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Machine Learning for the Web

  • Course Code: Artificial Intelligence - Machine Learning for the Web
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 2 Days Audience: This course is geared for Python experienced developers, analysts or others who are intending to Explore the web and make smarter predictions using Python

Course Snapshot 

  • Duration: 2 days 
  • Skill-level: Foundation-level Machine Learning for the Web  skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for Python experienced developers, analysts or others who are intending to Explore the web and make smarter predictions using Python 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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 Python’s impressive Django framework and will find out how to build a modern simple web app with machine learning features. 

Working in a hands-on learning environment, led by our Machine Learning for the Web instructor, students will learn about and explore: 

  • Targets 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. 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Get familiar with the fundamental concepts and some of the jargons used in the machine learning community 
  • Use tools and techniques to mine data from websites 
  • Grasp the core concepts of Django framework 
  • Get to know the most useful clustering and classification techniques and implement them in Python 
  • Acquire all the necessary knowledge to build a web application with Django 
  • Successfully build and deploy a movie recommendation system application using the Django framework in Python 

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who wish to Explore the web and make smarter predictions using Python 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills. Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them. 
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Introduction to Practical Machine Learning Using Python 
  • Introduction to Practical Machine Learning Using Python 
  • General machine-learning concepts 
  • Preparing, manipulating and visualizing data – NumPy, pandas and matplotlib tutorials 
  • Scientific libraries  
  • When to use machine learning 
  1. Unsupervised Machine Learning 
  • Unsupervised Machine Learning 
  • Clustering algorithms 
  • Dimensionality reduction 
  • Singular value decomposition 
  1. Supervised Machine Learning 
  • Supervised Machine Learning 
  • Model error estimation 
  • Generalized linear models 
  • Naive Bayes 
  • Decision trees 
  • Support vector machine 
  • A comparison of methods 
  • Hidden Markov model 
  1. Web Mining Techniques 
  • Web Mining Techniques 
  • Web structure mining 
  • Web content mining 
  • Natural language processing 
  • Postprocessing information 
  1. Recommendation Systems 
  • Recommendation Systems 
  • 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 
  1. Getting Started with Django 
  • Getting Started with Django 
  • HTTP – the basics of the GET and POST methods 
  • Writing an app – most important features 
  • Admin 
  1. Movie Recommendation System Web Application 
  • Movie Recommendation System Web Application 
  • Application setup 
  • Models 
  • Commands 
  • User sign up login/logout implementation 
  • Information retrieval system (movies query) 
  • Rating system 
  • Recommendation systems 
  • Admin interface and API 
  1. Sentiment Analyser Application for Movie Reviews 
  • Sentiment Analyser Application for Movie Reviews 
  • 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 
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