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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-1

Regression, Classification & Clustering Algorithms, Natural Language, Dimensionality Reduction.

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-experiments

All jupyter notebook experiments related to ML, DL

machine-learning-in-python-code---ay---lecture

Machine Learning code in python includes topics like Exploratory Data Analysis (EDA), Classification, Regression, Clustering and Dimensionality Reduction

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-python

Python code for common Machine Learning Algorithms

machine-learning_with_r

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

machinelearningalgorithms-----lecture

Classification, regression, clustering, dimensionality reduction and Ensemble Methods Model selection, regularization, design of experiments, model evaluation Neural Networks, Deep Learning and Reinforcement Learning

machinelearningnotebooks

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

machinelearning_-mth594_

The materials for the course MTH 594 Advanced data mining: theory and applications (Dmitry Efimov, American University of Sharjah)

machine_learning-sl-ul-ml

Practical Machine Learning : Machine Learning in Nut shell, Supervised Learning, Unsupervised Learning, ML applications in the real world. Introduction to Feature engineering and Data Pre-processing: Data Preparation, Feature creation, Data cleaning & transformation, Data Validation & Modelling, Feature selection Techniques, Dimensionality reduction, Recommendation Systems and anomaly detection, PCA ML Algorithms: Decision Trees, Oblique trees, Random forest, Bayesian analysis and Naïve bayes classifier, Support vector Machines, KNN, Gradient boosting, Ensemble methods, Bagging & Boosting, Association rules learning, Apriori and FP growth algorithms, Linear and Nonlinear classification, Regression Techniques, Clustering, K-means, Overview of Factor Analysis, ARIMA, ML in real time, Algorithm performance metrics, ROC, AOC, Confusion matrix, F1score, MSE, MAE, DBSCAN Clustering in ML, Anomaly Detection, Recommender System Self-Study: • Usage of ML algorithms, Algorithm performance metrics (confusion matrix sensitivity, Specificity, ROC, AOC, F1score, Precision, Recall, MSE, MAE) • Credit Card Fraud Analysis, Intrusion Detection system

machine_learning_algorithms

In this repository you will get all necessary algorithms, for supervised and unsupervised learning (Clustering). It's simple and will be a good code guide for Kick-start in ML.

malware-detection-by-system-call-graph-using-machine-learning

Use a system call dependency graph to detect malware and analyze their behavior. The system calls are extracted and collected by Fredrickson and et al.[1] it contains two sets of benchmarks: the malware and the regular software set. The malware set comprises 2631 samples pre-classified into 48 families and 11 types. The regular software set compris

mastering-nextjs

A free video course for building static and server-side rendered applications with Next.js and React.

mastering-scikit-learn

Scikit-learn (Sklearn) is the most useful and robust library for machine learning in Python. It provides a selection of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction via a consistence interface in Python.

medical-consultation-system

CRUD Java Swing