eyvaz27 Goto Github PK

-build-a-modern-computer-from-first-principles-from-nand-to-tetris-

Project-Centered Course Logic Gate Computer Architecture Assembly Languages Hardware Description Language (HDL)

aws-ai-qna-bot

Code samples related to "Creating a Question and Answer Bot with Amazon Lex and Amazon Alexa", published on the AWS AI Blog. QnABot (pronounced “Q and A Bot”), uses Amazon Lex and Amazon Alexa to provide a conversational interface to your “Questions and Answers”, so users can just ask their questions and get quick and relevant answers.

aws-deepcomposer-samples

building-cloud-applications-at-scale

If you are interested in developing the skills needed to be a data engineer, the Python, Bash and SQL Essentials for Data Engineering Specialization is a great place to start. We live in a world that is driven by big data - from what we search online to the route we take to our favorite restaurant, and everything in between. Businesses and organizations use this data to make decisions that impact the ways in which we navigate our lives. How do engineers collect this data? How can this data be organized so that it can be appropriately analyzed? A data engineer is specialized in this initial step of accessing, cleaning and managing big data. Data engineers today need a solid foundation in a few essential areas: Python, Bash and SQL. In Python, Bash and SQL Essentials for Data Engineering, we provide a nuts and bolts overview of these fundamental skills needed for entering the world of data engineering. Led by three professional data engineers, this Specialization will provide quick and accessible ways to learn data engineering strategies, give you a chance to practice what you’ve learned in integrated lab exercises, and then immediately apply these techniques in your professional or academic life.

coursera-convolutions-for-text-classification-with-keras

Convolutional Neural Networks for Text Classification applied by using 'Keras'

deep-reinforcement-learning

Repo for the Deep Reinforcement Learning Nanodegree program

dsnd_term2

Contains files related to content and project of DSND Term 2

fake-news-detection-with-machine-learning

In this course, we are going to focus on the following learning objectives: Apply python libraries to import and visualize datasets Perform exploratory data analysis and plot word-cloud Perform text data cleaning such as removing punctuation and stop words Understand the concept of tokenizer. Perform tokenizing and padding on text corpus to feed the deep learning model. Understand the theory and intuition behind Recurrent Neural Networks and LSTM Build and train the deep learning model Access the performance of the trained model

ibm-machine-learning-with-python

Works from IBM Course : Machine Learning with Python

ibm_practice

IBM: Python For Data Science

introduction-to-embedded-systems-software-and-development-environments

Introduction to Embedded Systems Software and Development Environments by University of Colorado Boulder

introduction-to-reinforcement-learning-in-python

java-programming-and-software-engineering-fundamentals-specialization-cousera

Take your first step towards a career in software development with this introduction to Java—one of the most in-demand programming languages and the foundation of the Android operating system. Designed for beginners, this Specialization will teach you core programming concepts and equip you to write programs to solve complex problems. In addition, you will gain the foundational skills a software engineer needs to solve real-world problems, from designing algorithms to testing and debugging your programs.

machine-learning-zoomcamp

Machine Learning Zoomcamp from DataTalksClub

microsoft-ml-course-linear-regression

numpy-implementation-in-r

pandas-implementation-in-r

plantvillage-dataset

Dataset of diseased plant leaf images and corresponding labels

pytorch-nlp-notebooks

Learn how to use PyTorch to solve some common NLP problems with deep learning.

r_package

New R Package from Developing Data Products

seaborn-visualization-implementation-in-r

sweetviz-implementation-in-r

tensorflow-certification-study-guide

Material and code samples used to help study for and pass the TensorFlow Developer Certification

unsupervised-machine-learning-for-customer-segmentation

This project could be practically applied at any marketing department in the banking and retail industries to segment customers into 'clusters' or 'groups'.