https://www.instagram.com/p/CKac5Yihxtx/?igshid=13t66p3zki6k7

In the second part, we talk about
1- What are the different types of data
2- What is probability distribution
3- Types of Probability distribution
4- Definition of correlation and covariance

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The DataSpoof educational posts are going viral on Tumblr and Facebook as well.

http://dataspoof.tumblr.com

Ask your questions related to Data science, machine learning, deep learning, computer vision and career related questions. You will get your answers within 24hrs
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https://t.me/joinchat/VgOmi4uB9OImdbLw

Some good books for data science are

Best book on machine learning by Abhishek thakur

World first kaggle Grandmaster

Paperback version
https://amzn.to/3olDb9h

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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