career, then learning machine learning might be the right move. The growing demand for experts in this field is evident, as they are an important asset to any business that is looking to stay ahead of its competition in terms of technology. Plus, this field will provide you with many opportunities for career growth.