Introduction to Machine Learning and Libraries

What is Machine Learning?

An Artificial Intelligence(AI) that computers able to learn by themselves without being explicitly programmed. It is a kind of method that automates the model building.

Machine Learning is used anywhere from automating mundane tasks to offering intelligent insights, industries in every sector try to benefit from it. You may already be using a device that utilizes it. For example, a wearable fitness tracker like Fitbit, or an intelligent home assistant like Google Home. But there are much more examples of ML in use.

  • Prediction — Machine learning can also be used in the prediction systems. Considering the loan example, to compute the probability of a fault, the system will need to classify the available data in groups.
  • Image recognition — Machine learning can be used for face detection in an image as well. There is a separate category for each person in a database of several people.
  • Speech Recognition — It is the translation of spoken words into the text. It is used in voice searches and more. Voice user interfaces include voice dialing, call routing, and appliance control. It can also be used a simple data entry and the preparation of structured documents.
  • Medical diagnoses — ML is trained to recognize cancerous tissues.
  • Financial industry and trading — companies use ML in fraud investigations and credit checks.

Machine Learning Types

A machine can learn to solve a problem by following any one of the following three approaches. These are the ways in which a machine can learn:

Supervised learning is a technique in which we teach or train the machine using data which is well labeled.

Unsupervised learning involves training by using unlabeled data and allowing the model to act on that information without guidance.

Reinforcement Learning is a part of Machine learning where an agent is put in an environment and he learns to behave in this environment by performing certain actions and observing the rewards which it gets from those actions

Machine Learning Libraries in Python

Important Machine Learning Libraries in Python:

  1. Pandas: handling data structure and used for data manipulation analysis.
  2. Numpy : the fundamental package for scientific computing with Python.
  3. Matplotlib : used for plotting the graph.
  4. SciKit learn( sklearn ): used for model building, it contains various Machine Learning algorithms.
  5. NLTK(Natural Language Tool Kit): a leading platform for building Python programs to work with human language data.

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