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Book Overview & Buying
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Table Of Contents
AI Engineering Masterclass: From Zero to AI Hero
By :
AI Engineering Masterclass: From Zero to AI Hero
By:
Overview of this book
This course offers a comprehensive journey from Python programming basics to advanced deep learning techniques. It starts by covering fundamental Python skills, including data structures, functions, and file handling, ensuring a solid foundation for beginners. As the course progresses, you'll dive into data science with tools like NumPy, Pandas, and Matplotlib, applying them to real-world data through projects like Exploratory Data Analysis (EDA).
The mathematical concepts crucial for machine learning, such as linear algebra, calculus, and probability, are explored in-depth to provide a strong theoretical base. You'll learn to build machine learning models, from supervised learning techniques like regression and classification to advanced algorithms like ensemble methods and gradient boosting.
The final weeks introduce deep learning with neural networks, CNNs, RNNs, and transformers. By the end of the course, you will be able to develop and optimize machine learning and deep learning models, applying cutting-edge AI technologies to real-world tasks like image recognition, text generation, and sentiment analysis. This hands-on approach ensures you gain practical experience to excel in AI roles.
Table of Contents (13 chapters)
Week 1: Python Programming Basics
Week 2: Data Science Essentials
Week 3: Mathematics for Machine Learning
Week 4: Probability and Statistics for Machine Learning
Week 5: Introduction to Machine Learning
Week 6: Feature Engineering and Model Evaluation
Week 7: Advanced Machine Learning Algorithms
Week 8: Model Tuning and Optimization
Week 9: Neural Networks and Deep Learning Fundamentals
Week 10: Convolutional Neural Networks (CNNs)
Week 11: Recurrent Neural Networks (RNNs) and Sequence Modeling
Week 12: Transformers and Attention Mechanisms
Week 13: Transfer Learning and Fine-Tuning
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