Top 10 Computer Vision Project Ideas

1. Edge Detection
2. Photo Sketching
3. Detecting Contours
4. Collage Mosaic Generator
5. Barcode and QR Code Scanner
6. Face Detection
7. Blur the Face
8. Image Segmentation
9. Human Counting with OpenCV
10. Colour Detection

Some helpful Data science projects for beginners

https://www.kaggle.com/c/house-prices-advanced-regression-techniques

https://www.kaggle.com/c/digit-recognizer

https://www.kaggle.com/c/titanic

Intermediate Level Data science Projects

Black Friday Data : https://www.kaggle.com/sdolezel/black-friday

Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones

Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset

Million Song Data : https://www.kaggle.com/c/msdchallenge

Census Income Data : https://www.kaggle.com/c/census-income/data

Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset

Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2

Text mining : https://www.kaggle.com/kanncaa1/applying-text-mining

Top Bayesian Algorithms and Methods:

- Naive Bayes.
- Averages one-dependence estimators.
- Bayesian belief networks.
- Gaussian naive Bayes.
- Multinomial naive Bayes.
- Bayesian networks.

An Artificial Neuron Network (ANN), popularly known as Neural Network is a computational model based on the structure and functions of biological neural networks. It is like an artificial human nervous system for receiving, processing, and transmitting information in terms of Computer Science.

Basically, there are 3 different layers in a neural network :

Input Layer (All the inputs are fed in the model through this layer)

Hidden Layers (There can be more than one hidden layers which are used for processing the inputs received from the input layers)

Output Layer (The data after processing is made available at the output layer)

Graph data can be used with a lot of learning tasks contain a lot rich relation data among elements. For example, modeling physics system, predicting protein interface, and classifying diseases require that a model learns from graph inputs. Graph reasoning models can also be used for learning from non-structural data like texts and images and reasoning on extracted structures.

Some helpful Data science projects for beginners

https://www.kaggle.com/c/house-prices-advanced-regression-techniques

https://www.kaggle.com/c/digit-recognizer

https://www.kaggle.com/c/titanic

BEST RESOURCES TO LEARN DATA SCIENCE AND MACHINE LEARNING FOR FREE

https://developers.google.com/machine-learning/crash-course

https://www.kaggle.com/learn/overview

https://forums.fast.ai/t/recommended-python-learning-resources/26888

https://www.fast.ai/

https://imp.i115008.net/JrBjZR

https://ern.li/OP/1qvkxbfaxqj

ENJOY LEARNING 👍👍

Just a correction here
"np.nan" instead of "na.nan"
Hope you all are aware that mostly we
import numpy as np

You should definitely check the documentation of numpy and scikit learn if you are facing difficulty in answering above questions
https://numpy.org/doc/stable/
https://scikit-learn.org/

Share the channel link with your friends to help them too in learning data science and machine learning
👇👇
http://t.me/datasciencefun

Learning and Practicing SQL: Resources and Platforms

1. https://sqlbolt.com/
2. https://sqlzoo.net/
3. https://www.codecademy.com/learn/learn-sql
4. https://www.w3schools.com/sql/
5. https://www.hackerrank.com/domains/sql
6. https://www.windowfunctions.com/
7. https://selectstarsql.com/
8. https://quip.com/2gwZArKuWk7W
9. https://leetcode.com/problemset/database/
10. http://thedatamonk.com/

What is gradient boosting trees?

Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.