ML / AI

Machine learning and artificial intelligence is one of the fastest-growing and most transformational technologies of our time, with 2.3 million new jobs opening up by 2020.

  • Advanced ML Algorithms
  • NLP
  • Image Processing & Computer Vision
  • Deep Learning
  • Mathematics and Statistics for AI
  • Data Wrangling
  • Deploying Scalable ML Models
Apply Now

Be in Demand
Learn the most recent tech skills

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. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

  • What will be fun?

    Machine Learning Engineers earn a pretty penny. The jobs are on the rise. You will learn how Facebook is using Machine Learning to tag your friends in your uploaded picture. Business Analyst, Product Analyst, Machine Learning Engineer, Data Scientist. These are the job opportunity and demands of these opportunities will increase with time.
  • What will be hard?

    In this course you will learn data structures and algorithms by solving 80+ practice problems. You will begin each course by learning to solve defined problems related to a particular data structure and algorithms.

Get training and experience on live projects

We have developed various Bootcamp modules keeping in mind the recent industry trends and the inclination of the market towards the skill set. The course curriculum has a blend of industry-based training and contemporary technical skills.

  • 1-5 weeks

    Introduction to Python:Python for Data Science, Data Visualisation in Python, Maths for Data Science, Data Analysis Using SQL, Advanced SQL, Tools Covered.
  • 6-10 weeks

    Analytics Problem Solving: Investment Case Study, Inferential Statistics, Hypothesis Testing, Exploratory Data Analysis.
  • 11-15 weeks

    Assignment: Linear Regression, Logistic Regression, Naive Bayes, Unsupervised Learning: Clustering, Unsupervised Learning: Principal Component Analysis.
  • 16-20 weeks

    Lexical Processing, Syntactic Processing, Semantic Processing, Chatbots Project.
  • 21-25 weeks

    Introduction to Neural Networks: Convolutional Neural Networks, Recurrent Neural Networks, Gesture Recognition - Projects.
  • 26-30 weeks

    Introduction to Reinforcement Learning, Exact Methods, Approximate Methods.