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Machine Learning is undoubtedly the most demanded technology in the market today. Machine Learning applications range from speech/image recognition to self-driving cars, to disease detection, and many more to go. The high demand for Machine Learning is motivating young tech enthusiasts to take up the course, learn, and research more in this field. Here, in this blog, you will understand a few of the basic concepts of Machine Learning and its practical implementation.

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Why Machine Learning?

The technical evolution has been generating a vast amount of data every day. It is estimated that nearly 1.5-2 MB data will be created every second by each individual on the planet. This will generate a lot of data, which needs to be analyzed and studied using predictive models and complex algorithms that can deliver accurate results on the data. Machine Learning is required here to analyze, structure, and draw useful and valuable insights from the given data. It also provides decision-making algorithms that can be used by companies to make better business decisions. It is also used to solve complex like detecting complex diseases. Machine Learning has many uses and applications, and hence, it is the present and the future of technology. Netflix recommendation engine, Amazon Alexa, Facebook auto-tagging are a few of the applications of Machine Learning. 

What is Machine Learning?

It is basically a part of Artificial Intelligence, which gives machines the ability to learn and perform various activities after gaining experience in that field. It interprets and plays with the provided data and results in useful information and prediction analysis. 

Machine Learning Process :

The process involves constructing a predictive model used to find the solution to a given problem. The process is carried out in steps in order to get the most accurate result. Steps are :

Step 1: Defining the objective (What needs to be predicted?)

Step 2: Data gathering (Data availability, data type etc.)

Step 3: Preparing the Data (in proper formats and consistency)

Step 4: Data Analysis(Deep diving into data to produce meaningful results)

Step 5: Building a ML Model (Based on the objective and data received)

Step 6: Evaluation and Optimization (Testing and evaluating the model)

Step 7: Predictions (For getting the desired results)

It is fascinating and captivating to see the machine playing with the data and giving out the predictions.

Types of Machine Learning :

Machines can solve problems by using any of the given approaches. These approaches are ways as to how a machine can learn the process. 

  1. Supervised Learning (a process where we teach or train machine on a well-labeled data)
  2. Unsupervised Learning (a process that contains unlabeled data and machines are to act on the data without any guidance)
  3. Reinforcement Learning (here agents ought to learn and take actions in a given environment by trial and error method, using feedback from its actions and experiences)

Machine Learning is an exciting field to try your hands on. If you are curious about how data is manipulated and predicted through machines, how the algorithms work on machines and how the predictions and results are generated, then you should definitely try your hands on Machine Learning. 

Learn more about Machine Learning and Data Science and carve your way to a bright future. 

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