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Ayoade J.'s Projects

deeplearning-lectures icon deeplearning-lectures

Here is the material for a course of two-weeks I will be giving in a Master of Data Science and AI

deeplearningproject icon deeplearningproject

An in-depth machine learning tutorial introducing readers to a whole machine learning pipeline from scratch.

deep_and_machine_learning_projects icon deep_and_machine_learning_projects

This Repository contains the list of various Machine and Deep Learning related projects. Related code and data files are available inside this folder. One can go through these projects to implement them in real life for specific use cases.

deploymlmodel-flask icon deploymlmodel-flask

This is a simple project to elaborate how to deploy a Machine Learning model using Flask API.

digit-detection-using-r- icon digit-detection-using-r-

In this project , machine learning ,deep learning tools and technique were used to Analyze the MNIST dataset .MNIST is a large database of handwritten digits that is commonly used for training various image processing systems.

disaster-response-pipelines icon disaster-response-pipelines

The project aim is to analyze disaster data from Figure Eight (https://appen.com) to build a model for an API that classifies disaster messages. the data set containing real messages that were sent during disaster events. Iā€™m creating a machine learning pipeline to categorize these events and then send the messages to an appropriate disaster relief agency. The project also include a web app where an emergency worker can input a new message and get classification results in several categories. The web app also display visualizations of the data. The project done in three phases the first one the ETL pipeline: in this phase I have done loads the messages and categories the datasets, merges the two datasets, cleans the data, and stores it in a SQLite database. In the second, ML pipeline: in this phase I have done loads the data from the SQLite database, splits the dataset into training and test sets, builds a text processing and machine learning pipeline, trains and tunes a model, outputs results on the test set and exports the final model as a pickle file. The last one, the deployment process using Flask web application.

dl-notebooks icon dl-notebooks

A collection of practical handson jupyter notebooks on bigdata/ml/dl/rl/cv/nlp/ds/scipy/viz-lib/various command lines

dl2_notebooks icon dl2_notebooks

Jupyter Notebooks from posts and ML/DL pipelines I've created

dlaicourse-tensorflow icon dlaicourse-tensorflow

Repository containing Jupyter Notebooks for the TensorFlow in Practice specialization in Coursera

dlpractice_10m icon dlpractice_10m

Repository for Jupyter Notebooks used for LG Deep Learning Course

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