A complete daily plan for studying to become a machine learning engineer.

This repository contains a 90-day cybersecurity study plan, along with resources and materials for learning various cybersecurity concepts and technologies. The plan is organized into daily tasks, covering topics such as Network+, Security+, Linux, Python, Traffic Analysis, Git, ELK, AWS, Azure, and Hacking. The repository also includes a `LEARN.md

A pixel office for your OpenClaw: turn invisible work states into a cozy little space with characters, daily notes, and guest agents. Code under MIT; art assets for non-commercial learning only.

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 BER

A 120-day CUDA learning plan covering daily concepts, exercises, pitfalls, and references (including “Programming Massively Parallel Processors”). Features six capstone projects to solidify GPU parallel programming, memory management, and performance optimization skills.

Scripts that automatically claim and download free daily eBooks from https://www.packtpub.com/packt/offers/free-learning

Download your daily free Packt Publishing eBook https://www.packtpub.com/packt/offers/free-learning

A curated list of trustworthy deep learning papers. Daily updating...

Daily Stock Forecasts using Machine Learning & Python

Open source Android, iOS and Web app for learning about and managing digital and physical security. From how to send a secure message to dealing with a kidnap. Umbrella has best practice guides in over 40 topics in multiple languages. Used daily by people working in high risk countries - journalists, activists, diplomats, business travelers etc.

PDDON windows client, PDDON is a daily drawing tool that supports low code for learning or office use. It can be used for flowchart, mind map, UML diagram, ER diagram, network topology diagram, BPMN, Venn diagram, database model diagram, whiteboard drawing, infinite canvas, and more

Day-wise Python Learning resources from basic concepts to advanced Python applications such as data science and Machine learning. It also includes cheat-sheets, references which are logged daily to accelerate your learning.

I have started Data structures and Algorithms on April 1, 2021, and this repository will be containing my resources, tutorial, codes, and my approach to Qs, for future reference. As I'm in the learning process, this repository will be refreshed daily with my new bits of knowledge.

Tech Notes Hub: All-in-one technical notes & code snippets - covering design patterns, algorithms, data structures, AWS, and more. A centralized knowledge base for learning, reference, and daily use.

🚀 A daily log of my learning journey in development! 💻📚 Here, I share important commands, code snippets, and topics—ranging from basic to advanced—that I'm exploring every day. Join me as I grow and evolve in my coding skills! 🌟

Taskify - An app to manage your daily tasks and boost your productivity. Taskify is built using kotlin and follows all modern android Development practices and hence is a good learning resource for beginners

A 60 days+ streak of daily learning of ML/DL/Maths concepts through projects

This Jesse strategy is based on financial astrology cycles detected with advanced machine learning techniques for the crypto-currencies symbols: ADA, BAT, BNB, BTC, DASH, EOS, LINK, LTC, ZEC and ZRX, the daily price trend is forecasted through this planets cycles and MAs indicator are used to narrow the precise entry/exit. Join our discussions at Telegram:

Healthify - An app to track your daily water intake and sleep and boost your work efficiency. Healthify is built using Kotlin and follows all modern android Development practices and hence is a good learning resource for beginners

Today I Learn (til) - Github `Issues` used as daily learning management system for taking notes and storing resource links.

🇩🇪 German vocabulary 🐳 from A1 to B2 levels 🐠 Curated word lists with ✈ translations example sentences 🐊 Covers everyday vocabulary 🚋 grammar essentials and idioms 🚒Thematic categories for 🚔 efficient learning ( 🏯 travel, work, daily life) 🚌 Perfect for learners 🏟 exam preparation (Goethe, ⛽TELC, ÖSD), and self-study ⛑

Open source Android, iOS and Web app for learning about and managing digital and physical security. From how to send a secure message to dealing with a kidnap. Umbrella has best practice guides in over 40 topics in multiple languages. Used daily by people working in high risk countries - journalists, activists, diplomats, business travelers etc.

My Daily Learning~

Series follows learning from Apache Spark (PySpark) with quick tips and workaround for daily problems in hand

We use advanced machine learning techniques to build models that correlate planet cycles and aspects (cosmic energy) with markets price action to forecast the daily trend direction. Join our discussions at Telegram:

100 Days of Code Learning program to keep a habit of coding daily and learn things at your own pace with help from our remote community.

Using Machine Learning, Regression Analysis, Sabermetrics, and the Love of the Game to predict daily projections for MLB players

In this repo, all about Machine Learning and I covered both Supervised and Unsupervised Learning Techniques with Practical Implementation. Everything from scratch and I solved a lot of different problems with different Machine Learning techniques either related to Healthcare, E-commerce, Sports, or Daily Business Issues.

In Agriculture Price Monitioring , I have used data provided by open government site data.gov.in, which updates prices of market daily . Working Interface Details: We have provided user choice to see current market prices based on two choices: market wise or commodity wise use increase assesibility options. Market wise: User have to provide State,District and Market name and then select market wise button. Then user will be shown the prices of all the commodities present in the market in graphical format, so that he can analyse the rates on one scale. This feature is mostly helpful for a regular buyer to decide the choice of commodity to buy. He is also given feature to download the data in a tabular format(csv) for accurate analysis. Commodity Wise: User have to provide State,District and Commodity name and then select Commodity wise button. Then user will be shown the prices of all the markets present in the region with the commodity in graphical format, so that he can analyse the cheapest commodity rate. This feature is mostly helpful for wholesale buyers. He is also given feature to download the data in a tabular format(csv) for accurate analysis. On the first activity user is also given forecasting choice. It can be used to forecast the wholesale prices of various commodities at some later year. Regression techniques on timeseries data is used to predict future prices. Select the type of item and click link for future predictions. There are 3 java files Forecasts, DisplayGraphs, DisplayGraphs2 ..... Please change the localhost "server_name" at time of testing as the server name changes each time a new server is made. Things Used: We have used pandas , numpy , scikit learn , seaborn and matplotlib libraries for the same . The dataset is thoroughly analysed using different function available in pandas in my .iPynb file . Not just in-built functions are used but also many user made functions are made to make the working smooth . Various graphs like pointplot , heat-map , barplot , kdeplot , distplot, pairplot , stripplot , jointplot, regplot , etc are made and also deployed on the android app as well . To integrate the android app and machine learning analysis outputs , we have used Flask to host our laptop as the server . We have a separate file for the Flask as server.py . Where all the the necessary stuff of clint request and server response have been dealt with . We have used npm package ngrok for tunneling purpose and hosting . A different .iPynb file is used for the time series predictions using regression algorithms and would send the csv file of prediction along with the graph to the andoid app when given a request .

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