Develop machine learning and deep learning models to become a AI Engineer using Data Science, LLM, ai agents, agentic ai
β±οΈ Length: 8.1 total hours
β 4.34/5 rating
π₯ 26,448 students
π September 2025 update
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- Course Overview
- Embark on a transformative journey to master the art and science of Artificial Intelligence engineering, equipping you with the cutting-edge knowledge and practical skills demanded by the rapidly evolving tech landscape.
- This comprehensive program is meticulously designed to elevate your expertise from foundational AI concepts to the sophisticated application of machine learning and deep learning architectures.
- You will delve into the practical implementation of AI solutions, focusing on building intelligent systems that can perceive, reason, learn, and act autonomously.
- Explore the profound impact of Large Language Models (LLMs) and the emergent field of AI agents, understanding how to design, train, and deploy sophisticated agentic AI systems capable of complex task execution.
- The curriculum emphasizes a hands-on approach, fostering a deep understanding of the entire AI project lifecycle, from conceptualization to scalable deployment and continuous improvement.
- Gain insights into the strategic thinking required to identify impactful AI applications and translate business needs into robust technical solutions.
- This course is your pathway to becoming a highly sought-after AI Engineer, capable of driving innovation and shaping the future of technology.
- Requirements / Prerequisites
- A foundational understanding of programming principles, ideally with proficiency in Python, is highly recommended.
- Familiarity with basic mathematical concepts, including linear algebra, calculus, and probability, will enhance your learning experience.
- Exposure to data structures and algorithms will be beneficial for grasping the efficiency and scalability of AI solutions.
- While prior AI or machine learning coursework is not mandatory, a genuine curiosity and a willingness to engage with complex technical concepts are essential.
- Access to a personal computer with sufficient processing power and internet connectivity is required for practical exercises and simulations.
- Skills Covered / Tools Used
- Machine Learning Fundamentals: Supervised, unsupervised, and reinforcement learning paradigms.
- Deep Learning Architectures: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, and Generative Adversarial Networks (GANs).
- Large Language Model (LLM) Engineering: Prompt engineering, fine-tuning, model selection, and application development with LLMs.
- AI Agent Design and Implementation: Creating autonomous agents, understanding agent architectures, and deploying multi-agent systems.
- Data Science Essentials: Exploratory Data Analysis (EDA), data visualization, and statistical modeling.
- Programming Languages & Libraries: In-depth use of Python, alongside key libraries such as TensorFlow, PyTorch, Scikit-learn, Pandas, and NumPy.
- Cloud Computing Platforms: Introduction to deploying and managing AI models on platforms like AWS, Azure, or GCP.
- Model Evaluation and Optimization: Techniques for assessing model performance, hyperparameter tuning, and ensuring robustness.
- Deployment Strategies: Containerization (Docker), API development (Flask/FastAPI), and MLOps principles.
- Version Control: Effective use of Git and GitHub for collaborative development and project management.
- Benefits / Outcomes
- Develop a robust portfolio of AI projects, showcasing your practical skills to potential employers.
- Gain the confidence and technical acumen to tackle complex AI challenges in real-world scenarios.
- Become proficient in designing, developing, and deploying sophisticated AI models, including LLMs and AI agents.
- Acquire the skills to critically analyze data, select appropriate algorithms, and engineer impactful features.
- Understand the ethical considerations and best practices in AI development and deployment.
- Position yourself for high-demand roles in the rapidly expanding field of Artificial Intelligence.
- Enhance your problem-solving abilities through a structured and analytical approach to AI engineering.
- Develop the capacity for continuous learning and adaptation in the dynamic AI landscape.
- Contribute to groundbreaking innovations across various industries by leveraging the power of AI.
- PROS
- Comprehensive coverage of both foundational AI and advanced topics like LLMs and AI agents.
- Strong emphasis on practical application and hands-on project building.
- Up-to-date curriculum reflecting current industry trends and September 2025 relevance.
- High student enrollment and rating suggest proven effectiveness and quality.
- CONS
- The extensive nature of AI may require significant dedication and time investment beyond the listed course hours for true mastery.
Learning Tracks: English,Development,Data Science
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