Introduction To Machine Learning And Its Applications

Machine learning is one of the fastest-growing technologies and it is going to play a pivotal role in the future of technology. With the help of various algorithms machine learning is used to build mathematical models that have the capability to make predictions based on historical data or past data. Currently, it is already being used for numerous tasks such as image and speech recognition, spam email filtering, Facebook auto-tagging, product recommender systems, etc.

This is the first article of the Machine Learning series in which we are going to cover basic and advanced concepts of machine learning. In this article you will learn the following:

  • What is Machine Learning?
  • How does Machine Learning work?
  • Machine Learning Life cycle
  • Applications and Examples of Machine Learning
  • Types of Machine Learning
  • Next Step

So without further delay, lets begin our Machine Learning journey!

What is Machine Learning?

Humans learn from their past experiences, and then we have machines/computers which work according to our instructions. What if machines have the learning capability to learn from past experiences/data? That’s where machine learning comes into the picture.

Let’s have a look at what Wikipedia says:

source: wikipedia

Thus, Machine learning is a subset of AI (artificial intelligence) which allows a machine to learn automatically from past data and improve its performance from experiences of its own. Machine learning algorithms are used to build mathematical models with the help of historical data (also known as training data) which allows it to make decisions and predictions without the requirement of being explicitly programmed. The more data it receives (in simple words increase in experience), the higher its efficiency and performance.

How Does Machine Learning Work?

We learned that a machine learning model learns by itself; but how does it do so?

A Machine Learning Model:

  • Initially, it learns from historical data or training data,
  • then it builds the prediction models.
  • Whenever new data is received by the model, it predicts the output for it.

Please have a look at the diagram given below which gives us an overview of how a machine learning model works:

To further understand how the machine learning model works, let us have a look at its life cycle.

Machine Learning Life Cycle

A proper machine learning model has the ability to learn and improve its performance by gaining more and more data and it does so by undergoing a cyclic process.

As evident from the above image, the machine learning process undergoes seven major steps:

  1. Data Gathering: To train a machine learning model we need data. Thus, data gathering is the first and foremost step in the machine learning lifecycle wherein data is collected from various sources and integrated together to create a combined set of data known as the dataset.
  2. Data Preprocessing: After the data set is ready it undergoes data preprocessing in which the data is transformed, or encoded so that the machine can easily read and parse it.
  3. Data Wrangling: Real-world applications have various errors and issues like missing values, duplicate data, invalid data, and noise that can hamper the training model and the final outcome. Hence, it is extremely important to deal with such issues and make the raw data understandable so that it can be easily understood by the machine learning algorithm. This process is known as data wrangling.
  4. Data Analysis: Once the data is ready to be processed it is used to build a machine learning model using numerous analytical techniques.
  5. Training Model: After data analysis, the model is trained using various algorithms so that it can understand the provided patterns, features, and rules. This allows the model to improve its performance and efficiency.
  6. Test Model: Once the model is trained it undergoes testing where it is checked for its accuracy and efficiency.
  7. Deployment: Finally the model is deployed in the form of a real-world application.

Applications of Machine Learning

Machine Learning is the future of of automation and almost all of us have been using machine learning in our day to day life knowingly or unknowingly. The following representation shows the applications of machine learning:

Now, let us have a look at few real world examples of the above applications of machine learning:

Types of Machine Learning

Machine learning can be broadly categorized into three types:

Supervised Learning

In supervised learning, a sample labeled data is fed to the machine learning model to train it, based on which it predicts the final outcome. Therefore, supervised learning allows us to create a model using labeled data that reads the datasets and learns each feature of the data-set. After training and processing are complete then the model is tested with the help a sample data to check its efficiency of predicting the output on future data. Thus, the goal of supervised learning mapping input data to the output data.

In simple terms, supervised learning is the machine learning technique that is based on supervision, just like a student who learns under the supervision of a teacher.

Supervised learning can be further divided into two groups or two categories of algorithms:

  • Classification
  • Regression

Example: Email Spam Filtering!

Unsupervised Learning

As the name suggests, unsupervised learning is a machine learning technique wherein the machinery model learns without any supervision. The model receives its training from a data-set that is unlabeled or uncategorized and the algorithm works without the need of a supervisor. The goal of unsupervised learning is to reorganize the input data into a group of objects with similar patterns. Therefore, in unsupervised learning, the results are not predetermined.

Unsupervised Learning can be further classifieds into two categories of algorithms:

  • Clustering
  • Association

Reinforcement Learning

Reinforcement learning is a type of feedback-based learning technique, wherein a learning agent is rewarded for each correct action while it gets a penalty for every wrong action. With the help of the feedbacks, the agent learns automatically and improves its performance. In reinforcement learning, the goal of the agent is to acquire the maximum reward points, to improve its performance.

Example: Robot Dog learning the movement of its arms!

Next Step: Data Preprocessing!

Now we have an overview of what is machine learning, how it works, its applications and examples, and its types. It is now time to move on to the next phase of our journey i.e., Data Preprocessing. Please feel free to click on the link/button given below to move on to the next tutorial on Data Preprocessing.

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