Language: English
Created by: Vivian Aranha
Rate: 5.0 / 2 ratings
Enroll: 2,090 students
What you’ll learn
- Aspiring AI professionals: Individuals who want to build a strong foundation in AI, machine learning, and deep learning.
- Data scientists and analysts: Professionals seeking to upskill and integrate AI into their work.
- Developers and software engineers: Those interested in building AI-driven applications.
- Students and researchers: People studying AI or working on AI projects who want practical, hands-on experience.
Requirements
- Access to a Development Environment
- Familiarity with Data Structures and Algorithms
- Basic Mathematics and Statistics
- Basic Programming Knowledge
Description
This AI Development Bootcamp is designed to guide learners through a series of 25 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.
Building a movie recommendation system using cosine similarity is another exciting project. Here, participants learn to create collaborative filtering systems, which are essential for personalizing user experiences in applications. By comparing user preferences and suggesting movies similar to what they have previously liked, participants gain insights into how recommendation engines function in popular platforms. Predicting house prices with linear regression then brings the focus back to supervised learning. Using historical data, learners build a model to predict house prices, introducing them to the basics of regression, data cleaning, and feature selection.
Weather forecasting using historical data takes learners through time-series prediction, an essential skill for handling sequential data. Participants will explore different modeling approaches to forecast weather trends. Following this, the bootcamp covers building a basic neural network from scratch. Here, participants write their own implementation of a neural network, learning about the intricacies of forward and backward propagation, weight updates, and optimization techniques. This project offers a hands-on approach to understanding neural networks at a granular level.
The course then progresses to stock price prediction using linear regression. This project teaches learners how to apply predictive modeling techniques to financial data, examining trends and patterns in stock prices. Predicting diabetes using logistic regression covers binary classification, where learners will predict the likelihood of diabetes in patients based on medical data. This project emphasizes the importance of healthcare data analytics and gives participants practical experience in building logistic regression models.
The dog vs. cat classifier project with a CNN introduces convolutional neural networks. This is a key project in image classification, as participants work on creating a model that differentiates between images of cats and dogs. With this project, learners gain a practical understanding of how CNNs work for image recognition tasks. Next, the Tic-Tac-Toe AI using the Minimax Algorithm introduces the concept of game theory and decision-making. The AI will learn to play optimally, providing participants with a foundation in developing game AI.
In credit card fraud detection using Scikit-learn, participants work on building a model that can identify fraudulent transactions, focusing on anomaly detection techniques. This project is highly applicable in financial services and demonstrates the importance of data-driven fraud detection systems. For Iris flower classification, learners utilize decision trees, one of the most interpretable machine learning algorithms. This project provides insight into how decision boundaries are formed and how simple classification algorithms operate.
Building a simple personal assistant using Python speech libraries allows learners to integrate speech recognition and text-to-speech features. This project enhances programming skills in creating voice-activated applications. The text summarizer using NLTK helps participants explore text summarization techniques, which are useful in applications that require condensing information from large documents or articles. In fake product review detection, participants delve into NLP for identifying deceptive reviews, building skills that are crucial in maintaining integrity on e-commerce platforms.
Detecting emotion in text using NLTK introduces emotion analysis, where participants will learn to classify text into categories such as happiness, sadness, anger, and more. This project is highly relevant for applications that require sentiment and emotion recognition. The book recommendation system using collaborative filtering is a practical extension of earlier recommendation techniques, allowing participants to explore more advanced methods for user personalization. Predicting car prices with Random Forest further reinforces regression and classification skills. Participants work on modeling car pricing, which is relevant in automotive industry applications.
The course also includes identifying fake news using Naive Bayes, a critical skill in today’s information landscape. Participants will learn techniques to detect misinformation, equipping them with skills to work on data integrity projects. In the resume scanner using keyword extraction, learners create a tool for analyzing resumes and identifying relevant skills based on job descriptions. This project provides insights into how text matching can be used in HR applications. Finally, the customer churn prediction project teaches participants how to model customer behavior and predict churn, which is crucial for customer retention strategies in many industries.
Throughout the course, each project builds on the concepts learned in previous projects, creating a comprehensive learning path. By working through these projects, participants will develop strong skills in data preprocessing, feature engineering, model training, evaluation, and deployment. They will also learn to work with different types of data, from text and images to time-series and tabular data. This bootcamp is structured to accommodate both beginners and those with some programming experience, providing a gradual learning curve that leads to increasingly complex applications.
With each project, learners not only build technical skills but also improve problem-solving abilities. The course emphasizes real-world applications, helping participants see how AI techniques are used in industries such as finance, healthcare, e-commerce, entertainment, and more. The hands-on approach encourages creativity and experimentation, allowing learners to adapt and improve their models based on project requirements. By the end of the course, participants will have completed a diverse portfolio of projects that demonstrate their proficiency in AI and machine learning, giving them the confidence to tackle AI challenges independently.
The bootcamp format is intensive but highly rewarding, designed to keep learners motivated and engaged. By dedicating a day to each project, participants immerse themselves in learning without overwhelming complexity, ensuring steady progress. The projects are structured to introduce core AI techniques incrementally, helping learners grasp each concept thoroughly before moving on to the next. This bootcamp is a unique opportunity to acquire industry-relevant skills in a short period, making it ideal for anyone interested in breaking into the field of AI or enhancing their technical abilities.
Who this course is for:
- Aspiring AI Professionals: Individuals who want to break into the field of artificial intelligence and machine learning. Whether you are a beginner or someone with a technical background looking to transition into AI, this course will provide a comprehensive and practical foundation.
- Data Scientists and Analysts: Professionals working with data who want to upskill and incorporate AI and machine learning techniques into their data analysis pipelines. This course will help data professionals apply AI techniques to enhance data-driven decision-making.
- Software Engineers and Developers: Developers with experience in software engineering who are looking to dive into AI-driven development, build machine learning models, and integrate AI solutions into their applications.
- Students and Researchers: University students, Ph.D. candidates, or academic researchers interested in AI for academic projects or advancing their careers in academia or industry research roles. This course will give them hands-on experience with AI tools and frameworks.
- Tech Enthusiasts and Self-learners: Enthusiasts and hobbyists who are passionate about AI and want to gain a structured and practical approach to understanding AI concepts, machine learning algorithms, and how to apply them in real-world scenarios.
