- Course: Algorithms of the Intelligent Web
- Duration: 2 days
- Skill-level: Foundation-level Algorithms of the Intelligent Web skills for Intermediate skilled team members. This is not a basic class.
- Targeted Audience: This course is geared for those who wants to know how to create machine learning applications that crunch and wrangle data collected from users, web applications, and website logs.
- 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.
- Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals.
Algorithms of the Intelligent Web teaches you how to create machine learning applications that crunch and wrangle data collected from users, web applications, and website logs. In this totally revised edition, you’ll look at intelligent algorithms that extract real value from data. Key machine learning concepts are explained with code examples in Pythons scikit-learn. This course guides you through algorithms to capture, store, and structure data streams coming from the web. You’ll explore recommendation engines and dive into classification via statistical algorithms, neural networks, and deep learning.
Working in a hands-on learning environment, led by our Algorithms of the Intelligent Web expert instructor, students will learn about and explore:
Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below
Audience & Pre-Requisites
This course is designed for developers interested in data science and for beginner data scientists
Pre-Requisites: Students should have familiar with
- Basics of Python
- Knowledge of Python is assumed.
Course Agenda / Topics
- Building applications for the intelligent web
An intelligent algorithm: Google Now
- The intelligent-algorithm lifecycle
- Further examples of intelligent algorithms
- Things that intelligent applications are not
- Classes of intelligent algorithm
- Evaluating the performance of intelligent algorithms
- Important notes about intelligent algorithms
- Extracting structure from data: clustering and transforming your data
- Data, structure, bias, and noise
- The curse of dimensionality
- The Gaussian mixture models
- The relationship between k-means and GMM
- Transforming the data axis
- Recommending relevant content
- Setting the scene: an online movie store
- Distance and similarity
- How do recommendation engines work?
- User-based collaborative filtering
- Model-based recommendation using singular value decomposition
- The Netflix Prize
- Evaluating your recommender
- Classification: placing things where they belong
- The need for classification
- An overview of classifiers
- Fraud detection with logistic regression
- Are your results credible?
- Classification with very large datasets
- Case study: click prediction for online advertising
- History and background
- The exchange
- What is a bidder?
- What is a decisioning engine?
- Click prediction with Vowpal Wabbit
- Complexities of building a decisioning engine
- The future of real-time prediction
- Deep learning and neural networks
- An intuitive approach to deep learning
- Neural networks
- The perceptron
- Multilayer perceptrons
- Going deeper: from multilayer neural networks to deep learning
- Making the right choice
- A/B testing
- Multi-armed bandits
- Bayesian bandits in the wild
- A/B vs. the Bayesian bandit
- Extensions to multi-armed bandits
- The future of the intelligent web
- Future applications of the intelligent web
- Social implications of the intelligent web