30 Projects in 30 days of AI Development Bootcamp

This AI Development Bootcamp is designed to guide learners through a series of 30 practical projects, each aiming to build foundational skills and a solid understanding of various AI concepts and machine learning techniques. The course begins with simple and approachable projects, gradually moving into more complex applications. By the end, participants will have an impressive portfolio of projects that span across diverse areas such as natural language processing, image classification, recommendation systems, predictive modeling, and more. Each project offers a hands-on learning experience and focuses on a particular machine learning concept, algorithm, or tool.

The journey begins with creating a basic calculator using Python. This project introduces participants to coding logic and familiarizes them with Python syntax. Although simple, this project is essential as it lays the groundwork for understanding how to design basic applications in Python. From here, learners move to a more complex task with an image classifier using Keras and TensorFlow. This project involves working with neural networks, enabling learners to build a model that can distinguish between different classes of images. Participants will gain experience with training and validating a neural network, understanding key concepts such as activation functions, convolutional layers, and data preprocessing.

A simple chatbot using predefined responses comes next, giving learners a taste of natural language processing. This project provides an introduction to building conversational agents, where the chatbot responds to user queries based on predefined rules. While it’s basic, it forms the foundation for more advanced NLP projects later on in the course. Moving on to the spam email detector using Scikit-learn, learners tackle text classification using machine learning. This project demonstrates how to process text data, extract relevant features, and classify messages as spam or not spam. Participants will work with techniques like TF-IDF vectorization and Naive Bayes, key tools in the NLP toolkit.

Human activity recognition using a smartphone dataset and Random Forest introduces the concept of supervised learning with time-series data. Here, participants will use accelerometer and gyroscope data to classify various physical activities. This project showcases the versatility of machine learning in handling complex, real-world data. Following this, sentiment analysis using NLTK allows learners to dive deeper into NLP by determining the sentiment behind text data. This project involves cleaning and tokenizing text, as well as using pre-built sentiment lexicons to analyze emotional undertones in social media posts, reviews, or comments.