Master the fundamentals of machine learning, deep learning, and mathematical optimization by building key concepts and models from scratch using Python.

This project demonstrates the application of machine learning techniques to predict house prices based on various features. By analyzing the dataset, preprocessing the data, and selecting an appropriate model, we were able to achieve a high level of accuracy in predicting house prices. The trained model can be further refined and deployed.

In this session, we'll show you how to use Python to automagically turn a PDF into an audiobook, without anyone needing to read the contents out loud to procure the audio. To achieve this, we'll use a few separate Python libraries—namely Pyttsx3 (for speech to text) and PyPDF2 (to parse PDF files)—and show you how to put it all together to obtain downloadable audio from your PDF input in a single command. We'll also demonstrate how you can customize this process to modulate output voice and speed. This technique can easily be then further refined for nuances of text and speech using other libraries and programming (including NLP/machine learning-based ones) Prerequisites: —Python (https://www.python.org/downloads/) —Visual Studio Code (https://code.visualstudio.com/download) ----------------------------------------- To learn more about The Assembly’s workshops, visit our website, social media or email us at workshops@theassembly.ae Our website: http://theassembly.ae Instagram: http://instagram.com/makesmartthings Facebook: http://fb.com/makesmartthings Twitter: http://twitter.com/makesmartthings #Python #Tutorial

Members: Vishwanath Patil Roopam Rajvanshi Manisha Shivshette Thien Nguyen FarmBay Our aim is to make the lives of millions of farmers easier by helping them make criticial decisions about the type of crops best suited for their fields with respect to maximum monetary gains and at the same time giving them an online market place where they can easily sell off their produce based on an auction framework. This will not only help farmers but also wholesale buyers of crops and give them access to more options thus creating a better interconected network for business. To acieve the first objective, we implement Machine Learning and Predictive Data Modeling on the dataset collected for the previous years for all the crops from the National Agricultural Statistics Service (NASS) arm of United States Department of Agriculture (USDA). Data is refined as per requirement so that it gives a list of best suited crops in descending order for the farmer based on key input parameters namely Zip Code, State, Farming Area, Time Duration, Amount Invested and Expected Returns. Using the suggestion from the Machine Learning algorithm, the farmer can make a well informed decision about the crop he's most likely to receive maximum profits from. The second objective involves giving the farmer the ability to list his crop on an online market place and place a minimum value of his produce as per his calculations. That is the minimum bid at which the farmer can make the sale. Once the crop is placed on the market place, a buyer anywhere in the nation will be able to see the listed crop and place a bid accordingly. Firebase Database instance is used to store the crop data for all farmers and the consequent bidding process. Blockchain is implemented to approve the transactions in real time and make the entire process as seamless as possible. The inspiration behind this project is to create something to serve the farmers who even after putting in so much effort into their fields end up losing huge sums of money becasue of insufficient information. We believe in serving and creating a better community and improving the lives of people by doing our part in the bigger scheme of things.

Python and MATLAB code examples and demos from the textbook "Machine Learning Refined" (Cambridge University Press)

A revised and refined adaptation of the Udacity Machine Learning course.

We apply a machine learning method and a refined basaltic dataset to reconstruct the relative proportion of continental crust exposed above the oceans since 3.8 billion years ago.

This project presents a machine learning pipeline for Network Anomaly Detection, leveraging the power of Random Forests to identify malicious activities in network traffic. The system is trained on the refined NSL-KDD dataset and can perform both binary and multi-class classification of network intrusions.

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Machine Learning Refined EDITION2 exercise answer

A refined pipeline to for machine learning. The goal of this pipeline is to be reusable and easy for teams to use.

Predicted earthquake by using the refined machine learning model. By implementing several machine learning techniques, makes the model more accurate and perfect.

Titanic Survivor Predictor: A multiple machine learning model project to forecast survival outcomes of Titanic passengers. Engineered from historical data, refined with feature selection, tested with CI/CD, and outputs validation metrics.

Uses machine learning and data science to generate unique color combinations by merging base colors into a refined dataset. Applies ensemble modeling to predict aesthetic, AI-driven palettes and backgrounds from raw color data

This project employs machine learning techniques, focusing on the Vectorizer method. It extracts and preprocesses data like actors, genres, movie names, ratings, and IDs, resulting in a refined movie.csv file. Hosted on Heroku, the project employs .git files. Converging machine learning, web development, and data management, this project details.

