Ever wondered how your email account determines spam mails from genuine emails, how Google maps direct you to your desired destination, or how Facebook knows the name of the person appearing in your pictures and suggests that you tag them? That is machine learning (ML) at work. Today, most businesses have wrapped their operations around data and are harnessing it to make informed decisions. One of the most popular data analytics techniques used today is machine learning. Because most of the data that is generated today is unstructured data, mostly social data and data from IoT devices, managing such massive information and making the most out of it takes sophisticated techniques like machine learning.

Machine learning is a subset of artificial intelligence that has gained traction remarkably in the past decade and one that has seen improvements in the last few years. Machine learning algorithms are trained to automatically access, learn from data, and improve from experience to perform intelligent tasks without explicitly being programmed. Machine learning has grown to become one of the most interesting disciplines to work in as well as one of the most demanded skills. It has a strong foundation in statistics, maths, and programming, which are the prerequisites that one needs to possess before considering undertaking **artificial intelligence and machine learning courses**.

**Machine Learning Prerequisites**

While machine learning overlaps with data science and artificial intelligence, machine learning courses focus on Machine Learning algorithms. This involves learning how to create mathematical models with a specific programming language like Python. This model is then exposed to a continuous input of data as well as the output applicable for them. As it analyses the input data, it establishes a correlation between the input and the supplied output iteratively to be able to discover patterns from input data without human supervision.

To launch a career in this field, you need to first have some prerequisite skills, as we shall discuss below.

**Statistics**

Statistical knowledge is core to machine learning. Statistics is involved with the collection, preprocessing, analysis, interpretation, and presentation of numerical data. Taking time to lay a solid foundation in statistics and probability theory, particularly in the following concepts, will come in handy when building ML algorithms.

**Descriptive and inferential statistics**: Descriptive statistics use data to describe the characteristics of data while inferential statistics allows us to make inferences about data based on the sample taken from the population. The latter involves first testing a hypothesis to determine whether the data being used is generalizable to a broader population.**Probability distributions and random variables**. Probability refers to the likelihood of an event occurring. A random variable, on the other hand, is the outcome of a statistical process. The probability distribution of a random variable describes the distribution of the probabilities over the random variable values. Under probability, you need to be familiar with different rules of probability such as Bayesâ€™ rule, the sum rule, and chain rule as well as techniques such as expected value, standard deviation, variance, and covariance. Also, master the different types of distributions, including Bernoulli distribution, binomial distribution, normal distribution, and Gaussian distribution, in addition to joint and conditional probability distributions.- Regression and decision analysis
**Math**

Some math techniques you will require to be conversant with include:

**Linear algebra**: Linear algebra for machine learning involves concepts like matrix multiplication. Matrices in machine learning are used to describe algorithms and in processes input of data variables when an algorithm is being trained. Basically, you need to learn the addition and subtraction operations as well as the multiplication of matrices. Other concepts you need to consider familiarizing yourself with are vector and scalar multiplication.**Multivariable calculus:**This concept helps us to explain the relationship between input and output variables and ultimately build accurate predictive models. In calculus, take time to master differential and integral calculus, partial derivatives, gradient, and chain rules.**Programming**

You canâ€™t use machine learning without a programming background. This is because machine learning algorithms are implemented using code. Some popular programming languages for machine learning are Python, R, C++, and Java. However, the best programming language to use will depend on the application that the ML algorithm will be implemented on. For instance, Python is the most preferred for NLP applications and Java for security-related tasks. Also, familiarizing with

Along these lines, some people may prefer to go right into machine learning without having to delve so much into the depths of programming. For this, they can opt to work in graphical machine learning environments like Orange and Weka, or scripting ML environments like Scikit learn that allow them to implement machine learning models with only coding basics.

**Data engineering**

Data engineering involves data collection, cleaning, and preprocessing. At the very least, you should know how to handle data because you will be dealing with data. Most specifically, you need to be familiar with SQL and NoSQL databases, ETL(extract load transform) processes, data analysis techniques, and data visualization, which is basically an entire data preprocessing and analysis cycle. During data preprocessing, consider learning how to deal with missing data, skewed distributions, and outliers. Quality data is essential for creating, tuning, and evaluating models.

In addition, you need to be familiar with machine learning techniques like data sampling and splitting, supervised and unsupervised learning, model evaluation, as well as ensemble learning which typically involves implementing a combination of models for improved performance.

**Pro tip!** It is better to practice these processes on large datasets than on smaller ones.

**Machine learning algorithms**

We understand that you will soon be enrolling in an artificial intelligence or machine learning course. However, as someone with a deep interest in this field, there is no harm in familiarizing yourself with popular machine learning frameworks like TensorFlow, ML techniques like decision trees, and the process of writing algorithms from scratch. You may not be ready just yet to build one, but knowledge is power. You will have prepared yourself for a smooth learning curve.

**Conclusion**

While you may not find it necessary at first, cloud computing and DevOps are where the real deal is. This is because businesses are now shifting their operations to the cloud and it will pay to have an idea of how to run your ML model in the cloud.

In this article, we covered essential prerequisites of machine learning including some of the commonly used programming languages. In summary, to learn machine learning, you need a background in statistics, math, programming, and data engineering knowledge.