Python for Machine Learning - Live Instructor-led Training Enroll For Free

Past Events
  • Here's what you'll learn

    Overview of Python for Machine Learning

    Introduction to Machine Learning

    Machine Learning - Examples

    Machine Learning - Applications

    Hands-on Demo on Machine Learning

  • What is Big Data & why is it important

    Big Data - examples and applications

    Introduction to Apache Spark and its components

    Hands-on demo on cloud-based lab

  • There shall be an online test for 1.5 hours which would consist of objective questions based on mathematics required for Machine Learning.

    • Topics that it will cover are linear algebra, probability theory, statistics, multivariable calculus, algorithms, time complexity, aptitude and Data Interpretation,

    • Number of questions will be 25,

    • Each question will have a weightage equal to the score it is for,

    • No negative marking, and

    • The test link will be sent only 30 minutes before the scheduled start time.

  • Here's what you'll learn

    • What is Big Data & why is it important

    • Big Data - examples and applications

    • Hadoop ecosystem & details of various components

    • Hands-on with the components in Hadoop

    • Understanding the Spark architecture

    • CloudxLab hands-on demo

  • Here's what you'll learn

    • What is Big Data & why is it important

    • Big Data - examples and applications

    • Hadoop ecosystem & details of various components

    • Hands-on with the components in Hadoop

    • Understanding the Spark architecture

    • CloudxLab hands-on demo

  • You will learn the below topics

    • Introduction to Deep Learning
    • Deep Learning Applications
    • Introduction to Artificial Intelligence
    • Getting started with TensorFlow
    • Hands-on examples on TensorFlow

  • Here's what you'll learn

    What is Big Data & why is it important

    Big Data - examples and applications

    Introduction to Apache Spark and its components

    Hands-on demo on cloud-based lab

  • Here's what you'll learn

    Overview of Python for Machine Learning

    Introduction to Machine Learning

    Machine Learning - Examples

    Machine Learning - Applications

    Hands-on Demo on Machine Learning

  • Deep learning is a promising approach for extracting accurate information from raw sensor data from IoT devices deployed in complex environments. Because of its multilayer structure, deep learning is also appropriate for the edge computing environment.

    Sandeep will provide an introduction to Deep Learning, core technologies, methodologies and approach on how Deep Learning can enable IoT applications.

  • There shall be an online test for 1.5 hours which would consist of objective questions based on mathematics required for Machine Learning.

    • Topics that it will cover are linear algebra, probability theory, statistics, multivariable calculus, algorithms, time complexity, aptitude and Data Interpretation,

    • Number of questions will be 25,

    • Each question will have a weightage equal to the score it is for,

    • No negative marking, and

    • The test link will be sent only 30 minutes before the scheduled start time.

  • This session would broadly cover the following:

    • Introduction to Machine Learning and AI
    • What is Classification?
    • Hands-on example on binary classification using MNIST dataset
    • Understanding Stochastic Gradient Descent (SGD) Classifier
    • Training a binary classifier using SGD
    • Performance measure

      • Cross Validation
      • Confusion Matrix
      • Precision and Recall
      • F1 Score
      • Precision / Recall Tradeoff
      • ROC Curve

  • This session would broadly cover the following:

    • End-to-end Machine Learning project
    • This bootcamp is designed to be an entirely hands-on experience for you.

  • In this workshop, you will be introduced to six modules. Each module introduces one or two core Machine Learning concepts while working through the practical implementation.

    • Overview of Machine Learning

    • Manipulation and plotting data

    • Preprocessing and exploring data

    • Training a binary classifier

    • Measuring Performance

    • Learning Multi-class classification