IBM-Machine-Learning-Professional-Certificate and ibm-machine-learning-certificate-projects

The projects are complements: the first project is a professional certificate course that teaches a wide array of machine learning topics, while the second project provides companion project work for the machine learning topics that the certificate covers, including Supervised Learning, Unsupervised Learning, Deep Learning, Reinforcement Learning, Time Series Analysis, and Survival Analysis.

Maintenance 0/25
Adoption 9/25
Maturity 8/25
Community 22/25
Maintenance 0/25
Adoption 4/25
Maturity 16/25
Community 16/25
Stars: 75
Forks: 50
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License:
Stars: 7
Forks: 7
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License: MIT
No License Stale 6m No Package No Dependents
Stale 6m No Package No Dependents

About IBM-Machine-Learning-Professional-Certificate

AI-MOO/IBM-Machine-Learning-Professional-Certificate

Machine Learning, Time Series & Survival Analysis. Develop working skills in the main areas of Machine Learning: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning. Also gain practice in specialized topics such as Time Series Analysis and Survival Analysis.

This program helps aspiring data professionals learn the core concepts and practical skills needed to apply machine learning in real-world scenarios. Through a series of courses, you'll gain expertise in various machine learning techniques, from analyzing data to building predictive models and understanding trends over time. This is for anyone looking to enter or advance their career in data science, analytics, or AI.

data-science-career machine-learning-training predictive-modeling time-series-forecasting data-analytics-skills

About ibm-machine-learning-certificate-projects

XandraV/ibm-machine-learning-certificate-projects

Projects for ML courses on Supervised Learning, Unsupervised Learning, Deep Learning, Reinforcement Learning and specialized topics such as Time Series Analysis and Survival Analysis .

This collection of projects helps aspiring data scientists and machine learning practitioners learn and apply various machine learning techniques. It provides practical examples for tasks like predicting economic impacts, classifying genetic variants, grouping companies by financial performance, and recognizing patterns in images or forecasting stock prices. The examples take in real-world datasets and demonstrate how to build models that produce actionable predictions or insights.

data science education predictive modeling financial forecasting genetics research image recognition

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