This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction. In addition, we have designed practice exercises that will give you hands-on experience implementing these data science models on data sets. These practice exercises will teach you how to implement machine learning algorithms with PyTorch, open source libraries used by leading tech companies in the machine learning field (e.g., Google, NVIDIA, CocaCola, eBay, Snapchat, Uber and many more).
Offert par
Introduction to Machine Learning
Université DukeÀ propos de ce cours
Résultats de carrière des étudiants
11%
Résultats de carrière des étudiants
11%
Offert par
Université Duke
Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.
Programme de cours : ce que vous apprendrez dans ce cours
Simple Introduction to Machine Learning
The focus of this module is to introduce the concepts of machine learning with as little mathematics as possible. We will introduce basic concepts in machine learning, including logistic regression, a simple but widely employed machine learning (ML) method. Also covered is multilayered perceptron (MLP), a fundamental neural network. The concept of deep learning is discussed, and also related to simpler models.
Basics of Model Learning
In this module we will be discussing the mathematical basis of learning deep networks. We’ll first work through how we define the issue of learning deep networks as a minimization problem of a mathematical function. After defining our mathematical goal, we will introduce validation methods to estimate real-world performance of the learned deep networks. We will then discuss how gradient descent, a classical technique in optimization, can be used to achieve this mathematical goal. Finally, we will discuss both why and how stochastic gradient descent is used in practice to learn deep networks.
Image Analysis with Convolutional Neural Networks
This week will cover model training, as well as transfer learning and fine-tuning. In addition to learning the fundamentals of a CNN and how it is applied, careful discussion is provided on the intuition of the CNN, with the goal of providing a conceptual understanding.
Recurrent Neural Networks for Natural Language Processing
This week will cover the application of neural networks to natural language processing (NLP), from simple neural models to the more complex. The fundamental concept of word embeddings is discussed, as well as how such methods are employed within model learning and usage for several NLP applications. A wide range of neural NLP models are also discussed, including recurrent neural networks, and specifically long short-term memory (LSTM) models.
Avis
- 5 stars74,68 %
- 4 stars20,65 %
- 3 stars2,78 %
- 2 stars0,63 %
- 1 star1,24 %
Meilleurs avis pour INTRODUCTION TO MACHINE LEARNING
Thank you so much to all the instructors there, for teaching the actual Machine Learning 😃😃 I have learned lot of things from this course and it will surely help in my future,😃🤝
This course is very rich in terms of content. The instructors are extremely good. I have learnt a lot. I would recommend this course to others who want to delve into machine learning.
very helpful course and all teachers are very expert and their teaching method is also simple but very helpful. I'm happy to take this course.
Thanks.....
Shivam Tyagi
A very concise and yet beautifully constructed course for introduction to machine learning for absolute beginner having basic knowledge of probability and mathematics.
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