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What Is Machine Learning For Data Science?

This blog aims to give better insights into Machine Learning in the Data Science context, its practical uses, and benefits to the industry. But before delving deeper into this topic, let’s briefly understand what exactly is Data Science and Machine Learning.

In simple terms, Data Science is a collection of disciplines, which includes math, statistics, data engineering, pattern recognition, data modeling, and advanced computing used to extract information and insights from data.

Machine Learning is a tool or a set of algorithms that learns patterns in existing data and then predicts similar patterns in new data.

You could understand machine learning more clearly with this example. When you read an email in your Inbox, you can quickly decide if the email is Spam. But how does a computer recognize a Spam email? Machine Learning helps a computer do that. With the help algorithms, computers learn how to think and perform tasks the way humans do.

Machine learning is not something new; it has been around since a very long time, but since the past few decades, there is a renewed interest due to the massive scalability of data and information, affordable processing power and inexpensive storage allowing for much more accurate predictions than was ever possible in earlier times. Due to these reasons, Machine Learning has moved out of the lab and into our lives with varied applications across industries.

Machine Learning is a subset of data science. A Machine Learning system is made up of a model that makes predictions based on the parameters that it uses to make calculations. The learner in the system adjusts the parameters and in turn, the model, by looking at differences in predictions versus actual outcome. In other words, the learner trains itself to create a better model.

Among the most widely adopted machine learning methods are supervised learning and unsupervised learning.

In Supervised Learning, the learning algorithm receives a set of inputs along with the corresponding correct outputs. The algorithm learns by comparing its actual output with correct outputs to find errors. It then modifies the model accordingly. Supervised learning is commonly used to predict likely future events. For example, credit card companies can use this method to predict which customers are likely to default on their payments in the coming future, or an insurance company would like to predict the frequency of insurance claims. All classification and regression algorithms fall under the category of supervised learning.

In Unsupervised Learning, the data is not labeled. The system is not given the correct output and must find out the right answers by learning, studying the given data, and finding some structure within it. Unsupervised Learning methods are used when the data is difficult to explore and there are no obvious natural groupings, or for market basket analysis to promote business based on personalized choices. Examples of such techniques include self-organizing maps, nearest-neighbor mapping, k-means clustering and singular value decomposition.

Another method, semi-supervised learning is a combination of supervised and unsupervised learning. It uses a small amount of labeled data along with a large amount of unlabeled data for training. This type of learning can again be used for classification and regression tasks. Some popular uses of semi-supervised learning are in face and object detection, multimedia event detection, and so on.

Some of the commonly used Machine Learning techniques are Bayesian networks, Gaussian mixture models, K Means Clustering Algorithm, Support Vector Machine Algorithm, Apriori Algorithm, Linear Regression, Logistic Regression, Artificial Neural Networks, Random Forests, Decision Trees, and Nearest Neighbours.

Machine Learning finds numerous industrial applications.

  • Text Categorization – Segregating text documents into predefined categories, for example, categorizing news articles according to ‘technology’, ‘sports’, ‘politics’, ‘entertainment’, ‘science’, and many others.
  • Character Recognition – Face detection and signature recognition
  • Customer Discovery – To identify customers and offer products and services to only those customers that are mostly likely to be interested in your products or services.
  • Sentiment Analysis – Such as analyzing trends, targeting advertising messages, gauging reactions and evaluating public opinions, bias identification in news sources, and many others.
  • Email Filtering – Filtering spam email
  • Medicine – For cancer diagnosis and drug screening

Good Machine Learning systems require tools and processes that pair up with the best algorithms to aid in deriving the most value from big data. These include:

  • Comprehensive methods to manage data.
  • User-friendly graphical interfaces for not only building the data models and process flows but also visualization of the model results via dashboards and interactive charts. These UIs must also have the ability to compare different models and quickly identify the best one.
  • Having an ensemble of models and having an automated method of evaluating them to identify the best performers.
  • Easy model deployment to get repeatable and reliable results


Machine Learning has taken big leaps in the past few years. These improvements herald a new era in which Machine Learning will help simplify many of the most complex and challenging tasks of dealing with big data and help businesses enhance their productivity, improve revenues, and make informed decisions.

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