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Overview of Machine Learning

In today’s data-driven world, we are generating a huge amount of data every second across various domains, based on that data we can predict the future possibilities, so machine learning will help us simplify the process of getting the prediction by outcome by applying different algorithms.

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What is Machine Learning?

Machine learning is an application of AI that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on getting expertise based on data and use them to learn for themselves

Machine Learning

  • Detecting patterns and trends
  • Statistical analysis
  • Creating software models

Examples

  • Predicting the success of the medical intervention
  • Identifying fraudulent financial transactions
  • Recommending books or movies
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The critical steps to building machine learning models:

  • Obtain raw data
  • Preprocess the data
  • Prepare the data
  • Apply one or more machine learning algorithms to the data
  • Determine the best model to use
  • Deploy the model

The general lifecycle for creating ML models

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Machine learning model: the code that is generated after the execution of an algorithm.

Training models:
  • Experiments
  • Evaluation
Deploying models:
  • Applications
  • Retraining

Types of machine learning Algorithms

There some variations of how to define the types of Machine Learning Algorithms but commonly they can be divided into categories according to their purpose and the main categories are the following:

  • Supervised learning
  • Unsupervised Learning
  • Reinforcement Learning

Supervised learning

Supervised learning as the name indicates the presence of a supervisor as a teacher. In supervised learning, we use the data which is having known inputs and known outcomes (labelled data) to train the machine. In simple words, A supervisor will help the model to identify the input features and its target outcomes.

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Types of Supervised learning:

Regression: A regression problem is when the target variable is a numerical value, such as “income” or “weight”.

Classification: A classification problem is when the target variable is categorical, such as “yellow” or “blue” or “Spam” and “Not Spam”.

Algorithms used for supervised learning:

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Unsupervised learning

Unsupervised learning is the training of machine using un-labelled data and allowing the algorithm to act on that data without any supervision. Here the algorithm is used to group unsorted data according to similarities, patterns, and differences without any previous experiences.

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Algorithms used for supervised learning: K-means clustering.

Call Record Detail Analysis:- A call detail record (CDR) is the information captured by telecom industries to know the customer demography information based on the last 24 hrs traffic.

Reinforcement Learning

Unlike supervised learning, the model self-learns through feedback.

  • Reinforcement learning - A machine automatically takes a decision based on the previous decision in a particular situation.
In other words, An algorithm, or agent, learns by interacting with its environment. The agent receives rewards by giving correct outcomes and penalties for providing the wrong result. The agent learns without intervention from a human by maximising its reward and minimising its penalty.

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To know more about using the right algorithm, click here
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