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 TensorFlow, open source libraries used by leading tech companies in the machine learning field (e.g., Google, NVIDIA, CocaCola, eBay, Snapchat, Uber and many more).
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Introduction to Machine Learning
Found in
Machine Learning,
Intro to Machine Learning
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- Provider Coursera
- Cost Free Online Course (Audit)
- Session In progress
- Language English
- Certificate Paid Certificate Available
- Effort 2-5 hours a week
- Start Date
- Duration 5 weeks long
- Learn more about MOOCs
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Syllabus
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.
Introduction to 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.
Introduction to Reinforcement Learning
-This week will cover Reinforcement Learning, a fundamental concept in machine learning that is concerned with taking suitable actions to maximize rewards in a particular situation. After learning the initial steps of Reinforcement Learning, we'll move to Q Learning, as well as Deep Q Learning. We'll discuss the difference between the concepts of Exploration and Exploitation and why they are important.
-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.
Introduction to 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.
Introduction to Reinforcement Learning
-This week will cover Reinforcement Learning, a fundamental concept in machine learning that is concerned with taking suitable actions to maximize rewards in a particular situation. After learning the initial steps of Reinforcement Learning, we'll move to Q Learning, as well as Deep Q Learning. We'll discuss the difference between the concepts of Exploration and Exploitation and why they are important.
Taught by
Lawrence Carin
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Anonymous
7 months ago
completed this course.
The theory is ok and videos are well created.
The practice is also fine, but not working properly. The page sends you to a Duke U site that is not working right now :-P). But even if it worked, since is a site outside of coursera, the activity never gets finished. So good luck for those who payed the course.
Also, the forums are abandoned, there really should be someone from Duke U answering trivial questions, such as "why is the page for the exercises not working?".
So, as for right now (march 11th 2019) you are stuck with the theory, and even when the practice worked, it was not clear how to tell coursera that the activity was finished. I am really really glad of not paying for the course right away!!!
The practice is also fine, but not working properly. The page sends you to a Duke U site that is not working right now :-P). But even if it worked, since is a site outside of coursera, the activity never gets finished. So good luck for those who payed the course.
Also, the forums are abandoned, there really should be someone from Duke U answering trivial questions, such as "why is the page for the exercises not working?".
So, as for right now (march 11th 2019) you are stuck with the theory, and even when the practice worked, it was not clear how to tell coursera that the activity was finished. I am really really glad of not paying for the course right away!!!
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