IBM AI Engineering Professional Certificate Course Syllabus

Overview: This professional certificate program is designed for intermediate learners and offers a comprehensive journey through AI engineering, covering machine learning, deep learning, natural language processing, computer vision, and generative AI. The curriculum spans approximately 200 hours of content, delivered through self-paced modules with hands-on labs and real-world projects, preparing learners for roles in AI and machine learning engineering.

Module 1: Machine Learning with Python

Estimated time: 20 hours

• Foundational machine learning concepts
• Supervised and unsupervised learning algorithms
• Implementation using Scikit-learn
• Model evaluation and optimization

Module 2: Deep Learning with Keras and TensorFlow

Estimated time: 32 hours

• Introduction to neural networks and Keras
• Building deep learning models with Keras
• Advanced model development using TensorFlow
• Training and fine-tuning neural networks

Module 3: Neural Networks and Deep Learning with PyTorch

Estimated time: 37 hours

• Introduction to neural networks using PyTorch
• Implementing and training models in PyTorch
• Real-world deep learning applications
• Model deployment and performance tuning

Module 4: Scalable Machine Learning with Apache Spark

Estimated time: 20 hours

• Big data processing fundamentals
• Scaling machine learning pipelines
• Implementing ML algorithms on Apache Spark

Module 5: Natural Language Processing and Sequence Modeling

Estimated time: 55 hours

• Text classification and vector space models
• Sequence models and attention mechanisms
• Applications in NLP tasks
• Large language model (LLM) development

Module 6: Generative AI and Final Project

Estimated time: 40 hours

• Introduction to generative AI and applications
• Prompt engineering techniques
• Building generative AI-powered applications with Python
• AI Capstone Project with Deep Learning

Prerequisites

• Proficiency in Python programming
• Basic understanding of data analysis
• Familiarity with mathematical concepts in machine learning

What You'll Be Able to Do After

• Implement machine learning models using Scikit-learn and SciPy
• Build and train deep learning models using Keras, TensorFlow, and PyTorch
• Deploy scalable ML pipelines on Apache Spark
• Develop applications using generative AI and Retrieval-Augmented Generation (RAG)
• Demonstrate AI engineering proficiency through a capstone project