Machine Learning Courses and Certifications

Regression and Gradient Descent

Master regression algorithms and gradient descent by building them from scratch, covering linear and logistic regression without relying on high-level libraries.

• CodeSignal
• 3 hours
• Self-Paced
• Free Certificate

Career Certificate

Hands-on Approaches to Handling Data Imbalance

Master techniques for handling imbalanced datasets through data preparation, advanced resampling methods, specialized algorithms, and proper evaluation metrics to build robust ML models.

• CodeSignal
• 6 hours
• Self-Paced
• Free Certificate
• 3 courses

Center for Language & Speech Processing(CLSP), JHU

An Introduction to the MALLET Machine Learning Toolkit

Discover MALLET, a powerful Java-based machine learning toolkit for natural language processing, text classification, and topic modeling applications.

• YouTube
• 1 hour 10 minutes
• Self-Paced
• Free Video

Machine Learning Tutorial Python - Machine Learning for Beginners

Comprehensive tutorial covering machine learning basics, algorithms, and projects using Python libraries. Includes deep learning, neural networks, and advanced topics for beginners to intermediate learners.

• YouTube
• 1 day 1 hour
• Self-Paced
• Free Video

Machine Learning

Comprehensive Python-based machine learning roadmap covering data analysis, visualization, algorithms, and deployment, with hands-on tutorials and real-world projects.

• YouTube
• 1 day 10 hours
• Self-Paced
• Free Video

Machine Learning with Python Tutorials

Explore AI, machine learning, and deep learning concepts, including linear regression models and practical applications like temperature estimation and virus prediction.

• YouTube
• 1 hour 30 minutes
• Self-Paced
• Free Video

Deploy Machine Learning Models as Web App Using Flask & Docker on Azure Cloud

Learn to deploy machine learning models as web applications using Flask, Docker, and Azure Cloud, covering linear regression, Flask app development, and Docker containerization.

• YouTube
• 3 hours 30 minutes
• Self-Paced
• Free Video

Machine Learning 1 - 2020

Comprehensive introduction to machine learning fundamentals, covering probability theory, regression, classification, neural networks, clustering, and advanced topics like kernels and Gaussian processes.

• YouTube
• 21 hours
• Self-Paced
• Free Video

Machine Learning

Explore machine learning models through hands-on tutorials in Python and Julia, covering clustering, regression, neural networks, and more. Practical examples with code provided.

• YouTube
• 3 hours 30 minutes
• Self-Paced
• Free Video

The AI University

Machine Learning Using Spark MLLib

Learn to implement machine learning algorithms using Spark MLLib, covering linear regression techniques, data preprocessing, model building, and evaluation methods in a hands-on, practical approach.

• YouTube
• 2 hours 30 minutes
• Self-Paced
• Free Video

Augmented Startups

Machine Learning Fun and Easy

Explore popular machine learning concepts through engaging lectures covering regression, clustering, neural networks, and more. Gain fundamental knowledge in an accessible, enjoyable format.

• YouTube
• 2 hours 30 minutes
• Self-Paced
• Free Video

Serrano.Academy

General Machine Learning

Explore key machine learning concepts, algorithms, and evaluation methods in this comprehensive introduction to the field.

• YouTube
• 3 hours 30 minutes
• Self-Paced
• Free Video

Serrano.Academy

Machine Learning

Comprehensive introduction to key machine learning concepts, algorithms, and applications, covering testing, error metrics, recommendation systems, and various classification methods.

• YouTube
• 3 hours 30 minutes
• Self-Paced
• Free Video

機器學習基石下 (Machine Learning Foundations)---Algorithmic Foundations

331 ratings at Coursera

探討機器學習的演算法基礎,包括線性迴歸、邏輯迴歸、分類模型、非線性轉換、過擬合、正則化、驗證等主題,著重實用工具和方法論。

• Coursera
• 9 hours 20 minutes
• Self-Paced
• Paid Course

DeepLearning.AI;  Stanford

Advanced Learning Algorithms

8679 ratings at Coursera

Explore neural networks, decision trees, and best practices in machine learning. Build and train models using TensorFlow, apply multi-class classification, and learn ensemble methods for real-world AI applications.

• Coursera
• 1 day 10 hours 18 minutes
• Self-Paced
• Paid Course