Everything You Need to Know About Machine Learning

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I remember the excitement and awe beaming through my face, as I watched Bruce Banner explain the deets of time travel to War machine and ant-man, going on about ‘How changing the past doesn’t change the present’, in 2019’s critically acclaimed blockbuster movie Marvel’s Avengers: Endgame. I had long waited for a big movie to fix the idea in people’s heads that such is true.

But, I’m not about to write about which movie is right about time travel and which movie is wrong, but about how Humans made a machine or rather some set of algorithms that helps us accurately predict the future with what we know of the present and past, It’s called Machine Learning.

What Is Machine Learning

Machine learning is the twenty-first-century science that borders on finding patterns and making intelligent conjectures from processed data based on results from data mining, pattern recognition, multivariate statistics, and advanced/predictive analytics. 

Who Uses Machine Learning

Amazon, Google, Twitter, Spotify, Netflix are all employing machine learning to streamline content based on what would interest you. When Amazon recommends a book you will like or when google reminds you to eat now so you’ll make your meeting on time or when Netflix suggests a series that you end up being glued to and when Spotify constantly updates your playlists with music you can’t help but love. 

But how they know what you’ll like in the future is down to a couple of mathematical models that fall under an application of machine learning called predictive analysis.

Under predictive analysis, we have a couple of machine-learning models that companies use to tell what their business would look like at any possible time in the future. Machine learning models are the heart of predictive analytics, and here are some of the most common models used by companies.

  • Customer segmentation model: Customer Segmentation (CS) is the sub-division of consumers into separate groups with identical characteristics. Customer segmentation helps with pinpointing areas where customers’ needs aren’t being met. Most common ways customers are separated is by:
  • Geography, such as the customer’s city, state, city, or country.
  • Demography, such as age, income, gender, marital status, academic and marital status.
  • Psychographics, such as social class, lifestyle, and personality traits.
  • Behavioral data, such as spending habits and consumption habits, products, and service usage. 

Customer segmentation helps develop highly effective customer campaigns, give appropriate prices of products, develop optimized distribution strategies. Which, in turn, helps them wade off competitors by delivering attractive top-notch products and services.

  • Customer Lifetime Value Model: Identifies customers who are most likely to invest more in products and services. This model helps determine customers who are more likely to continue using a product and even refer people to it.Customer lifetime value (CLV) is the term used to describe the entire worth that a customer (or group of customers) provides a company throughout the client’s relationship with the company. CLV can be estimated from historic data, particularly where a level of value is attributed to particular groups of customers or in cases where customers are acquired from unique sources.   
  • Quality Assurance Model: Spot and prevent defects to avoid disappointments and extra costs when providing products or services to customers. When employed for quality assurance, this machine learning model provides an abundance of information on commonplace errors, malfunction patterns, and critical effects that can affect the stability of software stability. 
  • Predictive Maintenance Models: this model collects and learns from historical data and live data to analyze failure patterns in machines. Our modern world is heavily dependent on machines and systems for optimum functioning. Almost everything we use on a day-to-day basis is controlled by a machine from ubiquitous tools like light switches and smartphones to elevators and automobiles.However, every machine experience wear and tear sooner or later. Predictive maintenance predicts the likelihood of a system or machine failing and when it would fail. While also including corrective actions, that is, the replacement of a system or machine parts, or even planned failure. Which would, in turn, save businesses millions of dollars in the cost of machine damages and failure. While also increasing the availability of machines and systems continually.         

But the success of these models is dependent on the accuracy of the machine learning algorithms used on the models. A model is only as good as the algorithm handling the data. Here are a few of the commonly used algorithms for predictive analysis.

  • Linear Regression measures the interconnection between one or more predictor variable(s) and an outcome variable. Linear regression is a very common machine learning algorithm employed in modeling and for predictive analysis. For instance, this algorithm can be used to evaluate the corresponding impacts of gender, age, and diet (predictor variable) on height (outcome variable).
  • Logistic regression is a categorization algorithm that comes into play when appropriating observations to a discrete set of classes. This mathematical statistics model is most suitable in estimating the likelihood of an event happening when provided with some of its historical data.
  • K-Means Clustering is one of the uncomplicated and conventional unsupervised machine learning algorithms. But before going further, let’s classify what unsupervised learning is, actually there is another counterpart called supervised learning, so what do they mean? Supervised learning refers to the technique of managing and completing a task by offering training, input, and output patterns to the systems.Unsupervised learning, on the other hand, is a self-learning technique where no prior set of categories are used, and systems are responsible for identifying the features of the input population.Consequently, K-Means Clustering uses an unsupervised learning algorithm for solving clustering problems via a straightforward method of categorizing the given data into a set of clusters, represented by the letter “k,” which is defined beforehand. These clusters are then arranged as points with all observations or data points connected to the nearest cluster being computed and adjusted. This process is repeated using the new adjustments until a satisfactory result is attained.

These are just some of the machine learning models and algorithms out there, but even though the complex theories of machine learning don’t make it as soothing to the ear as a time machine that maximizes the intricacies of the quantum realm to travel to any point in time.

Machine Learning makes up for itself in many other ways, especially considering the fact that it is still at its Infancy;  But maybe…just maybe some years into the future machine learning might help us travel back in time, in a similar fashion as to how Tony Stark and his gang of heroes did to defeat the mad titan ‘Thanos.’ But for now, machine learning will keep helping companies make us love and stay glued to their products, the more.

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