Author Archives: 365Data Science

In this blog, I’ll share my experience and method to Build a Machine Learning Model for predicting Heart Disease. Dataset Used– https://www.kaggle.com/ronitf/heart-disease-uci About the dataset: So as you all can see this dataset contains 14 features. age: The person’s age in years sex: The person’s sex (1 = male, 0 = female) cp: The chest pain experienced (Value 1:Continue reading “Machine Learning Model For Predicting Heart Disease”

There is still a long way to go before machine agents match overall human gaming prowess, but Deepmind’s gaming research focus has shown a clear progression of substantial progress. Originally from KDnuggets https://ift.tt/2ZEknt7 source https://365datascience.weebly.com/the-best-data-science-blog-2020/deepminds-gaming-streak-the-rise-of-ai-dominance

Also: Python For Everybody: The Free eBook; Complex logic at breakneck speed: Try Julia for data science; An easy guide to choose the right Machine Learning algorithm; Dataset Splitting Best Practices in Python; Appropriately Handling Missing Values for Statistical Modelling and Prediction Originally from KDnuggets https://ift.tt/3dcoC3a source https://365datascience.weebly.com/the-best-data-science-blog-2020/kdnuggets-news-20n21-may-27-the-best-nlp-with-deep-learning-course-is-free-your-first-machine-learning-web-app

To help you truly rock your next virtual data interview, we’ve pulled together a few tips that we recommend when conducting our online interviews for The Data Incubator’s Data Science Fellowship Program. Originally from KDnuggets https://ift.tt/2Xxxycz source https://365datascience.weebly.com/the-best-data-science-blog-2020/how-to-rock-a-virtual-data-interview

If you are splitting your dataset into training and testing data you need to keep some things in mind. This discussion of 3 best practices to keep in mind when doing so includes demonstration of how to implement these particular considerations in Python. Originally from KDnuggets https://ift.tt/36HLWDv source https://365datascience.weebly.com/the-best-data-science-blog-2020/dataset-splitting-best-practices-in-python

In this article, I will try to predict the locations of deforestation in Amazon Forests using Ridge, Decision Tree, Random Forest and XGBoost regression models. Image was taken from geospatialworld.net Introduction Deforestation is a clearing, destroying, or removing trees due to farming, mostly cattle, logging, for materials and development, mining, and drilling. All of thisContinue reading “Can We Predict Deforestation in Amazon Forests with Machine Learning?”

An engineer’s work life mainly consist of research projects, therefore, an engineer is often in need of effective iterative research processes to guide a product redesign. Here are some tips. Source Challenges & Solutions Let’s assume that we’d like to develop a video streaming platform, here are basic challenges and some potential solutions. Challenge: Super broad scopeContinue reading “Solutions to Challenging Engineering Research”

Source We have probably all came across the saying or statement that Machine Learning and Artificial Intelligence are a big part of technological advancement and the future. Opportunities in Machine Learning and AI are definitely going to be big for the next decades since they are the main driver of emerging technologies. If you areContinue reading “How to get started as a Machine Learning Engineer.”

Dive into experimenting with machine learning techniques using this open-source collection of interactive demos built on multilayer perceptrons, convolutional neural networks, and recurrent neural networks. Each package consists of ready-to-try web browser interfaces and fully-developed notebooks for you to fine tune the training for better performance. Originally from KDnuggets https://ift.tt/3goUAej source https://365datascience.weebly.com/the-best-data-science-blog-2020/interactive-machine-learning-experiments

Microsoft is bringing the latest research in responsible AI to Azure (both Azure Machine Learning and their open source toolkits), to empower data scientists and developers to understand machine learning models, protect people and their data, and control the end-to-end machine learning process. Originally from KDnuggets https://ift.tt/3eiLlKU source https://365datascience.weebly.com/the-best-data-science-blog-2020/machine-fairness-how-to-assess-ai-systems-fairness-and-mitigate-any-observed-unfairness-issues