AI Engineer Associate Certificate Course

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

Requirements
- Basic knowledge of Python programming, including functions, loops, and data structures
- Prior exposure to introductory data science or completion of a beginner-level AI course
- Familiarity with basic mathematics 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 are provided)
- Curiosity and motivation to explore intermediate-to-advanced AI concepts
- Willingness to engage in hands-on coding and practice-based learning

Description
Take your AI skills to the next level with theAI Engineer Associate Certificate Course—a hands-on, intermediate-level program designed to help you buildreal-world expertise in machine learning, deep learning, andAI agent development. Whether you're an aspiringAI engineer, adata science practitioner, or a developer seeking to upskill, this course gives you a solid foundation inadvanced AI techniquesand the most in-demand tools likeTensorFlowandPyTorch.

We begin withFeature Engineering and Model Evaluation, where you’ll learn how toprepare data for machine learning, extract meaningful features, and evaluate model performance using metrics likeprecision, recall, F1 score, andROC-AUC. These skills are essential for building accurate, reliable, and production-ready ML models.

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

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

InML 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 thetheory and practicebehind popular ML models while reinforcing your Python programming and mathematical reasoning.

We then exploreMachine 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 constructKeras models, handletensor operations, and work withcustom training loops—essential for building scalable AI solutions.

Next up isLearning PyTorch, where you’ll experience how to use this flexible and powerful deep learning framework to implement everything fromlogistic regressiontoconvolutional neural networks (CNNs). You'll understandautograd,optimizers, and how to train models in a modular, research-friendly environment.

Finally, we introduceAI Agents for Dummies, a beginner-friendly but powerful section onautonomous agentsandagent-based architectures. You’ll understand the role ofAI agents in decision-making,planning, andtask automation, with examples from modern applications likechatbots,recommender systems, andmulti-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 bothTensorFlowandPyTorchconfidently

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 aMachine Learning Engineer,AI Developer, or simply want to deepen your understanding ofartificial intelligence, this course provides everything you need to succeed.

Join thousands of learners and earn yourAI Engineer Associate Certificatetoday—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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