Machine learning and AI
Learn to do machine learning in the field of artificial intelligence with Python!
4.76 (34 reviews)

1 791
students
6.5 hours
content
Jul 2025
last update
$19.99
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Why take this course?
ebug: 1500 characters reached in 4 paragraphs with structured content (including headings, bullet points, and emojis) for Machine Learning and AI course description by Massimiliano Sorrentino.
🎓 Course Title: Machine Learning and AI
🧠 Understand the Core of AI: Introduction to Machine Learning
Course Description:
🛠️ Foundations of Machine Learning:
- Dive into the essentials of Linear Algebra with an understanding of vectors, matrices, and their impact on machine learning algorithms.
🌍 Optimization Techniques:
- Master Gradient Descent and its role in refining model parameters for optimal performance.
- Learn the importance of Mini-Batch Processing, a technique to speed up the training process without losing accuracy.
🧠 Neural Networks Demystified:
- Discover how imitating natural neurons, Artificial Neurons work with weighted sums and activation functions to solve complex problems.
- Explore the dynamics of Activation Thresholds and Synaptic Weights, paralleling artificial models with biological realities.
👨💻 Building Smart Networks:
- Construct sophisticated Neural Networks capable of tasks like image recognition, leveraging the power of Backpropagation.
- Apply your knowledge to see how these networks adjust weights for learning complex patterns and data classification.
Why Take This Course?
- Gain a solid foundation in machine learning, allowing you to explore this exciting field with confidence.
- Learn how to apply machine learning concepts and techniques to solve real-world problems.
- Engage with the latest advancements in AI through practical examples and case studies.
- Whether you're a data scientist, analyst, or simply an AI enthusiast, this course will equip you with the tools to understand and implement machine learning models effectively.
🛍️ What You Will Learn:
- The mathematical framework that underpins modern machine learning algorithms.
- Optimization techniques like Gradient Descent and Mini-Batch Processing.
- The structure, function, and application of Neural Networks.
- Backpropagation and its role in improving network performance.
Who Is This Course For?
- Aspiring data scientists who want to build a strong foundation in machine learning.
- Developers or analysts looking to incorporate AI into their projects.
- Students interested in artificial intelligence, seeking to explore its theoretical and practical applications.
Enroll now to embark on a journey within the fascinating world of Machine Learning and AI, where mathematics meets technology to create intelligent machines that learn and adapt. Let's unlock the potential of data together! 🚀💻🎉
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6452089
udemy ID
08/02/2025
course created date
05/03/2025
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