Few courses will help you in utilizing your weekend. If you have interests in machine learning these courses will be of more importance to you and you will be more advanced with the knowledge of machine learning with these courses.
Machine LearningProvided by Columbia University (Columbia X)
You will learn:
- Supervised learning techniques for regression and classification
- Unsupervised learning techniques for data modeling and analysis
- Probabilistic versus nonprobabilistic viewpoints
- Optimization and inference algorithms for model learning
Machine Learning is the basis for the most exciting careers in data analysis today. You will learn the models and methods and apply them to real-world situations ranging from identifying trending news topics to building recommendation engines, ranking sports teams and plotting the path of movie zombies.
Methods include linear and logistic regression, support vector machines, tree classifiers, boosting, maximum likelihood and MAP inference, EM algorithm, hidden Markov models, Kalman filters, kmeans, Gaussian mixture models, among others.
Principles of Machine Learning: R Edition Provided by Microsoft
What You Will Learn
- After completing this course, you will be familiar with the following concepts and techniques:
- Data exploration, preparation, and cleaning
- Supervised machine learning techniques
- Unsupervised machine learning techniques
- Model performance improvement
This course is part of the Microsoft Professional Program Certificate in Data Science.
Machine learning uses computers to run predictive models that learn from existing data in order to forecast future behaviors, outcomes, and trends.
In this data science course, you will be given clear explanations of machine learning theory combined with practical scenarios and hands-on experience building, validating, and deploying machine learning models. You will learn how to build and derive insights from these models using R, and Azure Notebooks.
Quantum Machine Learning University Of Toronto
Quantum computers are becoming available, which begs the question: what are we going to use them for? Machine learning is a good candidate. In this course, we will introduce several quantum machine learning algorithms and implement them in Python.
What you'll learn
- By the end of this course, students will be able to:
- Distinguish between quantum computing paradigms relevant for machine learning
- Assess expectations for quantum devices on various time scales
- Identify opportunities in machine learning for using quantum resources
- Implement learning algorithms on quantum computers in Python
Machine Learning Fundamentals Provided by The University of California, San Diego (UCSanDiegoX)
What You Will Learn
- Classification, regression, and conditional probability estimation
- Generative and discriminative models
- Linear models and extensions to nonlinearity using kernel methods
- Ensemble methods: boosting, bagging, random forests
- Representation learning: clustering, dimensionality reduction, autoencoders, deep nets
Robotics: Vision Intelligence and Machine Learning University of Pennsylvania
Learn how to design robot vision systems that avoid collisions, safely work with humans and understand their environment.
How do robots see, respond to and learn from their interactions with the world around them? This is the fascinating field of visual intelligence and machine learning. Visual intelligence allows a robot to sense and recognize the surrounding environment. It also enables a robot to learn from the memory of past experiences by extracting patterns in visual signals.
You will understand how Machine Learning extracts statistically meaningful patterns in data that support classification, regression and clustering. Then by studying Computer Vision and Machine Learning together, you will be able to build recognition algorithms that can learn from data and adapt to new environments.