AIML02 - A.I. and Machine Learning Applications: Connecting Concepts to Practice
1024522
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Program Description
This professional certificate program is designed for aspiring data scientists and machine learning enthusiasts. Learn fundamental concepts and apply AI/ML to real-world datasets across various fields.
Who Should Attend
- Aspiring data scientists and machine learning enthusiasts who want to learn how to use Python to manipulate data and solve problems using AI.
- Individuals looking to see how basic statistics, machine learning, and deep learning concepts are applied in real-world scenarios.
Learning Objectives
- Understand AI Basics: Distinguish between AI and non-AI applications
- Python Proficiency: Learn the basics of reading and using Python code
- Accelerated Libraries: Know when and how to use libraries like NumPy and Pandas for efficient data manipulation
- Data Cleaning: Identify and modify NumPy and Pandas code to clean data effectively
- Computational Modeling: Grasp the concepts of computational modeling and experimental design
- Data Visualization: Utilize libraries like Matplotlib and Seaborn to create various types of data visualizations
- Machine Learning Fundamentals: Understand the differences between classification and regression in machine learning
- Data Splitting: Learn how to split datasets into training and testing sets
- Bias and Variance: Comprehend the tradeoffs between bias and variance in machine learning models
- Classical Algorithms: Apply classical machine learning algorithms such as k-Nearest Neighbors, k-Means, and artificial neural networks
- Clustering Techniques: Identify and use clustering algorithms effectively
- Training vs. Inference: Understand the distinction between training and inference phases in machine learning
- Advanced Neural Networks: Leverage advanced deep neural networks, like CNNs, to solve computer vision problems
- Large Language Models: Understand the basics of large language models (LLMs) and their functionality
- Prompt Engineering: Use prompt engineering to generate answers using large language models
- Retrieval Augmented Generation: Learn how retrieval augmented generation systems work and their limitations
- AI Research: Effectively find, read, and understand AI research concepts from academic papers
- Practical Application: Integrate learned concepts to solve a self-selected problem
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