What Should I Know Before Trying to Learn Machine Learning?

Ever wondered how your phone suggests the next word you’re about to type, or how Netflix recommends shows you just might love? That’s the magic of machine learning (ML) at play! But before you jump in and try to build your own chatbots or movie predictors, there are some foundational skills that will make the journey smoother. In this blog, we’ll discuss some of the most essential skills you must have before you start with your machine learning journey. Even if you don’t know these skills, you can enrol in a machine learning course that teaches you these skills.

5 Skills You Must Know Before Starting Machine Learning

If you want to master machine learning, there are certain skills that will make your learning journey easier and help you understand complex machine learning topics quicker. So, here are five prerequisite skills that you should be well-versed in before you decide to pursue machine learning:

1. Statistics

Statistics forms the very foundation upon which machine learning is built. It arms you with the capacity to analyse data, find trends and patterns, and come up with conclusions that actually mean something. You can learn this skill with a machine learning course as well. This is a very important skill and is among the most commonly asked machine learning interview questions. Here are some of the key statistics subjects you must understand:

1. Descriptive Statistics – This describes and summarises the data, providing a brief rundown using measures like central tendency and dispersion. These are somewhat relatively simple ‘play-by-play’ on your data. Measures you must know are:
   1. Central Tendency
      1. Mean
      2. Median
      3. Mode
   2. Dispersion
      1. Variance
      2. Standard Deviation
2. Probability Distributions – This is a probability function used to describe the likelihood of various outcomes in a certain scenario. For example, you have standard functions like normal, binomial, and Poisson distributions. They are key as many machine learning algorithms hinge on them.
3. Hypothesis Testing – This class will teach you how to test claims and determine their validity. More importantly, it gives you the power to judge whether the machine learning model you are using is even worth anything.
4. Correlation and Causation – The main difference between them is that correlation describes a relationship. You simply cannot infer causation from correlation alone. It is invaluable to think like the machine learning models but not confuse correlation with a direct cause relationship.

2. Probability

Probability theory relies on the probability of a specific event occurring. Given that most machine learning algorithms are based on making assumptions regarding the data, the theory is the foundation for exploring the “likelihood of events.” Machine learning courses focus a lot on this skill because this is another area where most machine learning interview questions are asked. Some of the main areas of probability focus include:

• Conditional Probability – This determines the probability of one event if another event has occurred. Probability is utilised to perform various machine learning and AI-relevant tasks, such as spam filtration. In this case, the probability of a message being spam is associated with the presence of potentially harmful words.
• Bayes’ Theorem – This proposition is the cornerstone of many machine learning tasks, such as classification. The general idea of utilising the theorem implies continuously assessing the prior probabilities with incoming information.
• Random Variables – It represents a “quantity that can take on different values” with a particular probability. Defining and understanding the concept of a random variable is critical in handling machine learning solutions and algorithms to account for the fact that their data is never 100% accurate all the time.

3. Linear Algebra

Linear algebra serves as the conceptual foundation for computation and work with data in ML. Its topics and doctrines can include:

• Vectors – They represent multiple values stemming from “single data points.” In other words, vectors may signify multiple collections of data points, while matrices serve as collections of vectors. Utilising them in ML happens when numerous calculations and transformations are applied to vector matrices.
• Differentiation – This concept helps you find the rate of change of a function. While not directly used in models, it is integral in most training algorithms in machine learning. A common example is gradient descent, which finds the optimal weights of a model by minimising a cost function.
• Integration – It enables you to calculate the total area under a curve or value under a function. It is also integrated into some machine learning models, such as support vector machines. Semi-related concepts are frequently integrated into word problems where you demonstrate an understanding of both rules.

Note: Differentiation is far more useful for a beginner in machine learning than its counterpart. Later, after you become proficient in more advanced mathematical topics, both concepts are applicable in the field. They can also be practically applied in statistical analyses, but they are less common.

4. Programming Language

Machine learning models need to be implemented correctly using code. Knowing a programming language is very important since it provides the foundation for this implementation. Moreover, a lot of machine learning interview questions were asked about this topic. Here are some common programming languages:

1. Python – The Machine Learning community mainly uses this due to its easy syntax, collection of libraries, and large user base. Make sure that you can write code other than writing clean, efficient code – like knowing common algorithms and data structures as well as OOP principles.
2. R – Statisticians prefer this language for data analysis but do not generally use it as widely in machine learning. Most datasets can be analysed and visualised using their own packages, like ggplot2. It may be less popular with all machine learning practitioners as it might not be as intuitive as Python.

5. Calculus

It is not a definite must-have for beginners, but it enriches the understanding of the working principles behind machine learning algorithms. Here are some key points in Calculus:

1. Derivatives – They let us determine and adjust the rate of change in functions immediately so models can be effectively fitted to data.
2. Gradients – The gradient of a function (say f(x,y)) always points in the direction of its greatest increase. In an optimisation algorithm, this, in turn, is applied to update the parameters of a machine learning model with respect to error.

Conclusion

Machine learning can seem very challenging and difficult to learn when you’re starting out. But with the right tools and a curious mind, you’ll be well on your way to becoming a machine learning whiz! And you don’t have to worry even if you are not well-versed in these skills! That’s because you can always take up a machine learning course.

If you want to launch your machine learning career with a machine learning course, then Interview Kickstart is here to help. Interview Kickstart offers an intensive program designed by 500+ FAANG instructors and industry veterans. Their machine learning course covers everything from the fundamentals to the most advanced techniques. 

Get hands-on experience through live training sessions and practise your interview skills with mock interviews led by experts. Interview Kickstart will equip you with the knowledge and confidence to tackle any machine learning interview question with ease. So, don’t wait! Join over 17,000 tech professionals who have taken their careers to the next level with Interview Kickstart. Register for their free webinar today and learn how they can help you master their machine learning skills and land your dream job!