Machine Learning and Cognitive Systems
( 24 Modules )

Module #1
Introduction to Machine Learning
Overview of machine learning, its importance, and applications

Module #2
Mathematical Foundations of Machine Learning
Linear algebra, calculus, and probability theory for machine learning

Module #3
Types of Machine Learning
Supervised, unsupervised, and reinforcement learning explained

Module #4
Supervised Learning
Regression, classification, and model evaluation metrics

Module #5
Unsupervised Learning
Clustering, dimensionality reduction, and density estimation

Module #6
Reinforcement Learning
Markov decision processes, Q-learning, and policy gradients

Module #7
Neural Networks Fundamentals
Artificial neural networks, perceptron, and multilayer perceptron

Module #8
Deep Learning
Convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks

Module #9
Cognitive Systems Overview
Introduction to cognitive systems, human cognition, and cognitive architectures

Module #10
Cognitive Architectures
ACT-R, SOAR, and LIDA cognitive architectures explained

Module #11
Natural Language Processing (NLP)
Text preprocessing, tokenization, and language modeling

Module #12
Computer Vision
Image processing, object detection, and image segmentation

Module #13
Robotics and Control Systems
Robotics, control systems, and robotic arm manipulation

Module #14
Machine Learning for Time-Series Data
Time-series forecasting, ARIMA, and LSTM for time-series data

Module #15
Machine Learning for Recommender Systems
Content-based, collaborative filtering, and hybrid recommender systems

Module #16
Machine Learning for Healthcare
Medical imaging, disease diagnosis, and personalized medicine

Module #17
Ethics and Fairness in AI
Bias, fairness, and transparency in machine learning models

Module #18
Explainability and Interpretability in AI
Model interpretability, feature importance, and explainable AI

Module #19
Transfer Learning and Domain Adaptation
Transfer learning, domain adaptation, and fine-tuning pre-trained models

Module #20
Generative Models
Generative adversarial networks (GANs), variational autoencoders (VAEs), and generative adversarial networks (GANs)

Module #21
Big Data and Distributed Machine Learning
Distributed machine learning, big data processing, and NoSQL databases

Module #22
Machine Learning at Scale
Scaling machine learning models, model deployment, and model serving

Module #23
Cognitive Robotics and Human-Robot Interaction
Cognitive robotics, human-robot interaction, and robotics for cognitive systems

Module #24
Course Wrap-Up & Conclusion
Planning next steps in Machine Learning and Cognitive Systems career

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Machine Learning and Cognitive Systems ( 24 Modules )

Machine Learning and Cognitive Systems
( 24 Modules )