Deep Learning and NLP with Python 2-in-1

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 ChLanguage: English | Duration: 3h 27m | Size: 1.3 GB

Unleash the power of deep learning and NLP to build real-world applications

What you'll learn

Learn popular algorithms in NLP and deep learning

Transform text tokens into numerical vectors by vectorizing

Compute the gradients of all output tensors

Create a machine learning architecture from scratch

Apply convolutional neural networks for image analysis

Discover the methods of image classification and harness object recognition using deep learning

Get to know recurrent neural networks for the textual sennt analysis model

Requirements

Familiarity with Python will be beneficial for this course.

Basic understanding of data science is assumed.

Description

Deep learning is a popular subset of machine learning that allows you to build complex models that are faster and give more accurate predictions. Natural Language Processing (NLP) offers powerful ways to interpret and act on spoken and written language. It’s used to help deal with customer support enquiries, analyse how customers feel about a product, and provide intuitive user interfaces.

This comprehensive 2-in-1 course teaches you to write applications using two popular data science concepts, deep learning and NLP. You’ll learn through practical demonstrations, clear explanations, and interesting real-world examples. It will give you a versatile range of deep learning and NLP skills, which you will put to work in your own applications.

This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Getting Started with NLP and Deep Learning with Python, starts off with an introduction to Natural Language Processing (NLP) and recommendation systems which enables you to run multiple algorithms simultaneously. You will then learn the concepts of deep learning and TensorFlow. You will also learn how to create machine learning architecture.

The second course, Deep Learning with Python, takes you from basic calculus knowledge to understanding backpropagation and its application for training in neural networks for deep learning and understanding automatic differentiation. You will then learn convolutional, recurrent neural networks and build up the theory that focuses on supervised learning and integrate into your product offerings such as search, image recognition, and object processing. You will also learn to examine the performance of the sennt analysis model. Finally, you be glanced through TensorFlow.

By the end of this training program, you’ll comfortably leverage the power of machine learning and deep learning algorithms to build high perfog day-to-day apps.

Meet Your Expert(s)

We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth

Giuseppe Bonaccorso is a machine learning and big data consultant with more than 12 years of experience. He has pursued his masters in electronics eeering from the University of Catania, Italy, and further post graduation specialization from the University of Rome, Tor Vergata, Italy, and the University of Essex, UK. During his career, he has covered different IT roles in several business contexts, including public administration, military, utilities, healthcare, diagnostics, and advertising. He has developed and managed projects using many technologies, including Java, Python, Hadoop, Spark, Theano, and TensorFlow. His main interests are in artificial intelligence, machine learning, data science, and philosophy of mind.

Eder Santana is a PhD candidate in Electrical and Computer Eeering. His thesis topic is on deep and recurrent neural networks. After working for 3 years with Kernel Machines (SVMs, Information theoretic learning, and so on), he moved to the field of deep learning 2.5 years ago, when he started learning Theano, Caffe, and other machine learning frameworks. Now, he contributes to Keras; deep learning library for Python. Besides deep learning, he also likes data visualization and teaching machine learning, either on online forums or as a teacher assistant.

Who this course is for

This Learning Path is for anyone interested to enter the field of data science and are new to machine learning.