Machine Learning
Discover and master the world of artificial intelligence with WiNK's own Machine Learning course, a comprehensive and exhaustive in the depth review of one of the most exciting fields of tech to date.
About the course
In recent years, there has been an increasing discussion about Artificial Intelligence, and the advancements in this field indicate that it will continue to be a prominent topic for a significant period. The driving force behind this interest lies in the potential to develop systems that can learn from their own experiences and enhance their responses based on their mistakes and successes. Consequently, one can envision a wide range of applications for such systems, spanning across industries like automotive, telecommunications, biomedical, defense, as well as encompassing sentiment analysis, business intelligence support, and data mining.
Objectives
The course aims to provide participants with the necessary knowledge to develop Artificial Intelligence systems, starting from the principles of neural networks and progressing to understand and implement complex Machine Learning and Deep Learning algorithms. Furthermore, this knowledge will be applied to large amounts of data to learn the basics of Big Data processing.
The theoretical study will be accompanied by practical activities, to be carried out both independently and in groups.
Course Length
160 hrs
Prerequisites
Basic coding knowledge
What you'll learn:
- Historical insight on AI and its application domains
- Analyzing Neural Networks' learning systems
- The mechanics of Machine Learning and Deep Learning
- Q-learning, model-based RL, exploration-exploitation, and asynchronous actor-critic agents
- Utilize AI and ML techniques on large datasets using popular frameworks
- Analyze real-world applications of AI and ML,their algorithms and the techniques employed
- Hands-on experience with Spark ML for practical implementation and experimentation
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A Comprehensive Journey into Artificial Intelligence Systems and Big Data Processing
This course empowers participants to develop AI systems through an exploration of neural networks, Machine Learning, and Deep Learning and gain expertise and effective implementation skills in complex algorithms.
Contact Us →Machine Learning course program:
Introduction to Artificial Intelligence
- Historical overview
Application domains
- Intelligent agents
- Simple reactive agents
- Model-based agents
- Goal-based agents
- Utility-based agents
Turing Test Neural Networks
- Analysis of the learning system
- Learning paradigms
- Hebbian learning
- Feedforward neural networks
- Backpropagation algorithm
- Types of neural networks
- Hopfield networks
- Elman networks
- Self-organizing maps (SOM)
- ANN networks
Machine Learning
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Classification
- Regression
- Clustering
- Approaches
- ILP
- Decision tree
- Association rules
- Artificial Neural Networks
- Genetic algorithms
- Bayesian networks
- Support Vector Machines
Deep Learning
- Deep neural networks
- Convolutional neural networks
- Recursive neural networks
- Deep Belief networks
- Boltzmann machines
- Multilayer kernel machines
- Differentiable LSTM-related memory structures
- Deep-Q networks
- Semantic hashing
- TensorFlow lab
Reinforcement learning
- Q-Learning Agents
- Model-Based RL
- Exploration-Exploitation
- Asynchronous Actor-Critic Agents (A3C)
Big Data
- Utilizing AI and ML techniques on large datasets
- Hadoop framework
- Spark framework
- Spark ML
Examples of real-world applications using AI and ML, and analysis of the algorithms and techniques used.
Spark ML Lab
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