Machine Learning in a Nutshell

Join us for an engaging and transformative learning experience as we delve into the world of Artificial Intelligence, Machine Learning, and Predictive Models. This bootcamp is not just a series of lectures; it's an enlightening journey towards mastering cutting-edge AI techniques and applications alongside an expert in the industry.

In our comprehensive 6-hour bootcamp, spread over three sessions, you'll embark on an interactive exploration of key concepts and applications in the realm of AI and Machine Learning. Here's what you can expect to learn:

• Exploring the Basics of Artificial Intelligence and Machine Learning: Gain a solid understanding of fundamental AI concepts and the principles of machine learning, setting the foundation for more advanced topics.
• Building Predictive Models: Learn about the different types of predictive models and their applications across various domains, along with hands-on experience in constructing these models.
• Technical and Ethical Implications of AI: Discuss the technical challenges and ethical considerations involved in the use of AI, ensuring a well-rounded perspective on its implementation.
• Developing Machine Learning Models Using Python: Understand the complete pipeline for building a machine learning model, from data preprocessing to model evaluation, using Python and its powerful libraries.

Our bootcamp is tailored to enrich your understanding with deep insights and real-world applications from various sectors. Embark on this educational odyssey with us to enhance and refine your AI skills.

Requirements:

• General knowledge of programming in Python
• Have Python 3 installed with the following packages:
• Matplotlib 3.7.2+
• Numpy 1.24.3+
• Pandas 2.0.3+
• Scikit-learn 1.3.0+
• Seaborn 0.12.2+
• Recommended to use the latest distribution of Anaconda
• Practical examples will be developed in the Spyder IDE

Speaker's Bio:

Daniel is a Research Professor at Tecnológico Nacional de México/Instituto Tecnológico de Tijuana, working in the Engineering Sciences Graduate program and the Industrial Engineering Department. His research focuses on advancing Artificial Intelligence and Data Science, developing new algorithms, and exploring new applications. With expertise in computer vision, machine learning, evolutionary computation, data mining, and data analytics, Daniel aims to develop algorithms that imbue machines with human-like reasoning capabilities, particularly for predictive, detection, classification, and recommendation tasks. Daniel holds a PhD in Computer.

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