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.

1. 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 Bayes’ 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 can’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.

Even though student life is full of incredible moments and lifelong memories, sometimes it could be hard to handle everything without any help. Luckily there are a lot of free resources to organize the educational process as you like. 

These services can save you a lot of time and effort. For example, if you are looking for a website to help study, there is no better resource than Studyfy. Here you can find help proofread and edit your essays. It also has the option of finding a tutor if it’s needed. 

Instead of being afraid of making mistakes in your final paper, check it with the professionals’ help. Stay confident and calm about everything on your student’s to-do-list, and enjoy your student life.

We care about you doing your best with your student obligations. That is why we have prepared five helpful study help resources every student should use to achieve their goals without any obstacles.

Improve Your Writing Skills. Some Help with Grammar Checking

For the Clubhouse generation, the proper spelling and correct grammar constructions could seem unimportant. But it’s an error of judgment. Good writing skills are necessary to achieve ambitious goals whether you want to write an essay or just sending an ordinary email to your professor. It’s always good to appear as competent and professional as possible. 

Fortunately, there are plenty of resources to improve your writing, which could help you right away. For this sort of assistance, we advise you to use grammar and spelling checking applications such as Grammarly. 

In addition to checking your mistakes, their AI technology helps you understand and change the text’s tone of voice. That is how your writing can sound more friendly or confident (depending on your message’s aim). Also, there is a Grammarly extension for your browser so that you can use it directly in your documents and emails.

Goodbye, Procrastination! A Few Words on Time-Management

It’s hard to stay concentrated when you are young, especially when you are constantly distracted by social media notifications. That is why you should be good at time-management to achieve more as a student. For developing the ability to stay focused, you can use different free applications or tools like Focus Keeper. 

Most of those kinds of apps use the Pomodoro Technique. This method divides your workflow into 25-minute blocks with 5-minute breaks. The breaks are getting a bit longer after you complete four blocks. This technique is beneficial as you finally start to appreciate your work time and remember to have a rest. 

The Art of Taking Notes 

The secret to being successful in academics is to be an organized person. As much as it’s essential to improve your time-managing skills, it also matters how and where you keep your notes. Of course, you can have no problems with paper notebooks and stickers. But what if you forgot it at home?

That’s how note tools such as Evernote would come in handy. This digital binder can replace all of your old notebooks. Keeping all the schedules, personal or lecture notes in one place makes your student life more manageable. This app can also be helpful with your to-do-lists and even scanning handwritten notes. 

Be a Project Manager 

Your education is your project. As the only person responsible for your success, you should understand how the projects work, manage the tasks and keep your plans updated. Simple project-management tools such as Slack allow you to develop your organizational skills and better understand how it feels to be a boss. 

This resource is also very convenient for communication with your mates, having group or one-to-one discussions, or simply exchanging files. Moreover, you can use this application for distance learning. 

Play and Learn

This tip will be handy for those who have problems with remembering new words. Quizlet is another application, which could be extremely useful for students. It’s an old but gold method based on memory flashcards and constant repeating of the studying material. 

You can choose an already existing topic on which you want to improve your knowledge or create your own set of cards. This resource is very convenient for learning languages or learning complicated (i.e., technical, medical) terms alone or with your mates. 

Don’t Underestimate YouTube!

The most obvious tip is to use video materials as your study resource. Indeed, most of the students were looking for additional information on YouTube. However, it has way more useful opportunities than you think.

You can find information almost on any topic, every subject, and every motivational technique. For instance, on the TEDTalks channel, you can find stories of successful people who had their ups and downs but who finally found their path.

Learn and get inspired. But don’t cram until your head is full! Take care of your health. You can find many free sports training, dance, classes, or even meditation sessions for your break on YouTube. 

Creative Projects, Good Marks

Canva is another valuable tool for education. With the help of this free resource, you can create unusual presentations or worksheets for your projects even if you are not good with graphic programs. This platform is easy to use, but the results could be inspiring.

Here you can create a design of your CV or make your charts for the student project more attractive. Free photo stock and templates for almost all purposes allow you to show your creativity even with the most boring assignment. 

It doesn’t matter what tools you will choose for your study journey. What is important is that you are trying different options and looking for the most easy-to-use. Don’t hesitate to ask your friends how they fight procrastination or which they use to improve their writings. Just keep trying, and you will find your own best study resources.