This project examined Global Production and Consumption Quantities of Refined Petroleum Products using EIA data. The data was visualized, yielding meaningful results. Deep learning and machine learning predict models were developed to predict US future consumption. For Turkish Article : https://medium.com/@aydogdunurdan

🔴 Credit Risk Prediction 🔴 A machine-learning–based analysis designed to predict whether a loan applicant is likely to default. Using a refined Credit Risk Dataset, I cleaned, processed, and visualized key financial features such as income, loan amount, and credit history. Multiple classification models were trained with accuracy.

In the age of connected devices, the quest for efficient energy consumption has peaked more than ever. Various industries attempt to make their existing operations more sustainable to prevent the planet from further damage. It is especially true in the case of the manufacturing sector as it has now reached an undeniable inflection point of being transformed by the fourth industrial revolution. The move towards digital transformation requires the existing manufacturers to rely heavily on smart devices, equipment, cloud computing, IoT, machine learning, artificial intelligence, and many more, which collectively consume vast amounts of energy. For this reason, energy efficiency is forming the core of Industry 4.0 operations. According to US Energy Information Administration, industrial energy usage in Europe accounts for 26% of overall energy consumption. At the same time, the industrial sector in the United States accounts for roughly 32% of the country's overall energy consumption. With these facts in mind, it's evident that energy efficiency and Industry 4.0 are a must-have combination. Overall, manufacturing sector is deemed as unsustainable in the way it operates. Integrating technological systems can unlock the many sustainable benefits for it by enabling more flexibility, scalability, and reliability to the complete production system. According to the United Nations Sustainable Development Goals, sustainability is a crucial business strategy for the future. As a part of sustainability, energy efficiency forms an inherent component for smart manufacturing. Digital transformation of the manufacturing units primarily contributes to energy efficiency by allowing the energy sector to remodel its operating landscape and promote more intelligent, modern, and sophisticated energy production and distribution systems. In this pursuit, data is an excellent ally. By recognizing inefficient operations, optimizing production and logistics routing, and predicting maintenance needs, data gathered from sensors, processed with analytics, and fed into predictive models can contribute to energy savings. Data also enables virtual testing and modeling of equipment, processes, and plants with digital twins, allowing demand to be adjusted to energy price and availability, temperature control to be refined, and even R&D expenses to be reduced. Solulever, a Dutch technology startup, aims at bringing the revolution of Industry 4.0 to its customers by aligning them with sustainability performance. Brabo, a manufacturing connectivity and intelligence platform developed by Solulever, aims at integrating smart manufacturing at the existing manufacturing sites in a sustainable manner. The Industry 4.0 platform provides OT-IT connectivity at the shop floor by connecting to the edge devices and equipment for real-time decision-making. The data collected by these edge devices helps optimize the use of utilities such as energy, air, catalysts, and others. By analyzing the data collected by the smart sensors, wastage of the utilities can be determined to make the processes more efficient. With more sophisticated operations, energy can be consumed by the devices efficiently, further reducing its use to manage the waste produced by the manufacturing operations. Doing so, not only is there a reduction in waste production and power management, but there is also an additional saving of the energy conserved. Industry 4.0 seems to be the most sought after technology that paves path for tremendous success for manufacturing sector by reducing the negative impacts of the industry on the environment. As closing notes it can be said that, an immense potential can be seen in the way the industry can be transmuted to a better version of itself where it renders unimaginable solutions to the world alongside making it healthier.

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Notebook assets for Jupyter notes in the Machine Learning Refined main repo.

Animation gifs illustrating widgets and walkthroughs for the main Machine Learning Reffined repository.

The aim of this project is to introduce new machine learning techniques into the field of open stope stability analysis: By integrating the classification model and genetic programming technique into a platform, a user-friendly graphical user interface (GUI) is created to provide decision-making references.

My attempt at doing exercises in Machine Learning Refined book

A refined version of machine learning procedures for educational and development purposes.

Beer Recommender Refined - using Kmeans clustering unsupervised machine learning, Python, Javascript, D3, Plotly

Using machine learning techniques, we developed and refined models to forecast car rental demand.

The Python repository containing all the refined machine learning projects crafted and optimized in Jupyter Notebooks.

Machine learning football match prediction completed by scraping data then creating predictors with the refined data.