AI & Automation

Explore the fundamentals of artificial intelligence and machine learning - understand the landscape, key concepts, types of AI, and the role AI plays in modern industry.

Get up to speed with Python as the primary language for AI development - covering essential libraries like NumPy, Pandas, and Matplotlib for data manipulation and visualization.

Learn how to gather, clean, and prepare datasets for AI models. Understand feature engineering, handling missing data, normalization, and train/test splitting.

Dive into the most widely used class of machine learning algorithms - linear regression, logistic regression, decision trees, and support vector machines.

Explore clustering, dimensionality reduction, and pattern discovery techniques including K-Means, PCA, and anomaly detection.

Understand the building blocks of deep learning - neurons, activation functions, backpropagation, and how to build and train neural networks using TensorFlow/Keras.

Learn how machines understand and generate human language - covering tokenization, embeddings, sentiment analysis, and text classification.

Understand how LLMs work and learn to integrate the OpenAI ChatGPT API, build prompt engineering strategies, and create AI-powered applications.

Explore image recognition, object detection, and video analysis using Convolutional Neural Networks (CNNs) and pre-trained models.

Learn to automate business workflows using n8n - connect APIs, trigger events, and build no-code/low-code automation pipelines.

Automate repetitive tasks and integrate apps without code using Zapier and Make (formerly Integromat) - build Zaps and Scenarios for real business use cases.

Discover how to build AI agents that can plan, reason, and take actions autonomously - using frameworks like LangChain and AutoGen.

Learn how to evaluate model performance, track experiments, version models, and deploy ML pipelines using tools like MLflow and DVC.

Understand the ethical implications of AI - bias in models, fairness, transparency, privacy, and responsible AI development practices.

Learn how to package and deploy AI models as APIs and web applications using FastAPI, Docker, and cloud platforms like AWS and GCP.

Build a full end-to-end AI or automation project of your choice - from ideation to deployment - with mentorship and code review.