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A Beginner’s Introduction To Machine Learning

a beginner's guide to machine learning

Understanding machine learning might be the key to decoding why you keep getting a certain type of content when you scroll through your Facebook news feed, even when you have no interest in such content?

Have you ever noticed that your reaction on an online post determines how often you’ll see such posts again?

Check out a wristwatch online today, and watch yourself receive more notifications on other types of wristwatches.

Fret not! These are the works of machine learning.

Machine learning is a term coined in 1959 by an American Computer Pioneer, Arthur Samuel, who used a game of checkers to show how much the computer can learn from humans, without it being programmed to do so.

According to him, Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed. It teaches the computer to perform certain tasks without it being programmed to do so.

Instead, it uses a precise step-by-step plan for a computer procedure. This plan is known as an algorithm, and, most times, it begins with an input value. These values are kept in a part of the computer, called the model, which then processes the values and yields an output value in a finite number of steps.


types of machine learning

Machine Learning is of three different types, and, they are:

1. Supervised Learning:

This is a type of learning where the computer learns from specified and labeled data to produce outputs based on them. This way, it can then make predictions on new and unseen data. With this type of learning, you can train a model to differentiate between a dog and a cat based on the pattern of pictures you’ve already introduced to it.

2. Unsupervised Learning:

Here, the model works with unlabelled data. It aims to find patterns, relationships, and structures within the data with no specific label. Using unsupervised learning helps you cluster similar customers together, based on their patterns of buying goods.

3. Reinforcement Learning:

This involves a model interacting with a user or an environment, learning to take actions that maximize a reward signal. This is more like a trial-and-error learning method. A practical use of this is a situation whereby you tell your Google Assistant to correct something it had done, because it works best for you that way.

Machine Learning; A Useful Tool

The ability of machine learning to analyze vast amounts of data and uncover hidden patterns makes it a valuable tool in various industries, some of which are the healthcare sector, e-commerce, fraud and spam detection, recommendation systems, language translation and sentiment analysis, and image and object recognition.

However, It even plays a more crucial role in predictive analytics and data-driven decisions. Here are some of its uses in these areas:

1. Predictive Modeling:

Machine learning algorithms can analyze historical data to make predictions about future outcomes. For example, in finance, machine learning models can be used to predict stock prices or detect fraudulent transactions based on patterns in historical data.

2. Customer Segmentation:

By applying machine learning techniques to customer data, businesses can segment their customer base into groups with similar characteristics and behaviors. This allows companies to tailor their marketing strategies and provide personalized recommendations to different customer segments.

3. Demand Forecasting:

Machine learning models can analyze historical sales data, market trends, and other relevant factors to forecast future demand for products or services. This helps businesses optimize their inventory management, production planning, and pricing strategies.

4. Risk Assessment:

Machine learning algorithms can assess and predict risks in various domains, such as insurance or credit scoring. This way, these models can evaluate the likelihood of specific events or outcomes, enabling businesses to make informed decisions.

5. Decision Optimization:

Machine learning can be used to optimize decision-making processes by analyzing large amounts of data and identifying patterns or trends. This can help businesses make data-driven decisions in areas such as supply chain management, resource allocation, or pricing strategies.

So, there you have it. I trust you now know what machine learning is about, and, how it empowers predictive analytics and data-driven decisions.

John Adebayo
John Adebayo

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