How To Learn Machine Learning

Krystle Dodge

Managing Editor

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What is Machine Learning? 

Machine learning is the field of computer science tasked with developing algorithms to advance programs of artificial intelligence (AI). For AI to function properly and scale-up to more comprehensive ability, the AI must be able to process and integrate data into its core program or ‘brain,’ in other words it needs to be able to learn. 

While this might sound like an outlandish premise from the back of a traditional sci-fi novel, machine learning is already a central element of various titans of industry. Netflix, Alexa, and Siri all use forms of machine learning to enhance its ability to process consumer data. Whether its voice recognition, pattern tracking, or word association–these applications are constantly learning from the way their users interact with them. 

Machine learning professionals are at the forefront of these endeavors. Taking these apps–and those like them–to the next level is the primary focus of their entire careers. Using programming expertise and command of computer science, these professionals perform maintenance and iterate on AI programs in order to increase intelligence, security, and functionality. 

Prerequisites in Machine Learning – 9 Steps to Success

In order to build a career in machine learning, it will be crucial to build a foundational knowledge base in key areas while also building the highly technical skills needed to work as a machine learning engineer. Given how many prerequisites of working within the field of machine learning are areas of expertise, it can be difficult for prospective professionals to keep track of it all. So here is a breakdown of the skills and knowledge you will need listed in the order of a sensible acquisition: 

  1. Computer Science 

With machine learning being a branch of software development and therefore computer science, this step might seem overly obvious. Many industry leaders however, point to the importance of building a strong foundation in computer science–even the areas seemingly unrelated to machine learning. The best machine learning engineers are professionals who are able to grasp the immense complexity and nuance of the endeavor, a perception made clear through mastery of computer science. 

  1. Software Development  

Software development is a key component of machine learning. To excel in a machine learning project, understanding the core tenets of software development will be crucial. (Learn more about which degree is best for a software engineer.) The Software Development Life Cycle (SDLC) for Machine Learning is as follows: 

  • Requirement Analysis

  • Planning and Scheduling

  • Architecture and Sub-System Design

  • Coding

  • Testing and Integration

  • Deployment

  • Maintenance

  1. Programming Skills

It is important to note that a career in machine learning is a specialized career of software engineering where the primary skill of focus is programming and coding. Different projects–and different phases of projects–utilize different programming languages and approaches, so machine learning engineers often specialize in a few languages. Though it will be necessary to master at least one. 

  1. Python Programming 

If you are going to choose one, Python is the primary language of choice for machine learning professionals. Most data toolsets have built-in Python functionality, which confers an ease of accessibility that is most treasured by engineers. You can polish your Python programming skills through the following practices: 

  • Use collaborative tools such as Github 

  • Review code on collaborative sites 

(coding is often a team-based endeavor)

  • Use Nose to run unit tests for code 

  • Run Postman to test your API

  • Run Jenkins to see if your code functions properly 

  1. Machine Learning Theory 

Machine learning professionals are often criticized for becoming too enamored with theory and distracted from practice. This is not a problem in itself though, because many of the primary challenges faced machine learning professionals are theoretical in nature. 

For instance, many machine learning algorithms essentially lock out engineers from understanding how an AI reached certain conclusions or functionalities. This makes scientific repeatability notoriously difficult. Understanding the foundation theories of machine learning will better prepare professionals for tackling the challenges presented in them. 

  1. Small Data Sets and Experimenting 

One you have a strong foundation in Python and have developed an understanding of machine learning theory, professionals are ready to tackle their first foray into machine learning practice. Many professionals are self-taught during this step–by simply messing around with small data sets and experimenting with various tasks. 

Many degree programs, MOOCs, and certification programs accomplish this by tasking the student with a diverse set of practices and experiments, or by assigning individual projects. 

  1. Deep Learning Frameworks (TensorFlow)

One developing familiarity with programming languages and the structure of machine learning software development, professionals can take a step up into the world of deep learning. Deep learning is a subset of machine learning which constitutes a state of super-accelerated learning, which can speed up the development process. 

Deep learning frameworks such as TensorFlow allow machine learning professionals to integrate deep learning functionality into their software. Tensorflow is a free, open-source software database for machine learning operations, and is the primary tool of choice for working with deep learning frameworks. 

