Machine Learning Training in Bangalore

Machine Learning Training in Bangalore

Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed. ML is one of the most exciting technologies that one would have ever come across. As it is evident from the name, it gives the computer that which makes it more similar to humans: The ability to learn. Machine learning is actively being used today, perhaps in many more places than one would expect.


A subset of artificial intelligence (AI), machine learning (ML) is the area of computational science that focuses on analyzing and interpreting patterns and structures in data to enable learning, reasoning, and decision making outside of human interaction. Simply put, machine learning allows the user to feed a computer algorithm an immense amount of data and have the computer analyze and make data-driven recommendations and decisions based on only the input data. If any corrections are identified, the algorithm can incorporate that information to improve its future decision making.


Machine learning is made up of three parts:

  • The computational algorithm at the core of making determinations.
  • Variables and features that make up the decision.
  • Base knowledge for which the answer is known that enables (trains) the system to learn.

Initially, the model is fed parameter data for which the answer is known. The algorithm is then run, and adjustments are made until the algorithm’s output (learning) agrees with the known answer. At this point, increasing amounts of data are input to help the system learn and process higher computational decisions.


Data is the lifeblood of all business. Data-driven decisions increasingly make the difference between keeping up with competition or falling further behind. Machine learning can be the key to unlocking the value of corporate and customer data and enacting decisions that keep a company ahead of the competition.


Machine learning has applications in all types of industries, including manufacturing, retail, healthcare and life sciences, travel and hospitality, financial services, and energy, feedstock, and utilities. Use cases include:

  • Predictive maintenance and condition monitoring
  • Upselling and cross-channel marketing
  • Healthcare and life sciences.Disease identification and risk satisfaction
  • Travel and hospitality.Dynamic pricing
  • Financial services.Risk analytics and regulation
  • Energy demand and supply optimization


  • Introduction
  • Supervised and Unsupervised learning
  • Parametric Methods
  • Dimensionality Reduction


  • Getting Started with Machine Learning
  • Artificial Intelligence | An Introduction
  • What is Machine Learning ?
  • An introduction to Machine Learning
  • Introduction to Data in Machine Learning
  • Demystifying Machine Learning
  • Applications
  • Machine Learning and Artificial Intelligence
  • Difference between Machine learning and Artificial Intelligence
  • Agents in Artificial Intelligence

Supervised and Unsupervised learning

  • Types of Learning – Supervised Learning
  • Types of Learning – Part 2
  • Supervised and Unsupervised learning
  • Reinforcement learning

Parametric Methods

  • Regression and Classification
  • Understanding Logistic Regression
  • Understanding Logistic Regression
  • Multivariate Regression
  • Confusion Matrix in Machine Learning
  • Linear Regression(Python Implementation)
  • Softmax Regression using TensorFlow
  • Linear Regression using PyTorch
  • Identifying handwritten digits using Logistic Regression in PyTorch

Dimensionality Reduction

  • Parameters for Feature Selection
  • Introduction to Dimensionality Reduction
  • Underfitting and Overfitting in Machine Learning
  • Handling Missing Values