eep Learning is revolutionizing a wide range of industries. Deep Learning allows you to solve problems where traditional Machine Learning methods might perform poorly: detecting and extracting objects from images, extracting meaning from text, and predicting outcomes based on complex dependencies, to name a few. If you're a Data Scientist who have basic Machine Learning knowledge and want to explore the possibilities of Deep Learning, then this Course is perfect for you!

This comprehensive 3-in-1 course is a direct, practical, and very hands-on approach where we deal less with theory and adopt a more hands-on style of learning. Initially, you’ll get hands-on experience building basic neural network models (and no maths!) using Python. You’ll also build a deep learning-based image recognition system using Python and learn how to deploy and integrate it into web apps or phone apps. Moving further, a discussion on the corresponding pros and cons of implementing solutions using a popular framework such as TensorFlow, PyTorch, and Keras is provided. Finally, you’ll reuse Python code snippets and adapt them to everyday problems also, evaluate the cost/benefits and performance implication of each solution.

By the end of this course, you'll apply Deep Learning concepts and use Python to solve challenging tasks. Identify mean tweets, detect smiles in your camera app, forecast stock prices, and more using Neural Networks.

Contents and Overview

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

The first course, Python Deep Learning for Beginners, covers applying Deep Learning concepts and use Python to solve challenging tasks. This course will teach you to apply deep learning concepts using Python to solve challenging tasks. You'll build a Python deep learning-based image recognition system and deploy and integrate images into web apps or phone apps. You will start out with an intuitive understanding of neural networks in general. We will guide you through the building blocks of deep learning networks to tackle complex neural networks.
So, take this course and learn the skills and temperament need to enter the AI marketplace today.

The second course, Real-World Python Deep Learning Projects, covers identifying mean tweets, detect smiles in your camera app, forecast stock prices, and more using Neural Networks. You will start off by creating neural networks to predict the demand for airline travel in the future. Then, you'll run through a scenario where you have to identify negative tweets for a celebrity by using Convolutional Neural Networks (CNN's). Next, you will create a neural network which will be able to identify smiles in your camera app. Finally, the last project will help you forecast a company's stock prices for the next day using Deep Learning. By the end of this course, you will have a solid understanding of Deep Learning and the ability to build your own Deep Learning models.

The third course, Python Deep Learning Solutions, covers over 20 practical videos on neural network modeling, reinforcement learning, and transfer learning using Python. The course includes solutions that are related to the basic concepts of neural networks; all techniques, as well as classical network topologies, are covered. The main purpose of this video course is to provide Python programmers with a detailed list of solutions so they can apply Deep Learning to common and not-so-common scenarios.

By the end of this course, you'll apply Deep Learning concepts and use Python to solve challenging tasks. Identify mean tweets, detect smiles in your camera app, forecast stock prices, and more using Neural Networks.