Pre-requisites

Machine Learning courses do not always require prior knowledge in the area, it probably boils down to how efficiently you can operate and deal with statistical means, histograms, computer languages, variables, linear equations, and so on. In a nutshell, if you want to pursue machine learning, you must be well trained.

To get you started, below is a list of machine learning prerequisites.

1- Statistics

Statistics, as a field, is primarily concerned with the collecting, sorting, analysis, interpretation, and presentation of data. Some of you may have anticipated how statistics might help Machine Learning. Of course, data is a significant aspect of any technology nowadays. 

There are two types of statistics: descriptive statistics and inferential statistics. Descriptive statistics, as the name implies, are numbers that summarize a certain data set; in other words, Descriptive statistics summarizes the data set at hand into something more intelligible. Inferential statistics infer conclusions from a subset of data rather than the complete data set.

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2- Probability

It expresses how probable an event is to occur. All data-driven judgments are founded on probability. You will be dealing with the following in machine learning:

  • Notation
  • Joint and conditional probability distributions.
  • Probability rules include the Bayes theorem, the sum rule, and the product or chain rule.
  • Independence
  • Random variables that are continuous

These are just a handful of the ideas that machine learning students will be exposed to.

3- Linear Algebra

While linear algebra is essential to machine learning, the interactions between the two are hazy. They can only be explained by abstract ideas like vector spaces and matrix operations. In machine learning, linear algebra encompasses concepts such as

  • Algorithms written in code
  • Linear transformations
  • Notations
  • Multiplication of matrices
  • Tensors, and their ranks.

4- Calculus

It is essential for developing a machine learning model. Calculus, an integral component of many Machine Learning algorithms, is another path you might take to pursue a career in machine learning. As a candidate, you should become acquainted with:

  • Fundamental understanding of integration and differentiation
  • Partially derived terms
  • Slope or gradient
  • Chain rule—for neural network training

5- Programming Languages

Because machine learning methods are implemented with code, it is beneficial to have a solid foundation in programming. While you may get by as a rookie programmer by focusing on mathematics, mastering at least one programming language can greatly improve your knowledge of the fundamental processes of machine learning. However, you must master a programming language that will allow you to apply machine learning algorithms easily. Here are a few examples of popular programming languages:

  • Python
  • R
  • C++
  • Matlab.

Target Audience

  • Already employed as an analyst, business intelligence specialist, or junior data scientist who wishes to advance their career.
  • Managers and corporate leaders wish to establish, grow, and lead a data science team. This Machine learning course gives enough examples and business use cases to get started for such persons.
  • Students and recent graduates interested in pursuing a career as data scientists. This Machine learning training course delivers enough knowledge for such individuals to ensure they receive the job they deserve.

FAQ – General Questions


Is this a live or recorded training session?

It is a virtual instructor-led interactive session that takes place at a set time when both you and the trainer check-in. Will record the live session so that you can review and recap any missed sessions.

Can I access these Machine Learning live classes from anywhere?

Yes, because the live classes will be held online on the Zoom Platform, you can access them anywhere.

All you need is to participate in the Zoom sessions using a laptop or computer.

Why should I join this Machine Learning online course at EDUCBA?

Online EDUCBA Machine Learning Live training includes an Intermediate Course and hands-on projects. Several reasons for selecting EDUCBA ML training online are:

You will master ML with Python, classification algorithms, linear algebra underlying linear regression, logistic regression, supervised and unsupervised learning, etc.

This Machine Learning curriculum focuses on real-time ML projects and exercises relevant to the workplace. It also contains a comprehensive curriculum developed by industry professionals.

Our ML course programs will prepare you to compete for some of the greatest jobs in the world’s leading organizations for greater pay.

We give lifelong access to these ML classes’ videos, materials, and free updates to the most recent editions.

What jobs may I apply for after finishing my ML course?

Here are some of the most popular employment roles to consider:

  • Machine Learning Engineer,
  • Human-centered Machine Learning Designer,
  • Data Scientist,
  • Business Intelligence Developer
  • NLP Scientist.

Is there any coding required to learn the Machine Learning Course?

A basic understanding of coding is required for Machine Learning. You should research OOPs ideas, data structures, and algorithms, which will address in EDUCBA’s course. R, Python, C++, and Java are major programming languages used in Machine Learning.