Python: Artificial Intelligence with Python: 3-in-1
What you'll learn
- Build applications based on deep learning algorithms to detect and track objects using different algorithms
- Classify text and images according to predefined categories and make use of neural networks, decision trees, random forests for classification
- Use deep reinforcement learning to build an AI that plays arcade games
- Learn the basics of deep learning and artificial neural networks to understand classification and probabilistic predictions with Single-hidden-layer neural networks
- Build supervised model using various machine learning algorithms
- Understand the fundamentals of reinforcement learning to explore application of deep learning in signal processing
- Basic working knowledge of Python programming is expected
- Basic understanding of machine learning and minimal experience in developing AI projects
Artificial Intelligence is one of the hottest fields in computer science right now and has taken the world by storm as a major field of research and development. Python has surfaced as a dominant language in AI/ML programming because of its simplicity and flexibility, as well as its great support for open source libraries such as Scikit-learn, Keras, spaCy and TensorFlow.
This comprehensive 3-in-1 course is designed to teach you the fundamentals of Deep Learning and use them to build intelligent systems. You’ll solve real-world problems such as face detection, handwriting recognition, and more. You’ll get an exposure to hands-on projects that simplify your first steps in the world of Artificial Intelligence with Python. You’ll get well-versed with AI concepts that have you up and running with AI in no time.Contents and Overview
This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.
The first course, Artificial Intelligence with Python – Deep Neural Networks, covers different Artificial Intelligence learning techniques with neural networks. The course is an introduction to the basics of deep learning methods. We will start with object detection and tracking, in which we will track faces, objects and eyes. We will then build a neural network and an OCR. We will then learn how to build learning agents that can learn from interacting with the environment. We will then build an image classifier using convolutional neural networks.
The second course, Python Artificial Intelligence Projects for Beginners, covers Hands-on Python recipes that implement practical examples to help you build artificial intelligence applications with eight realistic projects. You’ll start with your first project that covers decision trees for classifying data using Scikit-learn libraries. Next, you will build a classifier using random forests. Then you will learn about text processing techniques and practice with bag-of-words and word2vec models. By the end of this video course, you will be confident to build your own AI projects with Python and be ready to take on more advanced content as you go ahead.
The third course, Advanced Artificial Intelligence Projects with Python, covers intelligent applications projects with Artificial Intelligence using the Python programming language. The first project introduces natural language processing including part-of-speech tagging and named entity extraction. The next project introduces genetic algorithms. The DEAP library is used. A music data set is used in a genetic algorithm that generates a music playlist satisfying multiple criteria such as song similarity and playlist length. The last project introduces reinforcement learning and deep reinforcement learning. The OpenAI Gym platform and Q-learning algorithm are used to build a game-playing AI.
By the end of this training program you’ll get an hands-on experience with Python recipes and build artificial intelligence applications with different Artificial Intelligence techniques and neural networks.About the Authors
- Prateek Joshi is an Artificial Intelligence researcher, the published author of five books, and a TEDx speaker. He is the founder of Pluto AI, a venture-funded Silicon Valley startup building an analytics platform for smart water management powered by deep learning. His work in this field has led to patents, tech demos, and research papers at major IEEE conferences. He has been an invited speaker at technology and entrepreneurship conferences including TEDx, AT&T Foundry, Silicon Valley Deep Learning, and Open Silicon Valley. Prateek has also been featured as a guest author in prominent tech magazines. His tech blog has received more than 1.2 million page views from over 200 countries and has over 6,600+ followers. He frequently writes on topics such as Artificial Intelligence, Python programming, and abstract mathematics. He is an avid coder and has won many hackathons utilizing a wide variety of technologies. He graduated from University of Southern California with a Master's degree, specializing in Artificial Intelligence. He has worked at companies such as Nvidia and Microsoft Research. You can learn more about him on his personal website.
- Joshua Eckroth is Assistant Professor of Computer Science at Stetson University, where he teaches Big Data Mining and Analytics, Artificial Intelligence (AI), and Software Engineering. Dr. Eckroth joined the Math and Computer Science Department at Stetson University in Fall 2014. He earned his PhD from Ohio State University in the areas of AI and cognitive science, focusing on abductive reasoning and metareasoning. He is an active researcher with numerous refereed publications in the fields of artificial intelligence and computer science education. Dr. Eckroth also serves as Chief Architect at i2k Connect, LLC., whose mission is to revolutionize the ability of companies to find, filter, and analyze data in documents by extracting essential information from data clutter. In addition, Dr. Eckroth is co-editor of AITopics, the Internet's largest collection of information about the research, the people, and the applications of Artificial Intelligence.
Who this course is for:
- Python developers who would like to work on neural networks and artificial intelligence and gain knowledge of how to survive in the world of big data.
- Python developers who want to take their first steps in the world of artificial intelligent solutions using easy-to-follow projects
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