As we are going ahead with technological advancements, the use of Machine Learning is also growing exponentially. Things that could only be done by human beings are now being done by machines without any human interference.
Before going ahead, let us have a look at what is machine learning? Machine learning is a technology which allows computer or machine to learn from the fed data and lets them make decisions with accuracy. Just like humans, with the help of machine learning, machines can learn from previous outcomes and increase their intellect.
Types of machine learning
Although there are various types of machine learning approaches, not all of them have many applications in the real world. For now, let us talk about the three major machine learning types or techniques that are being widely followed.
1. Supervised machine learning- When a large amount of labeled data is fed to the algorithms, it is called supervised machine learning. Data scientists also define the variables they need the algorithms to assess for correlations. However, supervised machine learning needs a vast pool of data to master the tasks.
2. Unsupervised machine learning- In unsupervised machine learning, algorithms don’t have any labeled responses. This technique is mostly used when data scientists want to explore their data without having a specific goal. The algorithm browses through data sets and starts dividing data into groups based on similar characteristics.
3. Reinforcement Learning- When a machine is taught to complete a multi-step process with clearly defined rules is called Reinforcement Learning. The algorithm takes decisions on its own & is built in such a way that it gets rewarded or faces penalties based on its actions.
Top machine learning algorithms
A Machine Learning algorithm is a system running on data that is used to build a production-ready machine learning model. Machine learning algorithms act as the engines driving the success of the task. There exist various types of machine learning algorithms that are used in the process. Choosing a specific type of machine learning algorithm depends on the nature of the dataset and the business problem you are solving.
Without any further ado, let us look at the top machine learning algorithms used by data scientists.
1) Linear Regression
The Linear Regression algorithm determines the connection between 2 variables (input and output) and how the change in one variable affects another variable. There are independent and dependent variables. Independent variables being the ones that explain the factors that impact the dependent variable, while the dependent variable is referred as the factor of interest or predictor.
A few common examples of linear regression are sales and weather predictions, employee salary estimations, etc.
The best example of linear regression from real life would be making students stand height-wise in the morning assembly.
2) Logistic Regression
This algorithm’s name could be a little confusing as the Logistic Regression algorithm is broadly used for classification tasks and not for regression problems. Logistic regression implements a logistic function to a linear combination of features to foretell the outcome of a categorical dependent variable. Putting it in simple words- The main use of logistic regression is to predict whether an event will occur or not.
The odds or probabilities that describe a single trial’s outcome are modeled as a function of explanatory variables.
Let’s suppose we want to predict the density of snowfall in New York. Here, the prediction outcome is not a serial number because there can either be snowfall or no snowfall and consequently, linear regression cannot be applied.
3) KNN Algorithm
KNN aka K-Nearest Neighbors is a supervised type of machine learning algorithm that can solve both classifications as well as regression problems.
K-Nearest Neighbors algorithm is one of the most used machine learning algorithms major reason being its simplicity. KNN Algorithm works on a simple principle- that every data point falling near each other comes in the same class, which works on the assumption that the things near each other are like each other.
KNN is mostly used in places like economic forecasting, Video and Image Recognition, and Speech Recognition.
E-commerce and OTT companies like Amazon and Disney+ Hotstar make use of KNN to recommend products and movies/shows to their customer based on their previous actions. These companies collect data on user’s behavior, like products they have added to their cart or movies you have watched. The products or the movies will then be recommended to you based on how the algorithm classifies the data points.
4) Naïve Bayes
Naïve Bayes is probably the simplest, fastest, accurate, and reliable machine learning algorithm. It is used for so many purposes, but it works magnificently with natural language processing (NLP) problems. Don’t you think it would be practically impossible to classify a document, an email, a webpage, or any other lengthy text notes manually? Well, the Naïve Bayes lands the ground as a savior.
Spam Filtering in emails and weather forecasts are some of the typical applications of the Naïve Bayes.
Naïve Bayes Classifier works on the famous Bayes Theorem of Probability. Naive Bayesian algorithms’ basic assumption is that all the features are considered to be independent of each other. It is handy for large datasets and can be very easily implemented for text datasets.
5) Decision Tree
A decision tree is basically a graphical representation that uses a branching methodology to illustrate all possible outcomes of a decision based on given conditions. Each branch of the tree represents the outcome of the trial.
Let us say you are planning to visit the best restaurant in town on the weekend, but you are confused about which restaurant to choose. Now you are asking your friend Tyrion for some suggestions. To answer your question, Tyrion first needs to find out the kind of restaurants you like. You name him a few restaurants you have visited so far and tell him which one you liked or not (giving a labeled training dataset). and let’s say you told him that you loved a restaurant named “A.” Based on your choice Tyrion will ask you different questions like “Is “A” a rooftop restaurant?”, “Does restaurant “A” serve Japanese cuisine?”, “Does A have live music?” and so on. Tyrion asks you several questions to collect more and more information about your preferences and give you “YES” or “NO” answers based on your responses to the questionnaire. Here Tyrion is a decision tree for your favorite restaurant preferences.
6) Random Forest
Data scientists and machine learning engineers consider Random forest as one of the most useful algorithms. As the name goes- the random forest is a forest of randomness. It is the arbitrary sample of data for the decision trees that form a random forest. The collection of results of the decision tree eventually makes the random forest result or prediction, depending on whichever class gets the most votes — wins. This also helps in preventing overfitting.
Some business use cases of Random Forest include- grouping different products for an e-commerce site.
As a classification algorithm, we can use a robust supervised algorithm that works well with multiple classes to classify product categories accurately.
E-commerce sites have hundreds of types or classes of products, but it would take hours to classify them manually. The random forest is perfect for this use case (of course, depending on the dataset).
But you must be very cautious about your training data and products. It should be well defined and separated. Otherwise, the model will have trouble classifying them.
- For instance, boots and hiking boots!
- Fix: make different categories; construction boots, hiking boots, and snow boots. Now, we have three unique types of categories of boots rather than just “boots” and “hiking boots” that would undoubtedly overlap.
Most beginners in the field of machine learning get confused because of the vast number of algorithms and techniques out there in the market.
Well, the answer depends on factors, like,
- Your experience levels.
- The nature and size of the data.
- Available time.
- Desired results.
However, when you are just getting started, make sure that you start with simple algorithms like KNN and decision tree and then eventually move ahead. And if you still haven’t started your Big data journey and wish to build your career in big data, I would suggest you start right away. The field is growing at the speed of light, and the sooner you understand the scope, the better it is.