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Code, Train, Deploy: The AI Engineer's Path to Success - Panter - 25.12.2024
![[Bild: a57165d457a06b1259fa695a4a28c7c0.jpg]](https://i124.fastpic.org/big/2024/1221/c0/a57165d457a06b1259fa695a4a28c7c0.jpg)
Code, Train, Deploy: The AI Engineer's Path to Success
Published 12/2024
Created by Vivian Aranha
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: Beginner | Genre: eLearning | Language: English | Duration: 259 Lectures ( 51h 13m ) | Size: 21 GB
Everything you need to know about AI Engineering - Hands-on from Algorithms, Programming to Real Projects
What you'll learn
Master Python for AI: Write efficient Python code, essential for AI and ML programming tasks.
Data Preprocessing Skills: Prepare, clean, and transform data to enhance model performance.
Statistical Knowledge: Apply core statistics to understand data patterns and inform decisions.
Build Machine Learning Models: Develop and fine-tune ML models for classification, regression, and clustering.
Deep Learning Proficiency: Design and train neural networks, including CNNs and RNNs, for image and sequence tasks.
Utilize Transfer Learning: Adapt pre-trained models to new tasks, saving time and resources.
Deploy ML Models with APIs: Create scalable APIs to serve ML models in real-world applications.
Containerize with Docker: Package models for portable deployment across environments.
Monitor and Maintain Models: Track model performance, detect drift, and implement retraining pipelines.
Complete ML Lifecycle: Master end-to-end AI project skills, from data to deployment and ongoing maintenance.
Requirements
Foundational Math Skills: Understanding of algebra and basic calculus concepts (derivatives, functions) for ML.
Interest in AI and ML: A passion for learning AI, machine learning, and data-driven technologies.
Laptop/Computer: A device capable of running data processing and ML libraries like TensorFlow, PyTorch, and Docker.
Curiosity and Perseverance: Willingness to solve problems, experiment with data, and work through challenges.
Description
Welcome to the AI Mastery Bootcamp, a comprehensive, hands-on program designed to transform beginners into skilled AI engineers. Over the course of 16 weeks, you will learn how to build, train, and deploy machine learning models, step by step, using the latest tools and techniques. This bootcamp focuses on practical skills, empowering you to apply artificial intelligence to solve real-world problems and create innovative solutions.The course starts with the fundamentals, covering essential topics like Python programming, data preprocessing, and an introduction to machine learning. As you progress, you'll dive deeper into advanced concepts such as neural networks, deep learning, and natural language processing. You will also explore powerful AI frameworks like TensorFlow, PyTorch, and Hugging Face, which are essential for modern AI development.This bootcamp is ideal for anyone passionate about artificial intelligence, whether you're starting from scratch or looking to deepen your expertise. You don't need any prior experience with AI-just a willingness to learn and explore. By the end of the program, you'll have the skills and confidence to build AI solutions from the ground up, making you ready to take on industry challenges or pursue advanced AI research.Join us on this exciting journey and become a part of the future of technology!
Who this course is for
Aspiring AI Engineers: Those looking to build a career in AI and gain hands-on, production-ready skills.
Data Scientists and Analysts: Professionals who want to expand their expertise to include machine learning, deep learning, and model deployment.
Software Engineers: Developers interested in applying programming skills to AI and machine learning projects.
Career Changers: Individuals from non-technical backgrounds with foundational Python knowledge, eager to transition into AI.
Graduate Students: Students in data science, computer science, or related fields wanting a practical, job-ready experience in AI engineering.
Tech Entrepreneurs: Founders and CTOs interested in understanding AI for building AI-driven products or managing AI teams.
![[Bild: 544284328_code_train_deploy_the_ai_engin...uccess.jpg]](https://img100.pixhost.to/images/972/544284328_code_train_deploy_the_ai_engineer-s_path_to_success.jpg)
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