Machine Learning Fundamentals
Walkthrough into the Machine Learning's principles and implementation of the main algorithms from scratch.

Activation Functions
Brief overview about some of the main activation functions applicable to Neural Networks and Deep Learning system.
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Perceptron
Overview and implementation of the most fundamental Neural Network model.
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Binomial Distribution
Brief overview about Discrete probability and binomial distribution.
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Clustering [DBSCAN]
Overview and implementation of clustering algorithm using the DBSCAN technique.
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Clustering [kmeans]
Overview and implementation of clustering algorithm using the kmeans technique.
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kNN Classification
Overview and implementation of kNearest Neighbor Classification.
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kNN Regression
Overview and implementation of kNearest Neighbor regression.
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Linear Regression
Overview and implementation of Linear Regression analysis.
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Logistic Regression
Overview and implementation of Logistic Regression analysis.
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Polynomial Regression
Overview and implementation of Polynomial Regression analysis.
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Deep Learning Models
Demonstration and practice of the most popular Deep Learning models.

Basics [PyTorch]
Basic functions and operations using PyTorch library.
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Basics [TensorFlow]
Basic functions and operations using TensorFlow library.
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Perceptron [Keras]
Implementation of Perceptron model using using Keras library.
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Perceptron [PyTorch]
Implementation of Perceptron model using using PyTorch library.
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Perceptron [TensorFlow]
Implementation of Perceptron model using using TensorFlow library.
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Multiclass Logistic Regression [Keras]
Implementation of Multiclass Logistic Regression using Keras library.
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Multiclass Logistic Regression [PyTorch]
Implementation of Multiclass Logistic Regression using PyTorch library.
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Multiclass Logistic Regression [TensorFlow]
Implementation of Multiclass Logistic Regression using TensorFlow library.
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Shallow Neural Network [Keras]
Implementation of Shallow Neural Network using Keras library.
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Deep Neural Network [Keras]
Implementation of Deep Neural Network using Keras library.
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Convolutional Neural Network [Keras]
Implementation of Convolutional Neural Network using Keras library.
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Autoencoder [Keras]
Implementation of Autoencoders using Keras library.
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Practical Applications
Practical experiments and creative applications using machine learning techniques.

Image Approximation
Image approximation and upscaling interpolation using deep Neural Network.
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Natural Language Processing
Everything about Natural Language Processing (NLP) , from fundamental concepts to practical applications.

Basics [NLTK]
Basics aspects of Natural Language Toolkit.
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Mathematical Foundations
Main mathematical concepts and numerical methods applied to Machine Learning.

Calculus  Fourier Series
Brief overview of Fourier series.
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Linear Algebra  Vectors
Linear Algebra topic about Vectors.
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Linear Algebra  Matrices
Linear Algebra topic about Matrices.
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Numerical Integration
Overview and implementation of some numerical methods for definite integration.
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Numerical Root Finding
Overview and implementation of some numerical methods for root finding.
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Dissimilarity Measure
Overview about dissimilarity and distance measure.
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Z Table
Study about standard normal distribution.
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High Performance Computing
Practices over high performance computing subjects such as parallel computing, gpu programming, code optimization and others.

Basics [Numba]
Basic functions and operations using Numba and Python.
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Basics [NumExpr]
Basic functions and operations using NumExpr and Python.
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Basics [Cython]
Basic functions and operations using Cython and Python.
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Basics [F2PY]
Basic functions and operations using F2PY and Python.
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