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Book Overview & Buying
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Table Of Contents
AI Engineer Associate Course
By :
AI Engineer Associate Course
By:
Overview of this book
In this course, you'll start with feature engineering, learning to preprocess and scale data, and create relevant features for machine learning. You’ll then explore model evaluation techniques, including cross-validation and hyperparameter tuning. This foundation leads to advanced algorithms like Random Forests and Gradient Boosting, with real-world projects for hands-on experience.
As you progress, dive into deep learning with TensorFlow and PyTorch, covering both traditional neural networks and advanced architectures like CNNs and RNNs. Through practical projects, such as image classification and time-series prediction, you’ll gain a strong understanding of deep learning. The course also introduces AI agents, with frameworks like AutoGPT, IBM Bee, and LangGraph, empowering you to build stateful and collaborative agents.
The course concludes with projects applying your knowledge to real-world problems such as customer churn prediction, sentiment analysis, and image recognition. Additionally, you'll explore the future of AI agents, their ethical implications, and their impact on industries like healthcare, finance, and robotics.
Table of Contents (9 chapters)
Introduction to Course and Instructor
Feature Engineering and Model Evaluation
Advanced Machine Learning Algorithms
Neural Networks and Deep Learning Fundamentals
Machine Learning Algorithms and Implementations
Introduction to Machine Learning and TensorFlow
Introduction to Learning PyTorch
AI Agents for Beginners
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