Скачать или смотреть Get Certified Professional Machine Learning Engineer - Day 1

"Get Certified Professional Machine Learning Engineer - Day 1," led by Philip Obiorah, organized by GDG Cloud Port Harcourt, is the first session of a study group focused on preparing for the Google Cloud Professional Machine Learning Engineer certification exam.

The session covers foundational concepts in Machine Learning and Artificial Intelligence, the role of an ML Engineer, and the next steps for joining the study group.

Here is a summary of the key topics discussed:

The Professional Machine Learning Engineer Role [01:10]: An overview of the responsibilities, including building, evaluating, and optimizing production-level AI solutions, handling large datasets, building pipelines, and training, deploying, and monitoring models.

Exam Overview [02:15]: The exam assesses proficiency in building prototypes, scaling ML models, automating pipelines, and monitoring AI solutions; it does not directly test coding skills. The session also touches on the $200 exam fee and potential avenues for waivers.

AI and Machine Learning Fundamentals [04:47]:

Artificial Intelligence (AI) [05:58]: Defined as the broad field of machines performing tasks ordinarily ascribed to humans (e.g., computer vision, self-driving).

Machine Learning (ML) [07:05]: Defined as a subset of AI where machines are taught to learn through data and experience.

Types of Machine Learning [08:08]:

Supervised Learning [08:57]: Uses labeled data where the correct answers are known, similar to a teacher showing a formula solution. This includes:

Classification [13:42]: Predicting categories (e.g., cat or dog).

Regression [13:50]: Predicting numeric values (e.g., house prices or future sales).

Unsupervised Learning [09:29]: Uses unlabeled data where the model finds patterns without guidance. Techniques include:

Clustering [18:07]: Grouping similar items (e.g., customer segmentation).

Association Rule Mining [19:00]: Identifying relationships between items (e.g., the correlation between beer and diapers sales).

Dimensionality Reduction [22:09]: Simplifying data by reducing the number of features.

Neural Networks [24:35]: An introduction to neural networks (the foundation of deep learning) and their basic structure: the input layer, hidden layer, and output layer, and the role of weights in determining the importance of inputs for prediction. [28:19]

Next Steps for the Study Group [36:14]: Instructions for participants to join the Google Cloud Innovators program to get free monthly credits and to start the Professional Machine Learning Engineer learning path on Google Skills Boost.