Machine Learning Concepts for Beginners

Let’s face it – EVERYONE wants to know about Machine Learning. Considering the immense job-creating, life-revolutionising potential that it has, it is no surprise that it is in such high demand now. There are so many articles, videos, and books everywhere!

The amount of online content is truly spectacular, but for a beginner, it can be quite intimidating. It’s almost like being given a plethora of cuisines, and then being instructed to review them all. Where would you start? How would you consume all of it? How much of each would you need to have until you can come up with an accurate review?

For this reason, this article aims to consolidate some of the Machine Learning fundamentals into one easy-to-understand article. Thus, those of you who are just getting started can easily learn the basics without being overwhelmed by the technical details.

That said, we will now get into the “What”, “Why”, “When”, “Where”, and “How” of Machine Learning.

Let’s begin!

WHAT is Machine Learning?

Machine Learning is the process by which a machine learns how to think like a human being in order to perform a specific task, without being explicitly programmed.

WHY do we use Machine Learning?

By training a machine to think like a human being, the execution of certain tasks becomes easier, quicker, and much more efficient.

WHEN do we use Machine Learning?

Machine Learning was invented by some very ambitious people who desired to develop an intelligence that could resemble, if not surpass, natural human intelligence. The term ‘Machine Learning’ was coined by Arthur Samuel in the 1950s. This was a time when Alan Turing proposed the ‘Learning Machine’, and Marvin Minsky and Dean Edmonds built the first Neural Network machine. Within that same decade, Arthur Samuel invented a Checkers playing machine, and Frank Rosenblatt developed the very first Perceptron. From there, Machine Learning steadily began to grow.

WHERE do we use Machine Learning?

Machine Learning has come so far, from playing games to recommending products to customers. The more the technology advanced, the better its applicability became. Listed below are five important applications of Machine Learning that are commonly used, easy to remember, and good to know –

Spam Filter: Spam emails can automatically be detected within your inbox and stored in your Spam folder. That way, it doesn’t interfere with your more important emails. It also reduces the amount of time and effort you would have to spend sorting out your inbox.
Recommendation Systems: Most online stores use Machine Learning to recommend items based on the user’s recent activity and requirements. This prevents customers from getting irrelevant suggestions, and increases the chances of them making a purchase.
Virtual Assistants: They assist users in their daily requirements like setting alarms, making lists, and so on. They then store data from previous tasks, and tailor their performance based on these preferences.
Search Engines: Search Engines use Machine Learning Algorithms to find and display results that are most accurate to a user’s search. They even filter them out based on the user’s past activity.
GPS: Travelling has become so much easier thanks to GPS apps. These systems use Machine Learning to make travelling less difficult. They can show people their current location, the distance between two places, the estimated time it would take to reach another location, and the amount of traffic that could either increase or decrease their time of arrival.

HOW does Machine Learning Work?

Now that we know some of the important facts of Machine Learning, we shall proceed to the more interesting part – Understanding how Machine Learning works.

The first thing to know is that Machine Learning is mainly of two types:

Supervised Learning: It involves the use of labelled data (where the number of classes are known).
Unsupervised Learning: It involves the use of unlabelled data (where the number of classes are unknown).

Let’s have a look at five differences between Supervised Learning and Unsupervised Learning.

Supervised Learning:

It is a method of Machine Learning that deals with labelled input data.
It is used for Regression (predicting continuous variables) and Classification (predicting categorical variables).
It is more time consuming and accurate.
Some applications of include stock price prediction, object detection, spam detection, and sentiment analysis.

Unsupervised Learning:

It is a method of Machine Learning that deals with unlabelled input data.
It is used for Clustering (finding patterns in the data) and Association (identifying relationships between elements in the dataset).
It is less time consuming and accurate.
Some applications include credit card fraud detection and customer behavior analysis.

There is also a third type of Machine Learning method, known as Reinforcement Learning.

Reinforcement Learning:

It is a method of Machine Learning that aims to make the most optimal decision in order to maximize the reward.
It uses algorithms that learn from previous outcomes and then decide what action to take next. Thus, decisions are made sequentially, i.e., the next input is based on the previous output, unlike supervised and unsupervised learning, in which decisions are made only based on the initial input data.
There are two types of reinforcement learning – Positive Reinforcement (adding a positive stimulus or reward after some behavior to increase the likelihood of its recurrence) and Negative Reinforcement (removing a negative stimulus after some behavior to increase the likelihood of its recurrence).
For example, positive reinforcement would be giving a dog their favorite toy as a reward for behaving, whereas negative reinforcement would be taking the dog’s favorite toy away when it misbehaves.
Some applications include text prediction and gaming.

Now that we are familiar with the types of Machine Learning, let’s briefly go through some of the different algorithms used in Machine Learning.

Types of Supervised Machine Learning Algorithms:

Linear Regression
Support Vector Machines (SVM)
Neural Networks
Decision Trees
Naive Bayes
Nearest Neighbour

Types of Unsupervised Machine Learning Algorithms:

k-means clustering
Association rule
Principal component analysis

Types of Reinforcement Learning

Q-Learning
Deep Adversarial Networks

Last but not least, we will now understand how to train a machine learning model using the step-by-step procedure that is shown below.

Steps Involved in Solving a Machine Learning Problem:

Step 1 – Understand the Problem

The first thing you need to do is understand the problem. Make sure you know what needs to be done. Pay attention to any specifications or factors that might be important.

Step 2 – Collect the Data

Data plays an extremely important role in Machine Learning. Without the right kind of data, it is almost impossible to expect any kind of accurate outcome. Hence, you must choose the right source from which you would like to collect your data. Then, go ahead and gather all the data you require.

Step 3 – Prepare the Data

You now need to prepare your data for analysis. This could involve cleaning it up, making a few minor but necessary changes, etc. For example, to predict the sales of a product by studying its demand over a period of 6 months, the names of the buyers will not have any affect on your analysis, so you can remove it from your data. Likewise, you might need to manipulate the rest of the dataset. Visualising data is a very helpful technique to understand the dataset with the help of graphs.

Step 4 – Define the Training and Test Data

Training and Test Data do exactly what their names suggest. Training Data is used to train the model, which means this data is passed through the model to help it learn. Test data is used to test the model, which means this data is used to verify the model’s accuracy. It’s similar to a child learning a subject and then writing an exam to see how well she understood that subject.

Step 5 – Choose the Model

You need to choose a Machine Learning Model that will help you to obtain the best outcome. This is done based on your problem, your desired output, your data quality, and so on.

Step 6 – Train the Model

You can now input the necessary parameters and hyperparameters into your Machine Learning model. This includes values like the weights and bias (the learnable parameters of the model), and the number of epochs (the number of times the data must pass through the model). The higher the accuracy during training, the better the model. However, 100% accuracy may not be the best result. It could mean that the model has just memorised the correct values for the training data (which is an undesirable behavior known as overfitting) but it would still give poor results for new data.

Step 7 – Evaluate the Model

This is done to check the accuracy of the model. It is necessary to evaluate the performance of the model and to prevent issues like overfitting. The parameters may need to be tuned (changed) to get better results after this evaluation. Repeat steps 6 and 7 until the outcome is satisfactory.

There you have it! A quick run-through of Machine Learning concepts, which should be enough to get you started. Once you get these basics out of the way, you can proceed to delve deeper into each of the topics. Feel free to keep coming back and using this article as a cheat sheet to revise your fundamentals.

All the best!