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breast-cancer-visualization-and-classification

This analysis aims to observe which features are most helpful in predicting malignant or benign cancer and to see general trends that may aid us in model selection and hyper parameter selection.

complete-feature-selection

coursera

Repository for lectures offered from Coursera Plus

dask-video-tutorial

Dask tutorial material for video tutorial series

data-mining

Data Mining course resources (University of Alicante Mater Degree in Data Science)

datascience

datascience-itbd

deep-learning-for-hackers

Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencoders, Time Series Forecasting, Object Detection, Sentiment Analysis, Intent Recognition with BERT)

deep-learning-introduccion-practica-con-keras

Github del libro Deep Learning - Introducción práctica con Keras

gcd

getting-started-with-tensorflow-2

All my code for the course Getting Started with Tensorflow 2 by Imperial College London. Well Written and Commented for Anyone to learn tf2. Check out the Markdowns for every folder.

getting-started-with-tensorflow-2-coursera

getting-things-done-with-pytorch

Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BERT.

handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

human-activity-recognition-using-smartphones-sensor-dataset

Human activity recognition, is a challenging time series classification task. It involves predicting the movement of a person based on sensor data and traditionally involves deep domain expertise and methods from signal processing to correctly engineer features from the raw data in order to fit a machine learning model.

itbd

lazypredict

Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning

ml_dl_interpretability

HANDS-ON WORKSHOP ON ML & DL INTERPRETABILITY

models

Models and examples built with TensorFlow

mongodb_pymongo_tutorial

Introduction to MongoDB and tutorial to implement in Python using PyMongo

neural-nets

Source code to go with my introductory video on neural nets in python

nm

pandas

Data & Code for my video on the Pandas library of Python

pandas-data-science-tasks

Set of real world data science tasks completed using the Python Pandas library

pml

prueba

r

sandraamadorm-mineriadedatos

unlocked_challenge_4