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Take machine learning to the next level with Udacity

Overview
Provider

Udacity

Dates

Open on-going enrollment

Duration

3 months

Location

Online

Price_Front_Page

Free

Price

Free

Type

Online course

Language

English

Requirements

This is an intermediate to advanced level course. Prior to taking this course, and in addition to the prerequisites and requirements outlined for the Machine Learning Engineer Nanodegree program, you should possess the following experience and skills:

- Minimum 2 years of programming experience (preferably in Python)
- Git and GitHub experience (assignment code is in a GitHub repo)
- Basic machine learning knowledge (especially supervised learning)
- Basic statistics knowledge (mean, variance, standard deviation, etc.)
- Linear algebra (vectors, matrices, etc.)
- Calculus (differentiation, integration, partial derivatives, etc.)

Image Credits

Proftrack

Deep learning methods are becoming exponentially more important due to their demonstrated success at tackling complex learning problems. At the same time, increasing access to high-performance computing resources and state-of-the-art open-source libraries are making it more and more feasible for enterprises, small firms, and individuals to use these methods.

Mastering deep learning accordingly positions you at the very forefront of one of the most promising, innovative, and influential emergent technologies, and opens up tremendous new career opportunities. For Data Analysts, Data Scientists, Machine Learning Engineers, and students in a Machine Learning/Artificial Intelligence curriculum, this represents a rarefied opportunity to enhance your Machine Learning portfolio with an advanced, yet broadly applicable, collection of vital techniques.

Lesson 1: From Machine Learning to Deep Learning

– Understand the historical context and motivation for Deep Learning.
– Set up a basic supervised classification task and train a black box classifier on it.
– Train a logistic classifier “by hand ”Optimize a logistic classifier using gradient descent, SGD, Momentum and AdaGrad.

Lesson 2: Deep Neural Networks

– Train a simple deep network.
– Effectively regularize a simple deep network.
– Train a competitive deep network via model exploration and hyperparameter tuning.

Lesson 3: Convolutional Neural Networks

– Train a simple convolutional neural net.
– Explore the design space for convolutional nets.

Lesson 4: Deep Models for Text and Sequences

– Train a text embedding model.
– Train a LSTM model.

If you have followed this course, please share your review below.

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