Machine Learning

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

Machine learning may have enjoyed enormous success of late, but it is just one method for achieving artificial intelligence.

Types of Machine Learning

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

Some commonly used machine learning algorithms are Linear Regression, Logistic Regression, Decision Tree, SVM(Support vector machines), Naive Bayes, KNN(K nearest neighbors), K-Means ,Random Forest, etc.

  • Traditional Programming : We feed in DATA (Input) + PROGRAM (logic), run it on machine and get output.
  • Machine Learning : We feed in DATA(Input) + Output, run it on machine during training and the machine creates its own program(logic), which can be evaluated while testing.

Data : It can be any unprocessed fact, value, text, sound or picture that is not being interpreted and analyzed. Data is the most important part of all Data Analytics, Machine Learning, Artificial Intelligence. Without data, we can’t train any model and all modern research and automation will go vain.

Information : Data that has been interpreted and manipulated and has now some meaningful inference for the users.
Knowledge : Combination of inferred information, experiences, learning and insights. Results in awareness or concept building for an individual or organization.

Split the data set into three types

  1. Training Data
  2. Validation Data
  3. Testing Data

Pre-requisites to learn ML:

  • Linear Algebra
  • Statistics and Probability
  • Calculus
  • Graph theory
  • Programming Skills