How To Learn Machine Learning in 2024

January 28th, 2022

Arthur Samuel first explained 'Machine Learning' in 1959, calling it a 'Field of study which provides the computer system with the capability to learn without being explicitly programmed.' And no one could have said it better!

In this blog, we are going to direct our focus towards understanding how to learn machine learning in 2024. Being a leading technology today, the future prospects look great and exploring the field today will ensure a good tomorrow. Before we go forward, let's dissect - what is machine learning?

Machine Learning

What is Machine Learning?

Machine learning is the field that works with data analytics techniques to train computer systems to take actions, learn, and change in a natural flow, similar to humans. Machine Learning algorithms employ computational methods for directly learning information from the data in hand. Hence, there is no dependency on a predetermined equation.

Machine learning is a fragment of artificial intelligence with specks of computer science. It lays direct focus on using algorithms and data to make the computer imitate the way humans learn.

Why Learn Machine Learning in 2024?

Before we move to how to learn machine learning in 2024, you must understand why to learn machine learning. Here are some of the top reason-

  1. Leveraging machine learning facilitates organizations to make better decisions without the need to have any human intervention.
  2. It facilitates organizations to gain a better understanding and view of the trends going on the business operations and customer behavior. This also aids in bringing forth new products and services.
  3. The demand for ML experts is rising at a steady pace. Many big names like Google, Facebook, Twitter, Uber, etc. have moved towards it.
  4. The salary packages for ML professionals are highly competitive.
  5. The opportunities are not limited to any one area or sector. You will benefit from global opportunities in all top industries.

Types of Machine Learning

Machine Learning Types

There are four types of machine learning. Having knowledge about these before you begin to explore this field will help you understand things faster. Let us look at the types of machine learning to understand them better.

  • Supervised Learning

Supervised learning means that data scientists provide the algorithm with labeled training data, and then define the variables. The goal is to allow the algorithm to assess correlations and learn accordingly.

  • Unsupervised Learning

Unsupervised learning means that the algorithm is trained on unlabeled data. This ML algorithm scans through datasets to search for any meaningful connection. The data, which the algorithms are trained on and offers predictions on, are predetermined.

  • Semi-Supervised Learning

Semi-supervised learning algorithm involves a mixture of the two preceding types. Here, data scientists might feed an algorithm, which is usually labeled training data. However, the model is free to explore the data on its own and develop its own understanding of the data set.

  • Reinforcement Learning

Reinforcement learning consists of data scientists using well-defined rules to train a machine on how to complete a multi-step process. Data scientists program an algorithm to complete a task and insert it with positive or negative cues, leaving it to work out how to complete the task. The goal of reinforcement learning is to learn the best policy.

If you are on your way to become an ML engineer, then this is the right blog for you. Why? Because we are about to give you a comprehensive insight on how to learn machine learning for beginners.

You May Also Read- Reasons Why Python is Good for AI and Machine Learning

Machine Learning Guide For Beginners

If you are a beginner in the field of machine learning, then you have come to the right place. We are going to unlock the roadmap to success in this field. Become a talented machine learning engineer by following this ML guide.

You can modify the steps according to your requirements and existing knowledge. To reach your desired end goal, you must diligently set out to learn everything important in this sector.

Step 1: Understand the Prerequisites

If you think you do belong to the field of machine learning, then these are some of the  prerequisites you need to know about. These are essential to get started with the applications and theories of ML.

  • Statistics & Probability
  • Programming (Python, Java, R, or Bash)
  • Mathematics (calculus and linear algebra)
  • Data Tackling

While you do not need to have a PhD in any of the above-mentioned topics, you will need a good understanding to get started with machine learning.

Step 2: Learn ML Theories

Once you are done with the prerequisites, you can move on to actually learning ML, by beginning with the theories. It's better to start with the basics and then further move to more complex things.

  • Planning & Data Collection
  • Data Interpretation with Model Results
  • Data Assumptions
  • Enhancing your Models
  • Data Preprocessing
  • Adding to Business Value

The most complicated aspects of machine learning are data collection, integration, cleaning, and preprocessing. Hence, it's really important to practice these as you will be working with a humongous amount of data.

Step 3:  Learn Essential Topics

Once you are aware of the key underlying concepts, you have come to the point where you should tap into the essential topics, reasoning, and capabilities. Here are a few things you must certainly begin with.

  • Practice Machine Learning Workflow
  • Work on Real Datasets
  • Learn topics like neural networks and deep learning
  • Create an MVP or minimum viable product

Step 4: Learn Different Machine Learning Tools

There are plenty of ML tools today that you can learn from. Whether you are searching for data preparation and collection, application deployment, or model building, there is a tool for it all. Some of the most trusted tools for machine learning/ artificial intelligence include -

  • TensorFlow
  • Azure Machine Learning Studio
  • Google Cloud AutoML
  • Accord.NET
  • Auto-WEKA
  • Amazon Machine Learning (AML)
  • Scikit-Learn
  • BigML

Step 5:  Opt for a Machine Learning Training Online

There are plenty of free machine learning tutorials online, along with blogs and videos. However, nothing can match the training level of a structured machine learning course that is crafted and taught by industry experts.

Since there is a very high demand for ML professionals, it has become important to do something different to shine out among the crowd. Enrolling in a machine learning training offered by a trusted learning platform is certainly a good step. An online program is definitely a better option as it gives you unprecedented flexibility, while saving you a lot of time.

Learn Machine Learning with Python

Python has always been a popular programming language but has gained more traction with the rising machine learning  popularity. Being a versatile language, Python is a favorite choice for machine learning enthusiasts.

It offers plenty of libraries, such as Scikit-learn, Keras, TensorFlow, Pandas, and NumPy, all of which are equally beneficial when learned by an ML professional. Each of these libraries provide functions and tools integral for data analysis, and manipulation, as well as to build Machine Learning models.

Learning machine learning with Python is extremely beneficial for beginners. Here, you learn the basic statistics and required programming to work on ML problems. Machine learning with Python training will help you learn these two in a single go, getting you closer to your goals faster.


Now that you know what machine learning is and how to learn it, it should not be a big deal to get started. All you need to do to begin your success journey is to choose the right resources for learning, at the very beginning.

The future of machine learning is great and that gets reflected from the fact that the average annual salary of machine learning Engineers in the US is $1.53k. This number itself is a testament to the kind of demand ML engineers and other related professionals can witness today and in the coming years.

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