Considered a subspecialty of artificial intelligence, machine learning is closely associated with programming computations, statistics, and mathematics. If you need to automate email filtering or extract information from images, machine learning will get the job done. Your timeline for becoming proficient in machine learning is dependent on your current level of expertise in data mining, modeling, and computer programming.
It will take 3 months to 6 years based on your current education and experience in programming, statistics, and data science to learn machine learning. As a subset of AI, machine learning makes predictions and decisions based on data inputs, execution of algorithms, and feedback loops.
Consequently, this article describes machine learning and the areas of expertise that create this specialty. You will also understand the different types of coursework that can be taken to fill in your gaps. Ultimately, read on for a timeline for learning to be developed and a quick look at careers in the field.
Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!
Table of Contents
What Is Machine Learning?
Often described as a subset of Artificial Intelligence (AI), machine learning is defined in Merriam-Webster as “the process by which a computer is able to improve its own performance (as in analyzing image files) by continuously incorporating new data into an existing statistical model.”
While artificial intelligence is a broad field of computer science in which computers simulate human behavior. AI is difficult to define partly because the standard definition is derived from science fiction, as seen in the movies and literature, which changes perception. Also, having evolved over time, AI contains new subspecialties that are still evolving.
- Deep learning
- Machine learning
- Computer vision
- Natural language processing
- Speech recognition
Instead of teaching a computer, everything it needs to know to carry out a task, computer scientist Arthur Samuel, in 1959, wanted to teach a computer to learn for itself. Progress was made over the years, but machine learning really took flight as its own entity in the 1990s because of the internet and data availability.
Artificial intelligence as a whole can make tasks easier, for example, winning in chess, or make tasks more difficult, for example, a robot trying to grasp an object. The chess example is statistical and predictive, but the robot example, even after over 800,000 tries, proves difficult to accomplish according to an IEEE Spectrum article entitled, Google Large Scale Robotic Grasping Project, due to the need to correlate sensor data and activity. Check out the article sources below for a link to the article.
Many subspecialties of AI are utilized to perform these tasks.
Machine Learning Defined
The transformative technology of machine learning is computer systems that act without clearly being programmed to do so because they learn. Data is fed in, and decisions and predictions are revealed using probability and feedback.
Specifically, machine learning tackles practical problems with analysis tools that utilize data and execute algorithms to build predictions and decisions.
- Training data is captured and stored until input into algorithms, which are run according to the step by step instructions indicated via computer programming.
- R and Python, computer programming languages, are used to write algorithms, which are mathematical instructions that have been in existence since Alan Turing published his computational theory in the 1950s.
- Learning is accomplished through feedback loops that decide right from wrong and learn or modify the approach to build the output, predictions, and decisions.
Furthermore, algorithms are obtained by finding applicable code in libraries on the Internet, or a computer programmer writes them. Another option of interest is web sites or apps in which machine learning algorithms can be customized and purchased, for example, Algorithmia.
You may also buy the textbook Algorithms that reviews important code currently in use, including fifty algorithms all programmers should be familiar with.
When approaching a problem and using machine learning to handle it, the more data, the better. Whenever an algorithm is run, information is learned, taking you one step closer to a resolution. Specifically, standard computer learning mechanisms are:
- Supervised – labeled data training
- Unsupervised – unlabeled data training
- Reinforcement learning – reward-based training
Option to Learn Machine Learning
Decide how to approach the path to understanding machine learning by being aware of your learning style and purpose. Do you want an accredited degree, or do you prefer real-world examples and very little instruction interaction? Your options are:
Earn a Computer Science or Engineering Degree
By earning a computer science or engineering degree, you can major in artificial intelligence and machine learning. Traditionally, AI and machine learning are masters degree programs, but as the data science field has exploded, bachelor’s degrees in this field have increased tremendously.
A college curriculum will be defined and scheduled, along with your guidance counselor. Stanford University has a Bachelor of Science degree with an AI track that includes machine learning. On the other hand, the California Institute of Technology also has a Bachelor of Science degree in Computer Science with a machine learning and AI track.