  1. Production-Level Data Sets Experimenting

Once you have experience in deep learning and small assets experiments, you are ready to build skills in working with production-level data sets. This means you are ready to graduate to working with multidimensional variables and full-scale databases. In other words at this stage professionals are ready to start experimenting with real-world machine learning projects. If professionals find this step overwhelming, they can accomplish this by engaging in high-level projects through coding bootcamps, professional internships, or advanced degree programs. 

  1. Build a Portfolio 

Like an artist or architect, professionals at this stage will want to start holding on to their work. Successful projects and exemplary code should be stored in a professional portfolio in order to impress future employers. 

Educational Paths in Machine Learning 

Because the field of machine learning is so new–and growing at a remarkable rate–new forms of machine learning education pop up every day. Various degree programs, open-source guides, and professional boot camps provide machine learning professionals of every level with opportunity for professional advancement. In other words, there has never been a better time to learn machine learning. Here’s a breakdown of the most common paths taken:

  • Associate’s

An Associate’s degree program will not be enough to make the jump into machine learning unless the student has tremendous aptitude and existing expertise in the field. An Associate’s program such as Associate of Data Science or Associate of Software Development is primarily used to prepare students for enrollment in a top-tier Bachelor’s of Software Development or Machine Learning. 

Career Outcomes:

  • Internships 

  • High-End Undergraduate Programs 

  • Bachelor’s 

A Bachelor’s program is the primary entry-point into the field of machine learning for many professionals. These programs are typically comprehensive introductions into computer science, software development, and machine learning spaced out over four years. Students will also be able to find undergraduate programs designed specifically for machine learning professionals. 

Career Outcomes:

  • Software Development Proficiency 

  • Internships 

  • Entry-Level Software Development / Machine Learning Jobs 

  • Graduate Programs 

  • Master’s 

A Master’s program will be the primary choice for advanced professional development for many machine learning engineers. Graduate programs are where students go to build an edge against their competition in the job market. Graduate programs of software development often offer entire degree programs designed for machine learning professionals. 

Career Outcomes:

  • Software Development Mastery 

  • Mid-to-High Level Software Development / Machine Learning Jobs

  • PhD Programs 

  • PhD 

PhD programs of machine learning are designed for expert professionals who wish to work on the cutting-edge of the industry. These professionals are often involved with the most prestigious projects of machine learning development, and will earn access to the most prestigious jobs 

Career Outcomes: 

  • Innovation in Machine Learning  

  • High Level Machine Learning Jobs 

  • Prestigious Research Projects

Machine Learning MOOCs, Boot Camps, and Certifications 

MOOCs (Massively Open Online Courses) are an excellent way to build efficient proficiency in a target area with the field of machine learning. Here’s a list of some of the most popular MOOCs in the subject: 

Like MOOCs, machine Learning bootcamps are a tremendous way to build targeted and efficient expertise. Bootcamps are typically a little more rigorous and structured due to the financial cost of enrollment. Here’s 5 of the most popular: 

  1. Springboard’s Machine Learning Engineering Career Track Program: Deploy ML Algorithms. Build Your Own Portfolio.

  1. Codesmith’s Full-time Onsite Software Engineering Immersive

  1. MIT’s Professional Certificate Program in Machine Learning & Artificial Intelligence

  1. NYC Data Science Academy’s Data Science with Python: Machine Learning

  1. California Institute of Technology’s Learning from Data (Introductory Machine Learning course)

Professional ‘Machine Learning Certification’ programs are an excellent resume-builder. These certification programs will ensure that you future employers recognize your professional development:

  1. Artificial Intelligence (Northwestern | Kellogg School of Management)

  2. Machine Learning with TensorFlow on Google Cloud Platform

  3. Artificial Intelligence: Business Strategies & Applications (Berkeley ExecEd)

  4. Machine Learning: From Data to Decisions (MIT Professional Education)

  5. Machine Learning Data Science Certification from Harvard University (edX)

  6. Certificate in Machine Learning – Teach Machines to Teach Themselves (University of Washington)

  7. Machine Learning Certification (University of Washington)

  8. College Machine Learning Certificate (edX)

Additional Machine Learning Resources 

Sometimes the best resources for learning are bite-sized resources that are quick and easy to integrate into your education regimen. Here are some totally free guides, videos, and other resources to aid in your machine learning education: 

Top Machine Learning Youtube Channels 

Top eBooks

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