What Is Machine Learning? The Field of the Future

Machine Learning is a way to get a computer to learn and behave like a human without being explicitly programmed. Essentially, Think of Netflix’s recommendation engine, self-driving cars, and facial recognition when uploading photos to Facebook. Do not worry. Computers won’t replace humans any time soon. Instead, machine learning is more of a tool that helps you solve complex problems faster and make more informed decisions. Read the Easy Guide to Become a Machine Learning Engineer in 2021.

How does machine learning work? What is the difference between artificial intelligence and machine learning? Why is machine learning important? All important questions! In this post, I will go into detail about what machine learning is exactly, what it is used for, how it works, what is the difference between machine learning, deep learning, and artificial intelligence, and some resources to help you get started learning. machine learning.

What is Machine Learning?

Machine Learning Definition: Machine learning, abbreviated as ML, allows computers to automatically learn and improve from experience, observations, real-world interactions, and other data without any explicit programming by humans. According to the University of Washington: “Machine learning algorithms can generalize examples to figure out how to do something important. This is often feasible and cost-effective where manual programming is not possible.”

Because machine learning mimics the way humans learn (i.e., through practice, understanding patterns, making mistakes, taking risks, learning from past actions, etc.), computers can become progressively more accurate in a task or set of tasks.

The term “machine learning” was coined in 1959 by Arthur Samuel, best known as Samuel Checkers-Player, who played championship-level checkers. Earlier, in 1950, Alan Turing also proposed a “learning machine” that could become artificial intelligence.

Moving quickly into the 1990s, machine learning began to shift from a knowledge-centric approach to a data-centric approach, and computers began to learn from vast amounts of data. Over the past few decades, data-driven machine learning has continued to evolve and even split into different types of machine learning (more on that below)!

Machine Learning vs AI vs Deep Learning

So, what is the difference between artificial intelligence (AI) and machine learning? What about Machine Learning vs Deep Learning?

Machine learning is a branch of AI, just like deep learning. Artificial intelligence is essentially an umbrella term for any method used to mimic human intelligence. Deep learning is a subset of machine learning in which neural networks learn using vast amounts of data. A neural network is a computing system modeled on the biological neural networks that make up the human brain and nervous system.

How does machine learning work?

There are three types of machine learning methods commonly used to train computers: supervised, unsupervised, and reinforcement. Supervised Machine Learning: You feed data to a well-defined and labeled machine learning algorithm so that the algorithm can learn from examples. Over time, a supervised learning algorithm can see previously unseen examples and predict labels based on examples it has learned. Overall, this method is closer to predicting something.

Unsupervised Machine Learning: Unlike supervised methods, the data provided to a machine learning algorithm is not labeled. Huge amounts of data have been provided and asked to self-identify previously unknown patterns, trends, and structures. Netflix’s recommendation system is a good example of this. Overall, this method is closer to finding hidden patterns in your data.

Reinforcement Machine Learning: In this method, the computer learns through mistakes and trial and error. The system is rewarded or penalized for the work performed. It is similar to how pets or children learn.

Common languages/libraries that developers looking to get started with machine learning need to know to include Python, TensorFlow, R, Scala, Julia, and C++.

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What is machine learning used for?

So, why is machine learning important? The great thing about this field is that machine learning applications can be used in almost any industry imaginable (and even being used to save lives).

Here are some examples where machine learning is used:

  1. Marketing: Recommendation engine (e.g. Amazon product offer, streaming service movie/show recommender, Spotify playlist creator)
  2. Finance: Insurance risk assessment or fraud detection
  3. Medical: Faster and more accurate diagnosis, disease prediction, new drug development, etc.
  4. Transportation: Self-driving cars, traffic prediction, route optimization, etc.
  5. Agriculture: Crop yield forecasting, soil and water quality management, livestock management, etc.
  6. Training: Personalized learning, grading assignments, and more
  7. Manufactured by: Predicting machine malfunctions and reducing errors, etc.

And as machine learning continues to advance in the future, we will see even more exciting machine learning applications such as:

  • A more personalized eCommerce experience
  • More applications in healthcare (especially due to COVID-19), including early detection of disease and new forms of treatment
  • Advances in Natural Language Generation (NLG) (i.e. the ability to generate written or spoken narratives from data sets)
  • and much more

If you decide to pursue a career in machine learning, there are so many places you can end up and projects you can work on. There are limits!

Where to learn machine learning

Would you like to start a career in machine learning or learn a little more about the subject? If you are looking for a good programming language to start building skills for machine learning, Python is a great choice. There are also beginner-friendly machine learning courses that can explain machine learning models and some key concepts you need to know.

Here are some courses to help you get started.

  1. Machine Learning Crash Course Provided by Google: A fast-paced, hands-on introduction to machine learning with lectures from Google researchers. More than 30 workouts included.
  2. Machine Learning, Data Science, and Deep Learning with Python On Udemy: Hands-on machine learning tutorials with data science, Tensorflow, artificial intelligence, and neural networks. Excellent beginner ML/data science course.
  3. Introduction to machine learning Duke University via Coursera offers: Provides a basic understanding of machine learning models (logistic regression, multi-layer perceptrons, natural language processing, and more).

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machine learning and you

Machine learning is a good field to enter, especially since it is experiencing rapid development and affecting almost every industry.

It’s a future-proof, exciting, and potentially world-changing field.

Not only that, but machine learning engineers make an average of $200,000 per year, making it a very lucrative field!

That said, if you are interested in pursuing a career in machine learning, you should know that being a machine learning engineer is not a beginner job. It would be advantageous to start as a data analyst or data scientist first, prepare for other data tasks, then continue learning from those tasks and ultimately specialize in machine learning as you advance your career.

Check out this article on Python for Data Science for more in-depth information (including information on Python/machine learning connections) and course recommendations!

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