TensorFlow is quickly becoming the technology of choice for machine learning and deep learning, because of its ease to develop intelligent machine learning applications and powerful neural networks. If you're a data professional who wants to use TensorFlow for performing machine learning and deep learning activities on a day-to-day basis, then go for this course.

This course gives you a clear understanding of machine learning models and the application of models at scale using clustering, classification, regression, and reinforcement learning, all with interesting examples and real-world use cases. You will then delve into deep learning with TensorFlow to gain the skills required to implement your own neural networks and apply them to the real world. You will also explore deep neural networks for different problems and the applications of Convolutional Neural Networks on two real datasets. Next, you will learn some of the important techniques to implement generative adversarial networks. Finally, you will learn to tackle any problems related to time series using RNN. This course is a perfect blend of concepts and practical examples which makes it easy to understand and implement. It follows a logical flow where you will be able to develop efficient and intelligent applications based on your understanding of the different machine learning and deep learning concepts with every section.

Contents and Overview

This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Hands-on Machine Learning with TensorFlow, focuses on key machine learning techniques and algorithms and you'll apply them practically using TensorFlow models in a hands-on approach. Each section covers a specific machine learning task and you will implement it on your system with TensorFlow models. For example, you will learn Logistic Regression and will then implement it with TensorFlow for your analysis tasks. You'll implement techniques such as classification and clustering effectively using TensorFlow. Similarly, this course takes you through different ML tasks/algorithms and teaches you to implement them in your applications/systems.

In the second course, Getting Started with TensorFlow for Deep Learning, you will be equipped with the skills to implement your own neural networks and apply them to the real world. You will use TensorFlow, an efficient Python library used to create and train your neural networks. You will then build up to more advanced networks. You'll learn to utilize a Convolutional Neural Network to classify images of handwritten text and then take your CNN further to perform object detection and localization in an image.

The third course, Hands-on Deep Learning with TensorFlow, begins with a quick introduction to TensorFlow essentials. You will then start with deep neural networks for different problems and explore the applications of Convolutional Neural Networks on two real datasets. If you’re facing time series problem then we will show you how to tackle it using RNN. You will also learn how autoencoders can be used for efficient data representation. Next, you will be glanced through some of the important techniques to implement generative adversarial networks. All these modules are developed with step by step TensorFlow implementation with the help of real examples.

By the end of this course, you will be able to build powerful machine learning and deep learning applications using TensorFlow.

Meet Your Expert(s):

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

• Kaiser Hamid Rabbi  is an aspiring Data Scientist who is super-passionate about Artificial      Intelligence, Machine Learning, and Data Science. Over the last four      years, he has entirely devoted himself to learning more about data science      technologies such as Python, Machine Learning, Deep Learning, Artificial      Intelligence, data mining, data analysis, recommender systems, and so on.      Kaiser also has a huge interest in Lygometry (things we know we do not      know!) and always tries to understand Domain Knowledge from his projects      as far as possible.

  
  

• Colibri  Digital is a technology consultancy company founded in 2015 by James Cross      and Ingrid Funie. The company works to help its clients navigate the      rapidly changing and complex world of emerging technologies, with deep      expertise in areas such as big data, data science, machine learning, and      cloud computing. Over the past few years, they have worked with some of      the world's largest and most prestigious companies, including a tier 1      investment bank, a leading management consultancy group, and one of the      World's most popular soft drinks companies, helping each of them to make      better sense of its data and process it in more intelligent ways. The      company lives by its motto: Data -> Intelligence -> Action.     
       

• Tom Joy is studying for a PhD at the University of Oxford in the      field of Semantic SLAM, which is the process of simultaneously localizing    a robot in space; producing a map/understanding of the surrounding area      whilst also detecting and delineating objects in 3D space. Achieving this requires a high level of competency in computer vision, machine learning, and optimization.
        Tom has extensive experience in computer vision and machine learning, having taken several internships and placements over the course of his      degree and spent time in industry prior to starting his PhD. He is a big      advocate of explaining concepts simply and in a clear and concise manner;  he strives to obtain and provide a comprehensive understanding of all      relevant methods to the task at hand.

  
  

• Salil Vishnu Kapur is a Data Science Researcher at the Institute for Big Data Analytics, Dalhousie University. He is extremely passionate about Machine Learning, Deep Learning, Data mining and Big Data Analytics. Currently working as a Researcher at Deep Vision and prior to that worked as a Senior Analyst at Capgemini for around 3 years with these technologies. Prior to that Salil was an intern at IIT Bombay through the FOSSEE Python TextBook Companion Project and presently with the Department of Fisheries and Transport Canada through Dalhousie University.