Build AI Solutions Without Coding Expertise

In this week, you’ll dive into classification techniques like Decision Trees and Random Forests to make accurate predictions from categorical data. You'll also learn to evaluate model performance using the Confusion Matrix, Information Gain, and the Bias-Variance trade-off.

• Decision tree
  
• Bagging
  
• Random forests
  

In this week, you will learn to develop powerful tools that personalize user experiences, a key asset in today's data-driven landscape. You’ll explore Recommendation Systems through clustering, collaborative filtering, rank-based, and content-based methods to design impactful, user-centric solutions.

• Recommendation systems: problem statements and solutions
  
• Clustering-based recommendation systems
  
• Collaborative filtering
  
• Rank-based and content-based techniques

In this week, you’ll embark on an exploration of deep learning, mastering core concepts, neural network building blocks, and training techniques. You'll apply your knowledge to a practical case study in Digit Recognition, mastering a classic deep learning application.

• Introduction to deep learning
  
• Building blocks of neural networks
  
• Training neural networks
  
• Digit recognition case study
  

In this week, you'll dive into the fascinating world of machines that can see and interpret visual data. You’ll explore CNN building blocks, training techniques, and practical applications like image detection and object recognition.

• Drawbacks of artificial neural networks (ANNs)
  
• Building blocks of convolutional neural networks (CNNs)
  
• Training convolutional neural networks
  
• Image detection

In this week, you’ll learn the foundational concepts of Generative AI, beginning with an exploration of its origins and the underlying principles behind generating new data. You’ll also explore matrix estimation perspectives, large language models as probabilistic sequence predictors, and key prompt engineering techniques.

• Origins of generating new data
  
• Generative AI as a matrix estimation problem
  
• Large language models as probabilistic models for sequence completion
  
• Prompt engineering

In this week, you’ll dive into the business applications of Generative AI, including Retrieval-Augmented Generation (RAG) for improving response relevance and Agentic AI for autonomous decision-making.

In this week, you’ll explore the ethical and responsible development of AI systems, from the AI lifecycle to addressing bias, causality, and privacy concerns. You’ll also examine how AI components interconnect and influence each other across various domains and complex systems.

• Introduction to the AI lifecycle
  
• Introduction to bias and its examples
  
• Introduction to causality and privacy
  
• Interconnections and domains
  
• Interdependency and feedback in AI systems

In these self-paced modules, you'll learn to navigate and extract insights from unstructured data, a crucial competency in today's data-rich environment. You will also explore the specialized techniques needed to analyze time-dependent data, a vital skill for predicting trends and making strategic decisions.

• Data Exploration: Unstructured Data (text processing, sentiment analysis, text classification) 
• Data Exploration: Temporal Data (time series methods, ARIMA modeling)