Machine Learning With Python

( Duration: 5 Days )

Machine Learning With Python training course is designed for people who are already working with Big Data and analysing large statistical sets. By attending this course delegates will learn what an effective Machine Learning approach looks like in an organisation. They will learn to understand different models of Machine Learning and how to implement them. Also covered is how to validate the statistical quality and the metrics attached to that data and how to implement them practically using Python.

By attending Machine Learning With Python workshop, delegates will learn to:

  • The benefits of Data Science with Python
  • Key Python Data Science libraries and packages
  • Data Visualisation with Python
  • Machine Learning with Python

  • Must be comfortable with mathematical and logical way of thinking
  • Familiar with basic programming knowledge: variables, control flow, scope and functions
  • Prior experience with Python would be beneficial

This Machine Learning With Python course is aimed at Analysts, Data Scientists, and Software Developers.



Machine Learning - Overview

  • What is Artificial Intelligence? What's up with the hype?
  • Data Science vs. Data Mining vs. Machine Learning
  • Machine Learning Problems and Applications
  • Python Environment Set-up
    • The Anaconda Python distribution
    • Jupyter Notebooks
    • Python Ecosystem for Data Science and Machine Learning

Machine Learning - Deep Dive

  • Learning and Prediction
  • Feature Engineering
  • Training data and Test data
  • Cross-validation
  • Underfitting and Overfitting
  • Supervised Learning Problems
    • Regression: predicting a quantity
      • Algorithm in depth: Linear Regression and Polynomial Regression
    • Classification: predicting a label
      • Algorithm in depth: k-Nearest Neighbours
      • Algorithm in depth: Support Vector Machine
      • Algorithm in depth: Naive Bayes
  • Unsupervised Learning Problems
    • Clustering: grouping similar items
      • Algorithm in depth: k-Means
      • Algorithm in depth: Hierarchical Agglomerative Clustering
      • Algorithm in depth: DBSCAN
    • Dimensionality Reduction
      • Algorithm in depth: Principal Component Analysis

Deep Learning & Neural Network Overview

  • Intro to Artificial Neural Networks
  • Mathematical Concepts required by Deep Learning
  • Neural Network concepts
    • Neural Network Types
    • Gradient Descend
    • Back-propagation
    • Activation Functions
    • Loss Functions
    • Hyper-parameters
  • Deep Network Architectures
  • Deep Learning Libraries

Encarta Labs Advantage

  • One Stop Corporate Training Solution Providers for over 4,000 Modules on a variety of subjects
  • All courses are delivered by Industry Veterans
  • Get jumpstarted from newbie to production ready in a matter of few days
  • Trained more than 50,000 Corporate executives across the Globe
  • All our trainings are conducted in workshop mode with more focus on hands-on sessions

View our other course offerings by visiting http://encartalabs.com/course-catalogue-all.php

Contact us for delivering this course as a public/open-house workshop/online training for a group of 10+ candidates.