AI Engineering - Data Science and Machine Learning Bootcamp

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Bootcamp

In this phase, you will deepen your understanding of data and learn how to analyze and prepare it effectively. You’ll work with SQL to extract data from various sources and use Pandas to process and transform it efficiently. This phase introduces you to the foundations of Exploratory Data Analysis (EDA), enabling you to identify patterns, relationships, and trends within datasets.
You’ll also apply different visualization techniques to present your findings clearly and effectively. This sets the foundation for everything that follows in Machine Learning and AI Engineering.

Machine learning sounds complex? We make it easy to understand! In this phase, you’ll dive into the fundamentals of supervised learning and discover how machines learn from data.
You’ll work hands-on with regression and classification models – two of the most important techniques for making predictions and categorizing data. You’ll also gain insights into how these models work and when to apply each method effectively.

Additionally, you’ll learn about key model evaluation concepts: precision, accuracy, F1-score, and other essential metrics that help you measure the quality of your predictions and make informed decisions.

To wrap up this phase, you’ll apply your knowledge in a dedicated machine learning project, experiencing firsthand how theory turns into practice.

In this phase, you’ll deepen your knowledge of deep learning and explore how artificial neural networks are structured and operate. You’ll gain a solid understanding of modern deep learning architectures and implement your own models.

Additionally, you’ll work with time series analysis to interpret data over extended periods and make accurate forecasts. You’ll also dive into Natural Language Processing (NLP) for text data processing and learn how to build recommender systems that deliver personalized suggestions.

By the end of this phase, you’ll not only understand the key concepts of deep learning but also be ready to apply them in real-world, hands-on projects.

In this phase, you’ll learn how to bring your machine learning models into production-ready applications and scale the entire development process. You’ll work with APIs to make your models accessible and integrate them into external systems.

You’ll also explore Docker, enabling you to build reproducible environments and ensure your projects run smoothly across different platforms. Another key focus is on cloud deployments, where you’ll learn to efficiently deploy your solutions in the cloud.

Finally, you’ll dive into MLOps, covering best practices for maintaining, monitoring, and continuously improving your machine learning models. By the end of this phase, you’ll be fully equipped to develop, deploy, and manage AI solutions at a professional level.

In the final phase of your bootcamp, you will apply all your knowledge in a capstone project. You will develop your own machine learning solution, prepare it for end-to-end deployment, and ensure the long-term stability and reliability of your models through effective model monitoring.

From data preparation to project work, you will work on real-world challenges that closely mirror industry demands. This hands-on experience equips you perfectly for your future role as an AI Engineer.