IBM AI Engineer Full Syllabus

Duration: Approximately 6 months (self-paced)
Level: Intermediate
Ideal For: Aspiring AI Engineers, Data Scientists, Machine Learning Engineers, and Software Developers
Prerequisites: Basic knowledge of Python programming and foundational understanding of machine learning concepts

Course 1: Machine Learning with Python

• Overview: Introduction to machine learning concepts, including supervised and unsupervised learning.
• Key Topics:
  • Regression and classification algorithms
  • Clustering techniques
  • Model evaluation and validation
• Hands-On: Implement machine learning models using Scikit-learn and evaluate their performance.Reddit+13E-Student+13Coursera+13

Course 2: Introduction to Deep Learning & Neural Networks with Keras

• Overview: Foundational concepts of deep learning and neural networks.
• Key Topics:
  • Structure and function of neural networks
  • Activation functions and backpropagation
  • Building models using Keras
• Hands-On: Develop and train neural networks for various tasks using the Keras library.E-Student+1Firebrand Training+1

Course 3: Introduction to Computer Vision and Image Processing

• Overview: Understanding how computers interpret and process visual information.
• Key Topics:
  • Image processing techniques
  • Feature extraction and object detection
  • Applications of computer vision in real-world scenarios
• Hands-On: Apply image processing techniques to analyze and interpret images.

Course 4: Deep Neural Networks with PyTorch

• Overview: Dive deeper into deep learning using the PyTorch framework.
• Key Topics:
  • Building and training deep neural networks
  • Implementing convolutional and recurrent neural networks
  • Handling overfitting and improving model performance
• Hands-On: Create and train complex neural network architectures using PyTorch.CourseraMedium

Course 5: Building Deep Learning Models with TensorFlow

• Overview: Utilize TensorFlow to construct and deploy deep learning models.
• Key Topics:
  • TensorFlow operations and computational graphs
  • Model optimization and tuning
  • Deploying models in production environments
• Hands-On: Develop and deploy deep learning models using TensorFlow.

Course 6: AI Capstone Project with Deep Learning

• Overview: Apply the knowledge and skills acquired throughout the program to a comprehensive project.
• Project Focus:
  • Define a real-world problem and develop an AI solution
  • Collect and preprocess data
  • Build, train, and evaluate a deep learning model
  • Present findings and insights derived from the project

Tools and Technologies Covered

• Programming Languages: Python
• Libraries and Frameworks: Scikit-learn, Keras, PyTorch, TensorFlow
• Concepts: Machine Learning, Deep Learning, Neural Networks, Computer VisionE-Student

Skills You Will Gain

• Proficiency in machine learning algorithms and their applications
• Ability to build and train deep learning models using various frameworks
• Expertise in computer vision techniques and image processing
• Experience in deploying AI models to solve real-world problemsE-Student+1Coursera+1

Certification and Career Prospects

Upon successful completion of the program, you will receive the IBM AI Engineering Professional Certificate, which can be showcased on your LinkedIn profile and resume. This certification can open doors to roles such as:

• AI Engineer
• Machine Learning Engineer
• Data Scientist