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Machine Learning Fundamentals

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

  • Activation Functions

    Activation Functions

    Brief overview about some of the main activation functions applicable to Neural Networks and Deep Learning system.

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  • Perceptron

    Perceptron

    Overview and implementation of the most fundamental Neural Network model.

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  • Binomial Distribution

    Binomial Distribution

    Brief overview about Discrete probability and binomial distribution.

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  • Clustering [DBSCAN]

    Clustering [DBSCAN]

    Overview and implementation of clustering algorithm using the DBSCAN technique.

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  • Clustering [k-means]

    Clustering [k-means]

    Overview and implementation of clustering algorithm using the k-means technique.

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  • k-NN Classification

    k-NN Classification

    Overview and implementation of k-Nearest Neighbor Classification.

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  • k-NN Regression

    k-NN Regression

    Overview and implementation of k-Nearest Neighbor regression.

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  • Linear Regression

    Linear Regression

    Overview and implementation of Linear Regression analysis.

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  • Logistic Regression

    Logistic Regression

    Overview and implementation of Logistic Regression analysis.

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  • Polynomial Regression

    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]

    Basics [PyTorch]

    Basic functions and operations using PyTorch library.

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  • Basics [TensorFlow]

    Basics [TensorFlow]

    Basic functions and operations using TensorFlow library.

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  • Perceptron [Keras]

    Perceptron [Keras]

    Implementation of Perceptron model using using Keras library.

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  • Perceptron [PyTorch]

    Perceptron [PyTorch]

    Implementation of Perceptron model using using PyTorch library.

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  • Perceptron [TensorFlow]

    Perceptron [TensorFlow]

    Implementation of Perceptron model using using TensorFlow library.

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  • Multi-class Logistic Regression [Keras]

    Multi-class Logistic Regression [Keras]

    Implementation of Multi-class Logistic Regression using Keras library.

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  • Multi-class Logistic Regression [PyTorch]

    Multi-class Logistic Regression [PyTorch]

    Implementation of Multi-class Logistic Regression using PyTorch library.

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  • Multi-class Logistic Regression [TensorFlow]

    Multi-class Logistic Regression [TensorFlow]

    Implementation of Multi-class Logistic Regression using TensorFlow library.

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  • Shallow Neural Network [Keras]

    Shallow Neural Network [Keras]

    Implementation of Shallow Neural Network using Keras library.

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  • Deep Neural Network [Keras]

    Deep Neural Network [Keras]

    Implementation of Deep Neural Network using Keras library.

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  • Convolutional Neural Network [Keras]

    Convolutional Neural Network [Keras]

    Implementation of Convolutional Neural Network using Keras library.

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  • Autoencoder [Keras]

    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

    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 [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

    Calculus - Fourier Series

    Brief overview of Fourier series.

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  • Linear Algebra - Vectors

    Linear Algebra - Vectors

    Linear Algebra topic about Vectors.

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  • Linear Algebra - Matrices

    Linear Algebra - Matrices

    Linear Algebra topic about Matrices.

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  • Numerical Integration

    Numerical Integration

    Overview and implementation of some numerical methods for definite integration.

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  • Numerical Root Finding

    Numerical Root Finding

    Overview and implementation of some numerical methods for root finding.

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  • Dissimilarity Measure

    Dissimilarity Measure

    Overview about dissimilarity and distance measure.

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  • Z Table

    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]

    Basics [Numba]

    Basic functions and operations using Numba and Python.

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  • Basics [NumExpr]

    Basics [NumExpr]

    Basic functions and operations using NumExpr and Python.

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  • Basics [Cython]

    Basics [Cython]

    Basic functions and operations using Cython and Python.

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  • Basics [F2PY]

    Basics [F2PY]

    Basic functions and operations using F2PY and Python.

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