Interview Question

What is the difference between Data Mining and Machine Learning?

Machine Learning is a branch of Artificial Intelligence which aims at making systems learn automatically from the data provided and improve their learning over time without being explicitly programmed. Data Mining, on the other hand, focuses on analyzing the data and extracting knowledge and/or unknown interesting patterns from it. The goal is to understand the patterns in the data in order to explain some phenomenon and not to develop a sophisticated model which can predict the outcomes for the unknown/new data. For instance, you can use Data Mining on the existing data to understand your company’s sales trends and then build a Machine Learning Model to learn from that data, find the correlations and adapt for the new data.

Interview Question

When would you use standard Gradient Descent over Stochastic Gradient Descent, and vice-versa?

Standard Gradient Descent theoretically minimizes the error function better than Stochastic Gradient Descent. However, Stochastic Gradient Descent converges much faster once the dataset becomes large. Thus standard Gradient Descent is preferable for small datasets while Stochastic Gradient Descent is preferable for the larger ones.
In practice, however, Stochastic Gradient Descent is used for most of the applications because it minimizes the error function well enough while being much faster and more memory efficient for large datasets.

Don't have enough data to train your model? Fret not! Use the synthetic one!

👉🏼 Synthetic data is artificially generated data that is not collected from real world events! It replicates the statistical components of real data without containing any identifiable information, ensuring individuals' privacy.

🧠 Synthetic data can be used for many applications:
- Privacy
- Removing Bias
- Balancing Datasets
- Augment Datasets

👉🏼 Where to generate it from and how?

Open Source Project YData Synthetic: This repository contains material on GANs for synthetic data generation, especially regular tabular data and time-series. It consists a set of different GAN architectures developed using Tensorflow 2.0. An example Jupyter Notebook is included, to show how to use the different architectures.

Link: https://github.com/ydataai/ydata-synthetic

🌟 Star the repository to save it for future use or reference!

Harvard University has this Free course on Data Science : Machine Learning!

Link to enroll : http://bit.ly/2WtDPFZ

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

Like ❤ and share

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 😊😊