What will you learn in this IBM AI Engineering Professional Certificate Course
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Understand the fundamentals of machine learning, deep learning, and neural networks.
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Implement supervised and unsupervised machine learning models using Python libraries such as SciPy and Scikit-learn.
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Build deep learning models and neural networks using Keras, PyTorch, and TensorFlow.
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Deploy machine learning algorithms and pipelines on Apache Spark for big data processing.
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Develop and fine-tune large language models (LLMs) using frameworks like Hugging Face and LangChain.
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Create generative AI applications incorporating Retrieval-Augmented Generation (RAG) techniques.
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Gain hands-on experience through labs and projects to showcase your skills to employers.
Program Overview
Course 1: Machine Learning with Python
⏳ 20 hours
- Learn foundational machine learning concepts and implement algorithms using Python and Scikit-learn.
Course 2: Introduction to Deep Learning & Neural Networks with Keras
⏳ 9 hours
- Explore deep learning fundamentals and build neural networks using the Keras library.
Course 3: Deep Learning with Keras and TensorFlow
⏳ 23 hours
- Develop advanced deep learning models using Keras integrated with TensorFlow.
Course 4: Introduction to Neural Networks and PyTorch
⏳ 17 hours
- Implement and train neural networks using PyTorch for various applications.
Course 5: Deep Learning with PyTorch
⏳ 20 hours
- Build and deploy deep learning models using PyTorch, focusing on real-world scenarios.
Course 6: Scalable Machine Learning on Big Data using Apache Spark
⏳ 20 hours
- Learn to scale machine learning tasks on big data sets using Apache Spark.
Course 7: Introduction to Computer Vision and Image Processing
⏳ 15 hours
- Understand computer vision concepts and apply image processing techniques.
Course 8: Natural Language Processing with Classification and Vector Spaces
⏳ 20 hours
- Explore NLP techniques, including text classification and vector space models.
Course 9: Sequence Models and Attention Mechanisms
⏳ 20 hours
- Delve into sequence models and attention mechanisms for advanced NLP tasks.
Course 10: Generative AI: Introduction and Applications
⏳ 7 hours
- Learn about generative AI models and their real-world applications.
Course 11: Generative AI: Prompt Engineering Basics
⏳ 7 hours
- Master prompt engineering techniques to optimize generative AI outputs.
Course 12: Building Generative AI-Powered Applications with Python
⏳ 13 hours
- Develop applications powered by generative AI models using Python.
Course 13: AI Capstone Project with Deep Learning
⏳ 20 hours
- Apply your acquired skills to a comprehensive project, demonstrating your proficiency in AI engineering.
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Job Outlook
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Completing this certificate prepares you for roles such as AI Engineer, Machine Learning Engineer, Data Scientist, or Deep Learning Specialist.
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The skills acquired are applicable across various industries that utilize AI technologies.
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Enhance your employability by gaining practical experience in building and deploying AI models and applications
Explore More Learning Paths
Advance your AI engineering expertise with these curated programs designed to enhance your skills in generative AI, LLMs, and prompt engineering for practical applications.
Related Courses
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Generative AI Prompt Engineering Basics Course – Learn the fundamentals of prompt engineering to effectively communicate with AI models and optimize outputs.
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IBM Generative AI Engineering Professional Certificate Course – Gain in-depth knowledge of generative AI engineering, including model deployment, fine-tuning, and practical applications.
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Generative AI Engineering with LLMs Specialization Course – Master large language models and learn to integrate them into AI-powered solutions across industries.
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