- Two months intensive training with certification.
- Apply artificial neural networks in practice
- Understand the intuition behind self-organizing maps
- Understand the intuition behind artificial neural networks
- Apply Convolutional neural networks in practice
- The impacts Machine Learning & Data Science is having on society
- How to avoid problems with Machine Learning, to successfully implement it without losing your mind
ML is one step closer to human mind in providing computers with the capability to learn without being exceptionally programmed. In order to do so, the computer is given an intention and performance measure, and it uses data and algorithms to train itself on how to get nearer and closer to the desired outcome until it succeeds.
The human brain does far more than linear thinking. It takes experience and context into consideration, as well as adapts continuously. For such complex and non-linear deductions, a machine learning technique called “deep learning” is usually applied. While a machine learning model self-learns by being fed more data, a deep learning model goes further by independently learning through its computing “brain.”
Implementing a Linear Regression model for predicting house prices from Boston dataset Implementing a Logistic Regression model for classifying Customers based on an Automobile purchase data set.
AI involves machines that can perform tasks that are characteristic of human intelligence. While this is rather general, it includes things like planning, understanding language, recognizing objects and sounds, learning, and problem solving. We can put AI in two categories, general and narrow. General AI would have all the characteristics of human intelligence, including the capacities mentioned above. Narrow AI exhibits some facet(s) of human intelligence, and can do that facet extremely well, but is lacking in other areas. A machine that’s great at recognizing images, but nothing else, would be an example of narrow AI. At its core, machine learning is simply a way of achieving AI.
About the Trainer:
The trainer for our ML course is much experienced professional who is also a:
- Research Fellow in AI, Microsoft Research, present
- intern, IBM, Fall 2017
- Machine Learning Engineer, Nebulaa Innovations, Summer 2017
- Computer Vision Engineer, Ayasta Technologies, Summer 2017
- Virtual Reality Research Intern, KoÇ University, Istanbul, 2015
- The Trainer’s Specialization: AI/ML, Computer Vision, Self-Driving Cars.
Module 1: Machine Learning?
- Linear Model regression
- Linear Model Classification
- Evaluation Metrics
- Decision trees.
- Random forests
- Support Vendor Machines
- Clustering Methods
- Markov models
Linear Model regression
Linear Model Classification
Support Vendor Machines
Module 2: Deep Learning?
- The Neural Network
- Advanced Activations
- Deep Neural Networks.
- Convolutional Neural Networks.
- State of the Art Networks
- Deep Generative Models
- Recurrent Neural Networks.
- Embedding Spaces
- CNN’s + RNN’s
- GRU’s / LSTMs
- Memory Augmented Networks
- Multimodal Deep Learning
- Deep Reinforcement Learning
The Neural Network
Deep Neural Networks
Convolutional Neural Networks
State of the Art Networks
Deep Generative Models
Recurrent Neural Networks
CNN’s + RNN’s
GRU’s / LSTMs
Memory Augmented Networks
Multimodal Deep Learning
Multimodal Deep Learning
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