Top 6 Machine Learning Techniques That Every Newbie Should Know

Machine learning is the latest advancement in the field of computer sciences that uses tech-savvy methodologies to improve business performances.

Machine learning algorithms, being relatively new, these techniques are in constant iteration to enhance user-experience. The continuous upgrades and developments not only overwhelm the newbies but makes it challenging for experts to keep up with new advancements.

Based on mathematical expressions, the algorithm of machine learning offers data-centric insights into a problem or barrier. Consider the following example that perfectly demonstrates the usage and application of machine learning algorithm:

As a business owner, if you want to predict your future sales, then you need to gather data related to previous sales and other relevant factors, such as seasonal discounts, consumer persona, and world economics. A machine learning algorithm will use all the information and forecast the sales rate in the upcoming years while also identify elements that can hinder the process. Since the estimates are based on data; thus accuracy is 99% guaranteed,

Similarly, the business organization or manufacturer can identify equipment faults, lifespan, and proficiency of tools, using machine learning algorithms.

Below, we have identified six machine learning techniques that build the foundation for machine learning. The easy-to-understand descriptions and relevant examples offer maximum grip on the subject and ensure perfection. It will also help improve skills and build a strong foundation for new advancements.

1. Regression

It is based on the basic principles of physics that help in predicting the future based on current data. It also helps you find the correlation between two variables to define the causal-effect relationship. You can plot a graph based on these variables and make forecast a continuous output, based on the predictor variable.

However, there are various forms of regression, starting from linear to complex, polynomial data calculation, and representation. You should always start from the basics, which means mastering linear regression and then moving to complex forms.

The common examples of linear regression are:

• Weather forecast
• Predicting market trends
• Identifying potential risks

2. Classification

The method defines a class value based on the input data. It will give you definitive predictions of a certain action. For instance, it will tell you if the visitor will become a customer or not.

However, the classification isn’t based on two categories only but multiple due to its probability calculation. For example, it can help you determine if the given picture contains a flower or a leaf; the classification method will give you three probable results: 1) flower, 2) leaf, 3) none.

The above example discussed is based on logistic classification, which is the easiest of all. Once you excel, you can hone your skills in non-linear classifications.

3. Clustering

It is an unsupervised machine learning technique, in which the similar traits are used to make a prediction, instead of past data. The algorithm uses visual cues to design a solution. K-Means is the most popular method of clustering the input, which allows you to set the value of K and categorize data based on K value.

Consider the energy-efficient building example we discussed above. Now to cluster set a similar building, you need to set the K value (which we assume to be 2) and input the variables, such as plug-in equipment, cooling units, a domestic gas (stoves), and commercial gas (heating units).

Since K’s value is 2, there will be two clusters: efficient buildings and inefficient buildings based on the set variables.

4. Dimensionality Reduction:

It is the process of reducing random variables while categorizing the data. The higher the number of variables, the complex the results will be, making it difficult to consolidate it.

Feature selection and extraction are the core of dimensionality reduction in machine learning. It allows you to drop out irrelevant variables. For instance, if you’re to predict the risk of weight gains in a group of people, you wouldn’t want to measure data based on their clothing; however, lifestyle is a detrimental factor, which can be omitted.

The most common example of dimensional reduction is the email classification process used to sort out spam emails. Typically, it uses a great number of variables such as email titles, content, and the template of the email, among other variables. But there are chances that the algorithm may overlap certain factors that can affect the output. Thus to make accurate guesses, the software incorporates Dimensionality Reduction to mitigate the chances of repetition and provide you with accurate results.

5. Ensemble Method:

It is a technique to stack data by using prediction variables from various models. Therefore, it combines various predictive models to form a highly accurate and optimized predictive output. The method is used to make decisions while considering various factors.

For instance, you plan to buy a property downtown, the ensemble method will predict your answer based on various factors such as property type, value, savings, long-term investment goals, and economic conditions. The method is used to find the most accurate answer to a problem in various scenarios. Thus, you can change each variable’s value every time to forecast the results or answers.

Random Forest algorithm is a typical example of ensemble methods that blend various decision trees based on multiple data sets. Due to which the predictive output is of much better quality compared to the estimates of a single Decision tree.

A single machine learning algorithm can be accurate in a certain situation, but the result could turn extremely incorrect in a different setting. Thus to minimize such inaccuracies, data scientists use the ensemble method for a more corrective prediction: Kaggle, an online ML competition portal, incorporated Ensemble Method to score the participants.

6. Neural Networks and Deep Learning

Unlike linear models, the neural network is based on a complex, divisional pattern of data. It comprises multiple layers of a parameter to provide you with a single and precise output. However, the model is still based on linear regression but uses multiple hidden layers; therefore, called a neural network.

The term Deep Learning indicates the complex knowledge required to summarize those multiple parameters. The technique is still in its developmental stages, making it difficult to stay up-to-date with the latest advancements.

Data scientists with expertise in deep learning require high graphical processing units to process large chunks of data. Therefore, these techniques are highly successful in genres related to visuals, audio, and video.

Conclusion

Here we have discussed only the six most common machine learning techniques that every beginner should be aware of. As you progress, you will be able to surpass more complex methods of ML implemented for accurate results.

This article serves as a starting point for developing your basic knowledge of computer science’s most advanced branch. With future development, you will be introduced to intricate elements, such as quality metrics and cross-validation, to name a few.

As a data scientist, your journey is an on-going one due to new inventions and the field’s latest technologies. So stay tuned for future updates!