Course description
Algorithms of the Intelligent Web | Building Intelligent Web Applications
Algorithms of the Intelligent Web is a hands-on Applied Machine Learning & AI course that teaches you how to create machine learning applications that crunch and wrangle data collected from users, web applications and website logs. Leveraging the most current standards, skills and practices, you’ll examine intelligent algorithms that extract real value from data. Key machine learning concepts are explained with code examples in Python’s 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.
Learning Objectives
Our engaging instructors and mentors are highly-experienced practitioners who bring years of current, modern "on-the-job" modern applied datascience, AI and machine learning experience into every classroom and hands-on project. Working in a hands-on lab environment led by our expert instructor, attendees will explore
- Machine learning essentials, as well as deep learning and neural networks
- How recommendation engines work
- Building applications for the intelligent web
- Extracting structure from data: clustering and transforming your data
- Recommending relevant content
- Classification: placing things where they belong
- Relevant Case Study: click prediction for online advertising
- Making the right Machine Learning choices for your web apps
- The future of the intelligent web
Do you work at this company and want to update this page?
Is there out-of-date information about your company or courses published here? Fill out this form to get in touch with us.
Training content
1. Building applications for the intelligent web
- An intelligent algorithm in action: 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
2. Extracting structure from data: clustering and transforming your data
- Data, structure, bias, and noise
- The curse of dimensionality
- K-means
- The relationship between k-means and GMM
- Transforming the data axis
3. 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
4. Classification: placing things where they belong
- The need for classification
- An overview of classifiers
- algorithms
- Fraud detection with logistic regression
- Are your results credible?
- Classification with very large datasets
5. 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
6. Deep learning and neural networks
- An intuitive approach to deep learning
- Neural networks
- The perceptron
- Multilayer perceptrons
- backpropagation
- Going deeper: from multilayer neural networks to deep learning
7. 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
8. The future of the intelligent web
- Future applications of the intelligent web
- Social implications of the intelligent web
Course delivery details
Student Materials: Each student will receive a Student Guide with course notes, code samples, software tutorials, diagrams and related reference materials and links (as applicable). Our courses also include step by step hands-on lab instructions and and solutions, clearly illustrated for users to complete hands-on work in class, and to revisit to review or refresh skills at any time. Students will also receive the project files, datasets, code and solutions (as applicable) required for the hands-on work.
Classroom Setup Made Simple: Our dedicated tech team will work with you to ensure your classroom and lab environment is setup, tested and ready to go well in advance of the course delivery date, ensuring a smooth start to class and seamless hands-on experience for your students. We offer several flexible student machine setup options including guided manual set up for simple installation directly on student machines, or cloud based / remote hosted lab solutions where students can log in to a complete separate lab environment minus any installations, or we can supply complete turn-key, pre-loaded equipment to bring ready-to-go student machines to your facility. Please inquire for details.
Costs
- Price: $2,395.00
- Discounted Price: $1,556.75
Why choose Trivera Technologies LLC?
Over 25 years of technology training expertise.
Robust portfolio of over 1,000 leading edge technology courses.
Guaranteed to run courses and flexible learning options.
Contact this provider
Trivera Technologies
Trivera Technologies is a IT education services & courseware firm that offers a range of wide professional technical education services including: end to end IT training development and delivery, skills-based mentoring programs,new hire training and re-skilling services, courseware licensing and...