AI Engineer Associate Certificate Course | Udemy
English | Size: 9.03 GB
Genre: eLearning[/center]

Master Machine Learning, Deep Learning & AI Agent Foundations with TensorFlow and PyTorch

What you'll learn
Perform advanced feature engineering for machine learning models
Evaluate model performance using precision, recall, F1, and AUC
Apply decision trees, random forests, and gradient boosting algorithms
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

Take your AI skills to the next level with the AI Engineer Associate Certificate Course-a hands-on, intermediate-level program designed to help you build real-world expertise in machine learning, deep learning, and AI agent development. Whether you're an aspiring AI engineer, a data science practitioner, or a developer seeking to upskill, this course gives you a solid foundation in advanced AI techniques and the most in-demand tools like TensorFlow and PyTorch.

We begin with Feature Engineering and Model Evaluation, where you'll learn how to prepare data for machine learning, extract meaningful features, and evaluate model performance using metrics like precision, recall, F1 score, and ROC-AUC. These skills are essential for building accurate, reliable, and production-ready ML models.

Next, we'll cover Advanced Machine Learning Algorithms, where you'll explore real-world implementations of decision trees, random forests, gradient boosting, XGBoost, and ensemble learning. You'll understand when and how to apply each algorithm for different data types and problem spaces.

Then we dive into Neural Networks and Deep Learning Fundamentals, giving you a clear understanding of perceptrons, activation functions, backpropagation, and network architectures. This section lays the groundwork for building your own deep learning models from scratch.

In ML Algorithms and Implementations, you'll get hands-on experience coding a variety of algorithms from the ground up. You'll sharpen your understanding of both the theory and practice behind popular ML models while reinforcing your Python programming and mathematical reasoning.

We then explore Machine Learning with TensorFlow, where you'll build, train, and evaluate models using one of the most widely adopted deep learning frameworks in the industry. You'll learn how to construct Keras models, handle tensor operations, and work with custom training loops-essential for building scalable AI solutions.

Next up is Learning PyTorch, where you'll experience how to use this flexible and powerful deep learning framework to implement everything from logistic regression to convolutional neural networks (CNNs). You'll understand autograd, optimizers, and how to train models in a modular, research-friendly environment.

Finally, we introduce AI Agents for Dummies, a beginner-friendly but powerful section on autonomous agents and agent-based architectures. You'll understand the role of AI agents in decision-making, planning, and task automation, with examples from modern applications like chatbots, recommender systems, and multi-agent coordination.

By the end of the course, you'll be able to:

Build and deploy advanced ML models

Understand the math and code behind neural networks

Use both TensorFlow and PyTorch confidently

Work with AI agent concepts and practical applications

Prepare for more specialized AI roles or certifications

Whether you're aiming to land a job as a Machine Learning Engineer, AI Developer, or simply want to deepen your understanding of artificial intelligence, this course provides everything you need to succeed.

Join thousands of learners and earn your AI Engineer Associate Certificate today-your next step toward becoming a full-stack AI engineer!

Who this course is for:
Aspiring AI Engineers ready to move beyond foundational concepts
Software developers looking to transition into AI or enhance their machine learning capabilities
Data analysts or junior data scientists aiming to specialize in AI-driven solutions
Students in computer science or related fields who want hands-on experience with real-world ML frameworks
Tech professionals seeking to build a portfolio of intermediate AI projects
Product managers and tech leads who need a deeper understanding of how AI models are trained, evaluated, and deployed

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