Applied AI: Algorithms of the Intelligent Web / Building Intelligent Web Applications

Learning Objectives

This course combines engaging instructor-led presentations and useful demonstrations with valuable hands-on labs and engaging group activities. Equipped with these skills, you'll be set to infuse AI magic into your web creations, making them more intuitive, dynamic, and user-friendly.

Working in a hands-on learning environment led by our engaging AI expert you’ll learn to:

• Decipher Smart Algorithms: Grasp the core concepts behind intelligent web design, enabling you to weave smart algorithms seamlessly into your web applications for enhanced user experiences.
• Navigate Data with Confidence: Delve deep into data transformation and grouping, helping you identify and interpret patterns, and ensuring your AI solutions are grounded in solid data science.
• Craft Personalized Content Recommendations: Develop a keen understanding of content suggestion systems, empowering you to curate web content that resonates with individual users.
• Master the Art of Sorting: Get hands-on with various item sorting techniques, ensuring that every piece of data finds its rightful place, and enhancing the efficiency and accuracy of your AI web applications.
• Predict with Precision: Uncover the secrets of forecasting online advertisement clicks. By the end of the course, you'll be equipped to anticipate user behaviors, optimizing ad placements and driving engagement.
• Harness Advanced Neural Networks: Take a deep dive into the interconnected world of neural systems. By understanding layers, perceptrons, and error correction methods, you'll be ready to craft sophisticated AI-driven solutions for your web projects.

Audience

This Intermediate level course is geared for experienced technical professionals eager to meld the capabilities of AI with the dynamism of web applications. Roles might include experienced web developers, data analysts, machine learning engineers, UX Designers and digital product managers. If you're passionate about enhancing digital experiences, tailoring user interactions, or predicting online behaviors, this immersive journey into the intelligent web realm is tailor-made for you.

Pre-Requisites

To ensure a smooth learning experience and maximize the benefits of attending this course, you should have the following prerequisite skills:

• Basic Python Programming experience.
• Basic web development experience, working with HTML / CSS, etc.  
• Comfort with elementary data concepts, such as databases, data structures, and basic data manipulation.

Course Agenda

1. Intelligent Web Design and Implementation

• A Glimpse of Smart Algorithms: An Insight into Google Now
• The Evolution and Phases of Smart Algorithms
• Additional Illustrations of Smart Algorithms
• Limitations and Misconceptions of Intelligent Apps
• Different Varieties and Types of Smart Algorithms
• Methods to Measure Intelligent Algorithm Effectiveness
• Crucial Considerations Regarding Smart Algorithms

2. Unraveling Patterns in Data: Transformation and Grouping

• Understanding the Core Elements, Skewness, and Unsystematic Fluctuations in Data
• Navigating through High-Dimensional Data Spaces
• Methodology of Grouping Using K-means
• Linking k-means Techniques with Gaussian Mixtures
• Modifying and Refining the Data Dimensions

3. Tailored Content Suggestions and Recommendations

• Scene Setting: Digital Film Shopping Platform
• Comparative Metrics and Similarity in Content
• Inside the Mechanics of Content Suggestion Systems
• User-Centric Filtering Approaches
• Mathematical Decomposition in Content Suggestions
• The Race to Win the Netflix Challenge
• Evaluative Metrics for Recommendation Systems

4. Item Sorting: Guiding Entities to Their Proper Locations

• Recognizing the Importance of Sorting
• A Brief Look at Various Sorting Techniques
• Algorithmic Procedures
• Unmasking Fraud Through Logistic Models
• How Trustworthy are the Outcomes?
• Dealing with Extensive Sorting Datasets

5. A Detailed Examination: Online Advertisement Click Forecasts

• Tracing the Roots and Framework
• The Mechanism of Trade
• Explaining the Role of a Bidding Participant
• Deciphering Decision-Making Engines
• Predicting Clicks Using Specific Algorithms
• Challenges in Assembling a Decision-Making Mechanism
• The Upcoming Era of Instantaneous Forecasts

6. Exploring Neural Connectivity and Advanced Learning Techniques

• A Layman's Guide to Sophisticated Learning
• Architecture of Interconnected Neural Systems
• Understanding the Simplest Neural Unit: Perceptron
• Layers of Connected Perceptrons
• Learning through Error Correction Methods
• Beyond Traditional Networks: A Journey into Advanced Learning

7. Decision Making: Selecting the Optimal Path

• Comparative Analysis through A/B Testing
• Exploring the Complexities of Multi-Option Scenarios
• Real-Life Uses of Probabilistic Selection Methods
• A/B Tests vs. Probabilistic Selection: A Contrast
• Expanding on Multi-Option Selection Strategies

8. The Road Ahead for Smart Web Technology

• The Role of ChatGPT and Other Comprehensive Language Processors