Artificial Intelligence Training @ Ether Infotech
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 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.
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 the classification and probabilistic predictions with Single-hidden-layer neural networks
- Build a supervised model using various machine learning algorithms
- Understand the fundamentals of reinforcement learning to explore the application of deep learning in signal processing
Syllabus
Essential foundations of AI :
The programming tools (Python, NumPy, PyTorch), the math (calculus and linear algebra), and the key techniques of neural networks (gradient descent and backpropagation).
Neural Networks:
- Introduction to neural networks
- Training neural networks
- Early stopping, regularization and dropout
- Deep learning with pytorch
- Overview of Deep Learning and Convolutional Neural Networks
- Identifying Handwritten Mathematical Symbols with Convolutional
- Neural Networks
- Deep Reinforcement Learning
AI Implementations:
- Building an Image Classifier Using a Single Layer Neural Network
- Building an Image Classifier Using a Convolutional Neural Network
- Building a Perceptron Based Classifier
- Constructing Single and Multilayer Neural Networks
- Building a Vector Quantizer
- Analyzing Sequential Data Using Recurrent Neural Networks
- Visualizing Characters in an Optical Character Recognition Database
- Building an Optical Character Recognition Engine
- Building a Perceptron-Based Linear Regressor
- Python for data science
- Python for machine learning
By the end of this training program, you’ll get hands-on experience with Python recipes and build artificial intelligence applications with different Artificial Intelligence techniques and neural networks.
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We provide 100% placement assistance . We are very proud of the fact that all our students are well placed in top level companies