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java-coding-problems

Java Coding Problems, published by Packt

java-deep-learning-cookbook

Java Deep Learning Cookbook, published by Packt

java-programming-exercises

Exercises from Daniel Liang's Intro to Java Programming Comprehensive (10th Edition)

java-tutorial-for-beginners

Java Tutorial For Beginners with 500 Code Examples

java_swing_mysql_crud

Java Swing MySQL CRUD with JTable

javastud

Official, Main: This is Core/Advance java example series project. It help to learn java step by step using pdf tutorial provided here and corresponding demo project for the eclipse. Tag: Java Student, Java Stud, Stud Java, StudJava, Java Teachers, Studs Quick Start Guide, Studs Java, Object Oriented Programming, Core Java, Java SE, Java EE, Java Enterprise Edition, Java Blog, Java Articles, Java Web, JSP, Servlet, Maven, Spring, Hibernate, Spring-boot, Spring MVC Web, Angular JS, Angular 2, Java Security, Java CRUD, Java Login Example, File Handling, Multi threading, exception handling, Collection classes, Swing, Database, Date Time, Joda Time, JPA.

jhustatcomputing2021

Course website for JHSPH Statistical Computing (Biostatistics 140.776) in Fall 2021

kivymdbuilder

Drag and drop components, and turn designs into real application screens in minutes instead of hours. Take advantage of the various widgets and their properties to build elaborate and beautiful apps.

laravel-advanced-crud-api

Laravel Advanced API Architecture for Small to Big Project with Auth, Repository, Interfaces, Unit Testing and so many stuffs with Laravel 9.x

learn-python-code-basic-to-advance-with-videos

Python practice set with basic and advance concepts ke sath. Python me database ki connectivity aur xampp server ka use. Is code ka full detail Hey Sushil YouTube Channel par aapko mil jayega.

learning-object-oriented-python

This repository walks you through the Object Oriented Programming in python. Illustrates real world examples, working codes and going about finding a coding solution.

learning_machine_learning

A brief introduction to machine and deep learning combining theory and practises using Jupyter

learnjava

Learn Java Programming

lectures

lectures-labs

Slides and Jupyter notebooks for the Deep Learning lectures at Master Year 2 Data Science from Institut Polytechnique de Paris

login-logout-with-prepared-statement

Simple login logout script in php usin prepared statement.

logistic-regression-with-a-neural-network-mindset

Welcome to your first (required) programming assignment! You will build a logistic regression classifier to recognize cats. This assignment will step you through how to do this with a Neural Network mindset, and so will also hone your intuitions about deep learning. **Instructions:** - Do not use loops (for/while) in your code, unless the instructions explicitly ask you to do so. **You will learn to:** - Build the general architecture of a learning algorithm, including: - Initializing parameters - Calculating the cost function and its gradient - Using an optimization algorithm (gradient descent) - Gather all three functions above into a main model function, in the right order.

machine-learning

Model Building using Supervised, Unsupervised and Reinforcement learning

machine-learning-a-z-1

Implementation of Everything in the video lectures of Machine Learning A-Z course on Udemy by Kirill Eremenko and Hadelin de ponteves in jupyter notebook

machine-learning-a-z-hands-on-python-and-r-in-data-science

Repository For Codes And Concept Taught in Udemy Course

machine-learning-and-deep-learning-projects

▪Deep Learning: Convolutional Neural Network (CNN), Deep Neural Network (DNN) regression ▪Supervised Learning: Decision Tree, Support Vector Machine and Gaussian Naive Bayes ▪Unsupervised Learning: Principal Component Analysis (PCA) and K-means clustering ▪Reinforcement Learning: DDPG (Deep Deterministic Policy Gradient) algorithm

machine-learning-deep-learning

This repository has all my projects on Oil and Gas (and Non Oil & Gas) Machine Learning Topics

machine-learning-end-to-endguide-for-java-developers

Code repository forMachine-Learning-End-to-Endguide-for-Java-developers, Published By Packt

machine-learning-ipt-parrotai

Week1 Report Here is a quick summary of what I have achieved to learn in my first week of training under ParrotAi. Introduction to Machine learning , I have achieved to know a good intro into Machine Learning which include the history of ML ,the types of ML such supervised, unsupervised, Reinforcement learning. And also answers to questions such why machine learning? , challenges facing machine learning which include insufficient data, irrelevant on data, overfitting, underfitting and there solutions in general. Supervised Machine algorithms, here I learnt the theory and intuition behind the common used supervised ML including the KNN, Linear Regressions, Logistic, Regression, and Ensemble algorithm the Random forest. Also not only the intuition but their implementation in python using the sklearn library and parameter tuning them to achieve a best model with stunning accuracy(here meaning the way to regularize the model to avoid overfitting and underfitting).And also the intuition on where to use/apply the algorithms basing on the problem I.e classification or regression. Also which model performs better on what and poor on what based on circumstances. Data preprocessing and representation here I learnt on the importance of preprocessing the data, also the techniques involved such scaling(include Standard Scaling, RobustScaling and MinMaxScaler) ,handling the missing data either by ignoring(technical termed as dropping) the data which is not recommended since one could loose important patterns on the data and by fitting the mean or median of the data points on the missing places. On data representation involved on how we can represent categorical features so as they can be used in the algorithm, the method learnt here is One-Hot Encoding technique and its implementation in python using both Pandas and Sklearn Libraries. Model evaluation and improvement. In this section I grasped the concept of how you can evaluate your model if its performing good or bad and the ways you could improve it. As the train_test_split technique seems to be imbalance hence the cross-validation technique which included the K-fold , Stratified K-fold and other strategies such LeaveOneOut which will help on the improvement of your model by splitting data in a convenience manner to help in training of model, thus making it generalize well on unseen data. I learnt also on the GridSearch technique which included the best method in which one can choose the best parameters for the model to improve the performance such as the simple grid search and the GridSearch with cross-validation technique, all this I was able to implement them in code using the Sklearn library in python. Lastly the week challenge task given to us was tremendous since I got to apply what I learned in theory to solve a real problem.It was good to apply the workflow of a machine learning task starting from understanding the problem, getting to know the data, data preprocessing , visualising the data to get more insights, model selection, training the model to applying the model to make prediction In general I was able to grasp and learn much in this week from basic foundation of Machine Learning to the implementations of the algorithms in code. The great achievement so far is the intuition behind the algorithm especially supervised ones. Though yet is much to be covered but the accomplishment I have attained so far its a good start to say to this journey on Machine learning. My expectation on the coming week is on having a solid foundation on deep learning.

machine-learning-notebooks

15+ Machine/Deep Learning Projects in Ipython Notebooks

machine-learning-portfolio

Compilation of my public Machine Learning projects. Supervised, Unsupervised and Deep Learning Models, together with larger Personal Projects.

machine-learning-projects

Algorithms on supervised, unsupervised, deep learning and other mathematics concepts.

machine-learning-scientist-with-python-by-datacamp

Python programming skill set with the toolbox to perform supervised, unsupervised, and deep learning, learn how to process data for features, train your models, assess performance, and tune parameters for better performance. natural language processing, image processing, and popular libraries such as Spark and Keras.

machine-learning_with_r

Supervised and unsupervised machine learning techniques - Exercises - Using R programming

machinelearningnotebooks

Python notebooks with ML and deep learning examples with Azure Machine Learning Python SDK | Microsoft