Pdf edition

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

Read part 1 and part2 both for proper understanding.

https://www.instagram.com/p/CKlNw7zhQZ8/?igshid=9atp7jmt3v21

Like ❤ and comment. And save it for data science preparation.

Object detection using single shot detection implementation.


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

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 😊😊

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.

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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

How to perform image annotation.

https://medium.com/deepquestai/object-detection-training-preparing-your-custom-dataset-6248679f0d1d

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

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

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

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