What is Machine Learning? A Complete Beginner’s Guide

Austin Chia, contributor to the CareerFoundry Blog.

Are you curious about machine learning (ML) and what it can do for you? Have you ever tried to learn about this cutting-edge technology only to find yourself lost between buzzwords and technical jargon?

Don’t worry; this guide is here to help anyone understand the basics of ML—including complete beginners!

We’ll cover all the essentials you’ll need to know, from defining what is machine learning, exploring its tools, looking at ethical considerations, and discovering what machine learning engineers do.

If you’d like to skip to a certain section, just use the clickable menu:

  1. What is machine learning?
  2. How does machine learning work?
  3. Machine learning examples
  4. Ethical considerations of machine learning
  5. The role of the machine learning engineer
  6. Final thoughts
  7. Machine learning FAQ

So if you’re ready, let’s dive in and have your first look at ML.

1. What is machine learning?

Machine learning is a type of artificial intelligence (AI) that allows computer programs to learn from data and experiences without being explicitly programmed.

At its core, machine learning is the process of using algorithms to analyze data. It allows computers to “learn” from that data without being explicitly programmed or told what to do by a human operator.

Instead, ML uses statistical techniques to make sense of large datasets, identify patterns in them, and make predictions about future outcomes.

If you’d prefer to learn more visually, then check out this video we made explaining what machine learning is:

So, now that you know what is machine learning, it’s time to examine closer how it functions.

2. How does machine learning work?

At its simplest, machine learning works by feeding data into an algorithm that can identify patterns in the data and make predictions.

This is made possible through some key components that make up this machine-learning process. These vital components of ML are:

  1. Data: This is used to train the ML algorithm so it can identify patterns and make predictions. It can be structured or unstructured, depending on how it’s being used.
  2. Algorithms: This is the set of instructions that ML uses to analyze data and make predictions.
  3. Model: The model is the “brain” of the ML algorithm, which processes and stores information from the data to make decisions.
  4. Training: This is how machine learning algorithms learn from data by being fed large amounts of it so they can identify patterns and relationships in it.
  5. Inference: This is the process of using the ML algorithm to create a calculated output score.

Machine learning tools 

To carry out these tasks, some tools and technologies are needed. Here are some of the most common tools used for machine learning:

  1. Python: A popular programming language used to develop applications for ML projects.
  2. TensorFlow: An open-source library developed by Google that allows users to define, optimize, and execute models using data flow graphs.
  3. Keras: A high-level neural network application programming interface (API) written in Python that allows users to define and train deep learning models rapidly.
  4. Scikit-learn: A Python library for machine learning that specializes in data analysis, classification, and clustering algorithms.

These tools provide the basis for the machine learning engineer to develop applications and use them for a variety of tasks.

If you’re interested in learning more about whether to learn Python or R or Java, check out our full guide to which languages are best for machine learning.

3. Machine learning examples

To better understand how ML works, let’s look at some real-world examples:

1. Computer vision

One example is computer vision, where an ML algorithm can be used to identify objects in images or videos.

This is done by feeding large amounts of data into an algorithm that looks for patterns and then uses this information to label the objects correctly.

2. AI-driven chatbots

Another application is AI-driven chatbots. These are computer programs designed to simulate a human conversation. They are trained using ML algorithms to respond to user queries and provide answers that mimic natural language.

For example, an advanced version of an AI chatbot is ChatGPT, which is a conversational chatbot trained on data through an advanced machine learning model called Reinforcement Learning from Human Feedback (RLHF).

3. Text analysis

Text analysis is another example of ML in action. In this case, an algorithm can be used to analyze large amounts of text and identify trends or patterns in it. This could be useful for things like sentiment analysis or predictive analytics.

This leverages Natural Language Processing (NLP) to convert text into data that ML algorithms can then use.

4. Ethical considerations of machine learning

As with any technology, there are ethical considerations associated with machine learning. It’s important to consider the potential impact of ML when choosing to deploy it in your business or organization.

Some common considerations include:

  • Privacy and security: When collecting data for ML algorithms, it’s important to think about how this information will be protected and who will have access to it.
  • Bias and discrimination: If the data used to train an algorithm is biased in any way, then the results may also be biased. It’s important to ensure that all data is unbiased and representative of the population. Bias in machine learning is a huge issue set to define the next decade of the field.
  • Human job displacement: Machine learning algorithms can be used to automate many tasks, so this brings about a new question on whether it impacts human employment opportunities.

A machine learning engineer sits at his laptop in an open office.

5. The role of the machine learning engineer

So, now that you know what is machine learning, it’s time to look closer at some of the people responsible for using it. While there are quite a few machine learning jobs out there, an ML engineer is perhaps the main one.

A machine learning engineer is the person responsible for designing, developing, testing, and deploying ML models. They must be highly skilled in both software engineering and data science to be effective in this role.

Some common tasks of a machine learning engineer include:

  • Designing ML algorithms
  • Developing software to implement those algorithms
  • Testing and validating the accuracy of the algorithms
  • Deploying ML models into production
  • Maintaining and optimizing the ML algorithms

However, this job of developing and maintaining machine learning models isn’t limited to a ML engineer either. This expands to other similar roles in the data profession, such as data scientists, software engineers, and data analysts.

6. Final thoughts

Machine learning is definitely an exciting field, especially with all the new developments in the generative AI/ML space.

To recap all the aspects covered in this article on what is machine learning, here are some key points:

  • Machine learning is a subset of AI and involves using algorithms to learn from data without being explicitly programmed.
  • Examples of ML applications include computer vision, chatbots, and text analysis.
  • Ethical considerations should be taken into account when deploying ML models, such as privacy and security issues, bias in data, and potential job displacement due to automation.
  • The role of the machine learning engineer involves designing, developing, testing, and deploying ML models.

It can be intimidating to start learning ML, but with the right resources and determination, you can get started on your journey.

CareerFoundry’s Machine Learning with Python course is designed to be your one-stop shop for getting into this exciting area of data analytics. Possible as a standalone course as well as a specialization within our full Data Analytics Program, you’ll learn and apply the ML skills and develop the experience needed to stand out from the crowd.

For more related reading, do check out the following:

7. Machine learning FAQ

What is the difference between machine learning vs AI?

Machine learning is a subset of AI, and it refers to the process by which computer algorithms can learn from data without being explicitly programmed. AI, on the other hand, is an umbrella term to describe software that mimics the complex functions of a human mind through computing, which includes machine learning.

What is the difference between machine learning vs deep learning?

Machine learning is a broad field that includes different approaches to developing algorithms from data. Deep learning, meanwhile, is a specific type of ML technique in which machines learn through neural networks. Learn more in our guide to machine learning vs deep learning.

Which is better, AI or ML?

AI can be used for more complex applications than ML, while ML is better suited for more specific, smaller tasks. Both technologies are equally important, and your answer would depend on the context of the problem you’re trying to solve.

Is it hard to learn machine learning?

It depends on the person and their level of experience. Generally, it does require quite a lot of knowledge in both computer science and mathematics to be successful in ML. However, there are also many resources available to help people learn ML more quickly.

Does Netflix use machine learning?

Yes, Netflix definitely uses machine learning. According to Netflix’s own research, they use ML to power their recommendation algorithms.

This provides you with personalized movies and show recommendations that you see in your Netflix app. This even allows for more unique recommendations where budget-constrained algorithms can be designed.

Should I learn machine learning or AI?

You should definitely take a first look at picking up machine learning basics first, before venturing into the more advanced applications of AI, where you’ll need to learn more about deployment.

Having a basic grasp of ML will also help you build up the foundation for any AI-related projects that you might take on in the near future.

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