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Course Skill Level:

Foundational to Intermediate

Course Duration:

2 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:


Who should attend & recommended skills:

Those with Python experience and basic IT & Linux skills seeking smarter predictions using Python

Who should attend & recommended skills

  • This course is geared for Python experienced developers, analysts or others with Python skills who are intending to explore the web and make smarter predictions using Python.
  • Skill-level: Foundation-level Machine Learning for the Webskills for Intermediate skilled team members. This is not a basic class.
  • IT skills: Basic to Intermediate (1-5 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su
  • Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them

About this course

Python is a general purpose and also a comparatively easy to learn programming language. Hence it is the language of choice for data scientists to prototype, visualize, and run data analyses on small and medium-sized data sets. This is a unique course that helps bridge the gap between machine learning and web development. It focuses on the difficulties of implementing predictive analytics in web applications. We focus on the Python language, frameworks, tools, and libraries, showing you how to build a machine learning system. You will explore the core machine learning concepts and then develop and deploy the data into a web application using the Django framework. You will also learn to carry out web, document, and server mining tasks, and build recommendation engines. Later, you will explore Pythons impressive Django framework and will find out how to build a modern simple web app with machine learning features.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Machine Learning for the Web instructor, students will learn about and explore:
  • Two big and prominent markets where sophisticated web apps are of need and importance.
  • Practical examples of building machine learning web application, which are easy to follow and replicate.
  • A comprehensive tutorial on Python libraries and frameworks to get you up and started.
  • Getting familiar with the fundamental concepts and some of the jargons used in the machine learning community
  • Using tools and techniques to mine data from websites
  • Grasping the core concepts of Django framework
  • Getting to know the most useful clustering and classification techniques and implement them in Python
  • Acquiring all the necessary knowledge to build a web application with Django
  • Successfully building and deploying a movie recommendation system application using the Django framework in Python

Course breakdown / modules

  • General machine-learning concepts
  • Preparing, manipulating and visualizing data NumPy, pandas and matplotlib tutorials
  • Scientific libraries
  • When to use machine learning

  • Clustering algorithms
  • Dimensionality reduction
  • Singular value decomposition

  • Model error estimation
  • Generalized linear models
  • Naive Bayes
  • Decision trees
  • Support vector machine
  • A comparison of methods
  • Hidden Markov model

  • Web structure mining
  • Web content mining
  • Natural language processing
  • Postprocessing information

  • Utility matrix
  • Similarities measures
  • Collaborative Filtering methods
  • CBF methods
  • Association rules for learning recommendation system
  • Log-likelihood ratios recommendation system method
  • Hybrid recommendation systems
  • Evaluation of the recommendation systems

  • HTTP the basics of the GET and POST methods
  • Writing an app most important features
  • Admin

  • Application setup
  • Models
  • Commands
  • User sign up login/logout implementation
  • Information retrieval system (movies query)
  • Rating system
  • Recommendation systems
  • Admin interface and API

  • Application usage overview
  • Search engine choice and the application code
  • Scrapy setup and the application code
  • Django models
  • Integrating Django with Scrapy
  • PageRank: Django view and the algorithm code
  • Admin and API