Become an AI Associate Engineer with Free Course

Elevate your AI skills with the Associate Engineer in AI Certification Course: a hands-on intermediate program designed to help you develop real-world expertise in machine learning, deep learning, and AI agent development. Whether you’re an aspiring AI engineer, a data science professional, or a developer looking to enhance your skills, this course provides a solid foundation in advanced AI techniques and in-demand tools like TensorFlow and PyTorch.

We start with Feature Engineering and Model Evaluation, where you’ll learn to prepare data for machine learning, extract meaningful features, and evaluate model performance using metrics like accuracy, recall, F1 score, and ROC-AUC. These skills are essential for building accurate, reliable models ready for production. Next, we delve into Advanced Machine Learning Algorithms, exploring real-world implementations of decision trees, random forests, gradient boosting, XGBoost, and ensemble learning. You’ll understand when and how to apply each algorithm based on the data type and the problem to be solved.

Moving forward, we immerse ourselves in Neural Networks and Deep Learning Fundamentals, providing you with a clear understanding of perceptrons, activation functions, backpropagation, and network architectures. This section lays the groundwork for you to build your own deep learning models from scratch. You will gain practical experience programming various algorithms while solidifying your theoretical and practical understanding of the most popular models, strengthening your Python skills and mathematical reasoning. By the end of the course, you will be able to construct and deploy advanced ML models, understand the mathematics and code behind neural networks, confidently use TensorFlow and PyTorch, and work with real-world AI agent concepts and applications.

What you will learn:

• Perform advanced feature engineering for machine learning models
• Evaluate model performance using accuracy, recall, F1, and AUC
• Apply algorithms like decision trees, random forests, and gradient boosting
• Understand deep learning concepts like activation and backpropagation
• Build neural networks from scratch using Python
• Train and deploy models using TensorFlow and Keras
• Use PyTorch to build, optimize, and evaluate deep learning models
• Understand the fundamentals of AI agents and their real-world applications

Course Content:

• Sections: 9
• Lectures: 143
• Duration: 21h 0m

Requirements:

• Basic knowledge of programming in Python, including functions, loops, and data structures
• Completion of an introductory data science course or a beginner AI course
• Familiarity with basic math concepts such as algebra, functions, and vectors
• Some understanding of probability and statistics is helpful, but not mandatory
• A computer (Windows, macOS, or Linux) with stable internet access
• Ability to install and work with tools like Jupyter Notebook, TensorFlow, and PyTorch (installation instructions provided)
• Curiosity and motivation to explore intermediate to advanced AI concepts
• Willingness to engage in practical coding and project-based learning

Who is it for?

• Aspiring AI engineers ready to go beyond the basics
• Software developers looking to transition into AI or enhance their machine learning skills
• Junior data analysts or data scientists seeking specialization in AI-driven solutions
• Computer science students wanting practical experience with real-world machine learning frameworks
• Tech professionals looking to build an intermediate-level AI project portfolio
• Product managers and technical leads needing deeper understanding of how AI models are trained, evaluated, and deployed

What are you waiting for to get started?

Enroll today and take your skills to the next level. Coupons are limited and may expire at any time!

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