DataSpoof

The top 10 computer vision papers in 2020 with video demos, articles, code, and paper reference. https://www.kdnuggets.com/2021/01/top-10-computer-vision-papers-2020.html

The giveaway of Handwritten notes on machine learning is started. To participate in this giveaway. You have to like 5 recent post on Instagram. Link down below 👇👇👇 https://www.instagram.com/p/CLv6syIBvC9/?igshid=1hsa7kbso43vj

https://www.youtube.com/watch?v=Nn4S5V8d--Q Github unofficial cool features. I think it would be helpful for everyone.

Here is awesome collection of computer vision pre-trained models. https://github.com/balavenkatesh3322/CV-pretrained-model

Best of Machine Learning in 2019: Reddit Edition A look at 17 of the most popular projects, research papers, demos, and more from the subreddit r/MachineLearning over the past year https://heartbeat.fritz.ai/best-of-machine-learning-in-2019-reddit-edition-5fbb676a808

The best to learn how to deal with text data. What you will learn in this book Natural language processing Deep learning algorithms. How to deal with text data. Advance machine learning and deep learning techniques. https://amzn.to/3aECsw5

How to perform image annotation. https://medium.com/deepquestai/object-detection-training-preparing-your-custom-dataset-6248679f0d1d

Some of the intermediate lists projects Plant-Leaf-Classification-using-Swedish-Leaf-Dataset Weed Detection in Soybean Crops Sentiment analysis of memes Social-Media-News-Generation

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Deploying ML as part of an application requires a blend of creativity, strong engineering practices, and an analytical mindset. ML products are notoriously challenging to build because they require much more than simply training a model on a dataset. Choosing the right ML approach for a given feature, analyzing model errors and data quality issues, and validating model results to guarantee product quality are all challenging problems that are at the core of the ML building process.

Many Data Science aspirants struggle to find good projects to get a start in Data science or Machine Learning. Here is the list of few Data Science projects (found on kaggle), it covers Basics of Python, Advanced Statistics, Supervised Learning (Regression and Classification problems) 1. Basic python and statistics Pima Indians :- https://www.kaggle.com/uciml/pima-indians-diabetes-database Cardio Goodness fit :- https://www.kaggle.com/saurav9786/cardiogoodfitness Automobile :- https://www.kaggle.com/toramky/automobile-dataset 2. Advanced Statistics Game of Thrones:-https://www.kaggle.com/mylesoneill/game-of-thrones World University Ranking:-https://www.kaggle.com/mylesoneill/world-university-rankings IMDB Movie Dataset:- https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset 3. Supervised Learning a) Regression Problems How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data IMDB Box office Prediction:-https://www.kaggle.com/c/tmdb-box-office-prediction/overview b) Classification problems Employee Access challenge :- https://www.kaggle.com/c/amazon-employee-access-challenge/overview Titanic :- https://www.kaggle.com/c/titanic San Francisco crime:- https://www.kaggle.com/c/sf-crime Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction Trip type classification:- https://www.kaggle.com/c/walmart-recruiting-trip-type-classification Categorize cusine:- https://www.kaggle.com/c/whats-cooking These are the links of competitions, from there previous notebooks can be checked to begin with, Hope it will be helpful 😊😊

Object detection using single shot detection implementation. https://www.linkedin.com/posts/data-spoof_deep-learning-for-object-detection-a-comprehensive-activity-6761293280685699072-zAVh

https://www.instagram.com/p/CKlNw7zhQZ8/?igshid=9atp7jmt3v21 Like ❤ and comment. And save it for data science preparation.

Read part 1 and part2 both for proper understanding.

Questions Can you suggest any models/model ideas for working with financial time series. Answer- some of the model that are available FOR FINANCIAL TIME SERIES are 1- ARIMA 2- GARIMA 3- Facebook prophet There is a great blog on time series analysis https://www.dataspoof.info/post/time-series-analysis-in-python