Introduction To Machine Learning For Beginners

In this article, we will learn about Machine Learning algorithms. Top 10 Most useful algorithms, Definitions, Uses, and overview of Machine Learning algorithms.

What is Machine Learning?

-> Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.

-> Machine learning is based on the idea that machines should be able to learn and adapt through experience.

-> Machine learning is used in internet search engines, email filters to sort out spam, websites to make personalized recommendations, banking software to detect unusual transactions, and lots of apps on our phones such as voice recognition.

Machine learning algorithms are classified into 4 types:

  1. Supervised Learning
  2. Unsupervised Learning
  3. Semi-supervised Learning
  4. Reinforcement Learning

1)  Supervised Learning

Supervised learning is the type of machine learning in which machines are trained using well “labelled” training data, and on basis of that data, machines predict the output. The labelled data means some input data is already tagged with the correct output.

Supervised learning, as the name indicates, has the presence of a supervisor as a teacher. Basically supervised learning is when we teach or train the machine using data that are well labelled. This means some data is already tagged with the correct answer.

2)  Unsupervised Learning

Unsupervised Learning Algorithms allow users to perform more complex processing tasks compared to supervised learning.  Unsupervised learning algorithms include clustering, anomaly detection, neural networks, etc.

Unlike supervised learning, no teacher is provided which means no training will be given to the machine.

3)  Semi-supervised Learning

Semi-supervised learning is an approach to machine learning that combines a small amount of labeled data with a large amount of unlabeled data during training. Semi-supervised learning falls between unsupervised learning (with no labeled training data) and supervised learning (with only labeled training data).

Semi-supervised learning uses both tagged and untagged data to fit a model.

4)  Reinforcement Learning

Reinforcement learning is a machine learning training model based on rewarding desired behaviors and/or punishing undesired ones. In general, a reinforcement learning agent is able to perceive and interpret its environment, take actions and learn through trial and error.

  • Linear regression
  • Logistic regression
  • Decision tree
  • SVM algorithm
  • Naive Bayes algorithm
  • KNN algorithm
  • K-means
  • Random forest algorithm
  • Dimensionality reduction algorithms
  • Gradient boosting algorithm and AdaBoosting algorithm

Machine Learning Use Cases

  • Fraud Detection for Secure Transactions
  • Email Monitoring
  • Predicting Behaviour
  • Self-Driving Cars
  • Chatbots
  • Personalized Marketing
  • Healthcare

I hope you’ll get impressed by the features of Machine Learning. So let’s start coding within the next blog. If you’ve got any questions regarding this blog, please let me know in the comments.

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