ML Time

Improving the Aftermath Management of an Earthquake

Happy Valentine's Day from MATLAB ❤️ Code ⬇️

50+ Statistics Interview Questions and Answers for Data Scientists for 2021

DIY- Multi-Level Dendrogram

How to use ML to perform RF modulation recognition?

Tools for building robust, state-of-the-art machine learning models

English Audio Speech-to-Text Transcript with Hugging Face | Python NLP

Neural Re-rendering for Full-frame Video Stabilization

AI-ML Cheat Sheets

Q4 2020 Food Delivery & Rideshare Sales Report


Microservices Architecture at Netflix!

10 Awesome Data Science Courses to make you an Awesome Data Scientist

Yann LeCun’s Deep Learning Course

Analyzing seasonality with Fourier transforms using Python & SciPy

An open-source text annotation tool for humans

PORORO: Platform Of neuRal mOdels for natuRal language prOcessing

Indian Flag with Turtle using Python

This Republic day I tried to do something creative and made an Indian Flag with Turtle using Python. Turtle is a pre-installed Python library. It enables users to create pictures and shapes by providing them with a virtual canvas.

You can also refer to the YouTube video tutorial for better understanding:

GitHub Repo:

Blog link:

The Shiny AWS Book

10 Most Stable Linux Distros In 2021

Python’s Pandas vs. R’s dplyr

Top Ten Kaggle Notebooks For Data Science Enthusiasts In 2021

An R package to build your resume or CV

Applied Data Science: Free E-Book

"The Data Engineering Cookbook" by Andreas Kretz!!

Data Cleaning

Wind Forecast

Fundamentals of Python Programming

Mistakes to avoid as a Data Scientist

Fake AI generated blog

ML Interview Questions


1-Introduction to Time Series and Forecasting

2-Statistics and Analysis of Scientific Data

3-Linear Algebra Done Right

4-Linear Algebra


6-Understanding Analysis

7-Understanding Statistics Using R

8-An Introduction to Statistical Learning

9-Statistical Learning from a Regression Perspective


11-Regression Modeling Strategies

12-A Modern Introduction to Probability and Statistics

13-The Python Workbook

14-Machine Learning in Medicine — a Complete Overview

15-Introduction to Data Science

16-Applied Predictive Modeling

  1. Data Science Process

  2. Data Visualization in Business

  3. Know Machine Learning Key Terminology

  4. Understand Machine Learning Implementation

  5. Machine Learning Applications on Marketing and Retail

TOP 10 SQL Concepts for Job Interview

  1. Aggregate Functions (SUM/AVG)

  2. Group By and Order By

  3. JOINs (Inner/Left/Right)

  4. Union and Union All

  5. Date and Time processing

  6. String processing

  7. Window Functions (Partition by)

  8. Subquery

  9. View and Index

  10. Common Table Expression (CTE)

TOP 10 Statistics Concepts for Job Interview

  1. Sampling

  2. Experiments (A/B tests)

  3. Descriptive Statistics

  4. p-value

  5. Probability Distributions

  6. t-test

  7. ANOVA

  8. Correlation

  9. Linear Regression

  10. Logistics Regression

TOP 10 Python Concepts for Job Interview

  1. Reading data from file/table

  2. Writing data to file/table

  3. Data Types

  4. Function

  5. Data Preprocessing (numpy/pandas)

  6. Data Visualisation (Matplotlib/seaborn/bokeh)

  7. Machine Learning (sklearn)

  8. Deep Learning (Tensorflow/Keras/PyTorch)

  9. Distributed Processing (PySpark)

  10. Functional and Object Oriented Programming

Data Science Topics: CRISP – DM - Project Management Methodology Exploratory Data Analytics (EDA) / Descriptive Analytics Statistical Data Business Intelligence and Data Visualization Plots & Inferential Statistics Probability Distributions (Continuous & Discrete) Hypothesis Testing - The ‘4’ Must Know Hypothesis Tests Data Mining Supervised Learning – Linear Regression, OLS Predictive Modelling – Multiple Linear Regression Lasso and Ridge Regressions Logistic Regression – Binary Value Prediction, MLE Multinomial Regression Advanced Regression for Count Data Data Mining Unsupervised Learning - Clustering Data Mining Unsupervised Learning - Dimension Reduction (PCA) Data Mining Unsupervised Learning - Association Rules Recommendation Engine Network Analytics Machine Learning - k - NN Classifier Decision Tree & Random Forest Ensemble Techniques - Bagging and Boosting AdaBoost & Extreme Gradient Boosting Text Mining & Natural Language Processing (NLP) Machine Learning Classifier Technique - Naive Bayes Introduction to Perceptron, Multilayer Perceptron Building Blocks of Neural Network Deep Learning Black Box Technique - Neural Network Deep Learning Black Box Technique - SVM Survival Analytics Forecasting/Time Series – Model Driven Algorithms Forecasting/Time Series – Data Driven Algorithms

Machine Learning with Graphs, Leskovec

1-The Elements of Statistical Learning

2-Introductory Time Series with R

3-A Beginner’s Guide to R

4-Data Structures and Algorithms with Python

5-Introduction to Statistics and Data Analysis

6-Principles of Data Mining

7-Computer Vision

8-Data Mining

9-Robotics, Vision and Control

10-Statistical Analysis and Data Display

11-Statistics and Data Analysis for Financial Engineering

12-Stochastic Processes and Calculus

13Statistical Analysis of Clinical Data on a Pocket Calculator

14-Clinical Data Analysis on a Pocket Calculator

15-The Data Science Design Manual

16-An Introduction to Machine Learning

The Laplace Transform: A Generalized Fourier Transform

’Introduction to Statistics for Data Science, Exploratory Data Analysis in Python‘


Probability Mass Functions

Cumulative distribution functions

Modeling distributions

Probability density functions

Relationships between variables


Hypothesis testing

Linear least squares


Time series analysis

Survival analysis

Analytic methods

Credit: Allen B. Downey

XGBoost Tutorials: 1-Introduction to Boosted Trees 2-Distributed XGBoost with AWS YARN 3-Distributed XGBoost with XGBoost4J-Spark 4-DART booster 5-Monotonic Constraints 6-Random Forests in XGBoost 7-Feature Interaction Constraints 8-Text Input Format of DMatrix 9-Notes on Parameter Tuning 10-Using XGBoost External Memory Version (beta)

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