Enroll in a Bootcamp
Bootcamps are pre-scheduled and can be added to your calendar after enrollment. It is also cheaper and less time consuming but without the accreditation. You may want to consider Springboard’s Machine Learning Engineering Career Track Program Bootcamp requires six months of commitment and focuses on engineering production skills.
Moreover, Codesmith’s Software Engineering Immersive Bootcamp is 12 weeks long and starts from the ground up, concentrating on engineering concepts such as algorithms.
Attend Online Classes
Online classes, although some are free, others are not, allow more freedom to pick and choose options and add them to your calendar. Stanford University offers a Machine Learning course in Coursera. This course concentrates on learning at your own pace and includes learning and building algorithms, understanding data mining, and real-world examples.
MIT Professional Education is a professional, 8-week course from a prestigious college. Therefore it is priced accordingly. Although more of an introduction, and definitely not a coding class, you will certainly understand machine learning from data inputs through decision outputs.
For further education, there are multiple masters programs and well-known certification programs. Although not specifically machine learning, the Massachusetts Institute of Technology has gained accolades for its Professional Certificate Program in Machine Learning & Artificial Intelligence, for offering a variety of real-world projects and experiments.
Also, learn the industry-standard computer programming languages. The most prevalent currently are R and Python. In addition to being open-source with extensive libraries, the support communities are plentiful, for example, Reddit and GitHub. Programming skills will enhance marketability in your job hunt.
Timeline to Learn Machine Learning
First, create a list of your learning gaps and determine how you would like to fill in those holes. Do this by listing each concept, matching with specific classes, and enrolling, then gain knowledge, via online tutorials, bootcamp, or college.
In order to gain practical experience, find an internship or entry-level job that fulfills those needs. If you are a quick learner and have access to work experience, your timeline will be short. Here are the essentials:
| Concept | Topics to learn through education and work experience | Time to complete |
|---|---|---|
| Computer Programming | Python, R, C/C++, Java, or many others. | 3 months – many years |
| Statistics and Probability | Testing, modeling, and evaluations. | 3 months – many years |
| Data Science | Data collection, preprocessing, storage, and inputting. | 3 months – many years |
| Algorithms | Learn to determine applicable algorithms, code, and execute. | 1 month – 1 year |
| Data Visualization | Learn Tableau or Power BI. | 1 month – 1 year |
| Specializations, for example, neural networks, and deep learning | Become proficient at the specialization that interests you. | 3 months – a lifetime |
Careers
Professionals with artificial intelligence and machine learning experience are in short supply now and in the foreseeable future. Machine learning requires many specific tools and techniques related to mathematics, statistics, and computer science to predict or resolve problems.
Algorithms are needed in every industry, and therefore a person with an aptitude for writing code will be in high demand. Here’s a quick list of jobs which a person with machine learning credentials:
- Computational linguists where machine learning technology works with voice recognition software.
- Data scientists.
- Designers in machine learning create algorithms that help eCommerce recommend applicable products to consumers.
- Machine learning engineers.
- Machine learning scientists.
- Software developers.
Keep in mind that job titles don’t necessarily accurately reflect the organization’s expectations. Many times, the titles vary significantly between industries and even companies within the same industry. Therefore, read every word of the job description and discuss it thoroughly with the hiring manager before accepting a position in machine learning.
Also, ask about professional development because, as you know, the artificial intelligence realm is changing very quickly, and you will want to stay on top of new trends and innovations.
Author’s Recommendations: Top Data Science Resources To Consider
Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.
- DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
- IBM Data Science Professional Certificate: If you are looking for a data science credential that has strong industry recognition but does not involve too heavy of an effort: Click Here To Enroll Into The IBM Data Science Professional Certificate Program Today! (To learn more: Check out my full review of this certificate program here)
- MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
- Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.
Conclusion
Machine learning carries out AI by training algorithms, and using large incoming data sets, the probability of predictions and decisions being accurate are statistically excellent. Consequently, it’s evident that the skill set needed to do the work is vast but can be mastered over time. Educational resources are abundant, given that you have decided on a clear path to follow that suits your goals and aspirations.
And one thing is for sure, the demand for machine learning specialists is expected to increase over time, and the innovative ideas and developments keep coming, making it an extremely exciting profession.
BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.
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