Fundamentals of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), including both supervised and unsupervised algorithms.
Practical experience in data preprocessing and data visualization using Python.
In-depth understanding of supervised learning, including regression, as well as unsupervised Machine Learning algorithms such as Support Vector Machines (SVM) and K-Nearest Neighbors (KNN).
Exploring the intricacies of Deep Learning techniques, including single and multi-layer Perceptron and Long Short-Term Memory (LSTM).
Introduction to AI and machine learning tools and techniques, covering topics such as simple and multiple linear regressions.
Comprehensive knowledge of neural networks, multilayer perceptron, Back Propagation, and Convolutional Neural Networks (CNN) applications.
Delving into heuristic and meta-heuristic techniques employed in AI.
Application of advanced research methodologies in Machine Learning and Deep Learning.
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