Machine Learning: An Overview


“… what we want is a machine that can learn from experience.”

– Alan Turing, 1947


We were born ready to learn, and our brains developed through use. Our first learnings came from observation, exploration, and experimentation. We learn most of the things best through experience. Machines, on the other hand, follow instructions given by humans. What if humans can train these systems to learn from their past data to do things accurately and at a faster pace than us? Well, that’s the essence of machine learning.

Based on algorithms, a machine learns from the existing data and builds a prediction model. So, when a new data point comes in, it can easily predict for it. More the data, better the model and higher will be the accuracy. It allows applications to receive inputs and predict outputs without being explicitly programmed.

Machine Learning Techniques

There are different ways a machine can learn. It could be Supervised Learning, Unsupervised Learning or Reinforcement Learning.

Supervised Learning

The supervised learning algorithm takes a categorized or labeled dataset, and trains a model to generate predictions for new inputs. Let’s say there’s a need to classify the incoming e-mails as spam or genuine. By using machine learning algorithms, the model is trained to identify a mail as spam or genuine based on the characteristics of existing e-mails. Using this logic that is built from past data, the model can determine if a new e-mail is genuine or not.

Unsupervised Learning

In unsupervised learning, the prediction model acts on uncategorized inputs by looking for patterns in the data, without any prior training. For example, a retail store wants to study customer behavior to improve its sales. Based on the purchase pattern of the customers, the model can predict which combination of products are likely to be bought together. This inference can facilitate bundling products thereby enhancing their sales.

Reinforcement Learning

It is a learning method where the machine interacts with its environment by producing actions and discovers rewards or penalties. The system determines the ideal behavior within a specific context to maximize its performance.

Reinforcement Learning is used in many PC Games. In the game of chess, the machine learns and changes its approach based on the inference made from the other player’s moves to optimize the score.

Some applications of Machine Learning

  • Predict diagnostics for a doctor’s review in the field of Healthcare
  • Detect fraudulent behavior in the area of Banking and Finance
  • Tap the potential of Sentiment Analysis on Social Media
  • Predict customer churn in the e-commerce sector

Machine Learning enables the analysis of volumes of data. It guarantees speed and accuracy while identifying opportunities or mitigating risks. When combined with cognitive technologies, it can enhance the processing capabilities of massive real-time data as well.

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