AI Engineering Masterclass: From Zero to AI Hero

Packt via Coursera Specialization

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Overview

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This AI Engineering Masterclass takes you on a transformative journey to master artificial intelligence, starting with the basics of Python programming and building up to advanced machine learning concepts. You'll explore key data science tools, mathematical foundations, and essential skills for real-world AI projects. As you progress, you'll delve into machine learning algorithms, including ensemble learning, neural networks, deep learning, CNNs, and RNNs, gaining a solid understanding of the architectures driving modern AI applications. The course includes hands-on projects, applying theory to practical challenges. Designed for learners passionate about AI, this specialization offers both theoretical and practical insights. It starts with beginner-level Python programming and advances to complex AI topics. A basic math background is helpful but not required. By the end of the specialization, you'll be able to: Develop and implement AI and machine learning models. Apply data science techniques like cleaning, visualization, and analysis. Build and optimize neural networks and deep learning architectures. Master real-world AI tasks such as image classification, sentiment analysis, and transfer learning.

Syllabus

Course 1: Foundations of AI Engineering
Course 2: Core Machine Learning & Evaluation
Course 3: Deep Learning & Modern AI Architectures

Courses

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11 hours 25 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 course, you will build a strong foundation in machine learning and model evaluation techniques. You will begin by learning the core concepts of machine learning, including supervised learning, regression models, and classification techniques. The course will then guide you through more advanced topics like feature engineering, model evaluation methods, and hyperparameter tuning, which are essential for building high-performing machine learning models. By working through hands-on projects, you'll apply these concepts and tools in real-world scenarios. Throughout the course, you will explore key machine learning algorithms such as decision trees, random forests, boosting, and ensemble learning methods. You'll also learn how to evaluate and optimize models using techniques like cross-validation and hyperparameter tuning. These skills will enable you to refine your models and improve their accuracy, ensuring that they are ready for real-world applications. This course is suitable for anyone looking to deepen their understanding of machine learning, model evaluation, and optimization. While there are no strict prerequisites, a basic understanding of Python programming and machine learning concepts is recommended. The course is designed for intermediate learners, and the content will provide valuable skills for anyone looking to pursue a career in data science or machine learning engineering. By the end of the course, you will be able to implement and optimize machine learning models using various algorithms, perform feature engineering and selection, evaluate models using cross-validation, and apply advanced techniques such as boosting and ensemble methods.
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15 hours 19 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 will introduce you to the cutting-edge techniques and architectures in deep learning and AI. You will start by mastering the fundamentals of neural networks and deep learning, including key concepts like forward propagation, backpropagation, and gradient descent. From there, you will advance to Convolutional Neural Networks (CNNs) for image classification tasks and Recurrent Neural Networks (RNNs) for sequence modeling tasks such as time series prediction and text generation. As you progress, you will explore the revolutionary Transformer architecture, its self-attention mechanism, and its application in Natural Language Processing (NLP) tasks like text summarization and translation. This course will also cover transfer learning, allowing you to fine-tune pre-trained models for your own tasks, saving time and improving model accuracy. With hands-on projects using frameworks like TensorFlow, Keras, and PyTorch, you will apply your skills to real-world challenges. The course is designed for intermediate learners with prior knowledge of machine learning or neural networks. If you're a machine learning enthusiast or aspiring AI engineer looking to deepen your understanding of deep learning models and their real-world applications, this course will take your skills to the next level. By the end of the course, you will be able to design and implement advanced deep learning models, including CNNs, RNNs, and Transformers, and use transfer learning techniques to fine-tune models for specific tasks such as image classification, text generation, and more.
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12 hours 54 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 course, you will gain a comprehensive foundation in AI engineering, starting with the fundamentals of Python programming and advancing through key data science and machine learning concepts. The course emphasizes hands-on projects that will solidify your understanding of these essential skills, providing a deep dive into Python, data science tools, and mathematics necessary for machine learning. By mastering these core concepts, you'll be equipped to approach AI engineering challenges confidently. The course is structured to guide you through each key area, beginning with Python programming basics. You will learn how to work with Python syntax, data structures, functions, and file handling, all necessary for real-world applications. As you progress, you'll explore data science essentials using NumPy and Pandas, working on projects that teach you data manipulation, visualization, and analysis. The course culminates with a deeper dive into the mathematics required for machine learning, including linear algebra, calculus, and probability. This course is perfect for aspiring AI engineers, data scientists, and those interested in pursuing machine learning. No prior experience is required, though a basic understanding of programming and mathematics will be helpful. The course is designed for beginners but includes complex mathematical concepts for those ready to delve deeper. By the end of the course, you will be able to write Python code for AI-related applications, clean and manipulate data using Pandas, visualize data with Matplotlib, apply machine learning math concepts, and execute probability and statistics techniques in data analysis and model-building projects.