AI Engineer Associate

Packt via Coursera Specialization

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Overview

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This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. The AI Engineer Associate Specialization will equip you with essential skills for a career in AI engineering. You will start by mastering feature engineering, model evaluation, and advanced machine learning algorithms. The specialization dives deep into ensemble learning, boosting, XGBoost, and techniques for optimizing models like cross-validation and hyperparameter tuning. Next, you’ll explore deep learning fundamentals, including neural networks, forward propagation, and loss functions. You'll also gain hands-on experience with deep learning frameworks such as TensorFlow and PyTorch, with applications like image classification and natural language processing. The specialization concludes by introducing AI agents and their practical applications in industries like healthcare, robotics, and business. This specialization is ideal for learners with a basic understanding of programming and machine learning. Intermediate in difficulty, it requires Python knowledge and an interest in data science. By the end of the specialization, you will be able to implement machine learning algorithms, build neural networks, and develop AI agents for real-world use cases.

Syllabus

Course 1: Machine Learning Foundations
Course 2: Deep Learning and Advanced Techniques
Course 3: AI Engineering and Deployment

Courses

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8 hours 58 minutes
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This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this comprehensive course, you will explore the entire AI development lifecycle, from building machine learning models to deploying them in real-world environments. Starting with an introduction to TensorFlow, you’ll learn how to set up your development environment, create machine learning models, and understand the inner workings of neural networks. You’ll dive deep into Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), and learn how to leverage pre-trained models for transfer learning to improve model performance. As you progress, the course introduces you to cutting-edge topics like AI agents, where you will explore their role in industries ranging from healthcare to entertainment. You will learn how to build AI agents using frameworks such as AutoGPT, IBM Bee, and LangGraph. Moreover, you will gain practical skills in deploying AI models with TensorFlow Serving, TensorFlow Lite for mobile applications, and scale models using Kubernetes. The course also touches upon important ethical and legal considerations in AI development, making it a well-rounded introduction to real-world AI deployment. This course is ideal for learners with a basic understanding of machine learning and programming who want to take their skills to the next level. By the end of the course, you will be well-equipped to design, develop, deploy, and optimize AI models, as well as build autonomous AI agents for various applications. By the end of the course, you will be able to build and deploy complex AI models using TensorFlow, design AI agents with state-of-the-art frameworks, and address real-world challenges like scaling, ethical concerns, and regulatory issues in AI development.
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9 hours 23 minutes
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This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This course offers a deep dive into advanced deep learning concepts and techniques, focusing on both theory and hands-on implementation. Starting with ensemble learning, you will learn techniques like bagging, boosting, and gradient boosting, helping you improve model performance for real-world applications. The course also covers powerful tools like XGBoost, LightGBM, and CatBoost, allowing you to build efficient and accurate models using these state-of-the-art frameworks. You will then venture into neural networks, covering the fundamentals of deep learning, forward propagation, activation functions, loss functions, and backpropagation. You'll also explore optimization techniques such as gradient descent, all while building neural networks using popular frameworks like TensorFlow, Keras, and PyTorch. As the course progresses, you will apply these skills to practical projects, such as image classification with CIFAR-10, and learn how to fine-tune models with transfer learning and handle complex data types like images and sequences. Designed for learners with a basic understanding of machine learning and programming, this course is ideal for those looking to master advanced deep learning techniques. Whether you're an aspiring AI engineer or a data scientist looking to enhance your skills, this course will prepare you for tackling complex real-world deep learning tasks. Familiarity with Python and machine learning fundamentals is recommended, but not required. By the end of the course, you will be able to implement advanced machine learning algorithms, build neural networks using TensorFlow and PyTorch, apply transfer learning techniques, and deploy models into production environments.
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7 hours 22 minutes
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This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this comprehensive course, you will dive into the world of machine learning, exploring key concepts, algorithms, and implementation techniques. You'll start by mastering feature engineering, a crucial aspect of building effective machine learning models. By focusing on data scaling, normalization, encoding categorical variables, and feature selection, you’ll enhance your ability to preprocess and transform data for optimal model performance. The journey continues as you explore the core machine learning algorithms. You'll implement these techniques using Python, including linear regression, logistic regression, decision trees, random forests, and gradient boosting. The course will also cover unsupervised learning techniques, such as K-means clustering, DBSCAN, and Gaussian mixture models, helping you tackle complex data analysis problems. Additionally, advanced methods like reinforcement learning and neural networks will be introduced, preparing you for cutting-edge machine learning applications. This course is designed for learners who have a basic understanding of programming and data science principles. It is ideal for those looking to build a solid foundation in machine learning, whether you're aiming to enhance your skills or transition into the field. No prior experience with machine learning is necessary, but a familiarity with Python is helpful. The course is suitable for intermediate learners looking to strengthen their understanding of machine learning algorithms and techniques. By the end of the course, you will be able to implement various machine learning algorithms in Python, from regression and classification to clustering and reinforcement learning, with a deep understanding of how to evaluate and optimize model performance.