Andrea Perlato

This website has been designed to share information about Data Science.
There are two main topics: Machine Learning and Artificial Intelligence, both explained along with real world examples in R and Python. Moreover, there is a specific session dedicated to Time Series, and a specific topic about the Theory underlying the main algorithms.

Andrea Perlato

2020

Data Science Technical Video Interview 2020/04/05

Topic Model Video Interview 2020/04/04

Sequence Based Data Video Interview 2020/04/04

Semantic Analysis Video Interview 2020/04/04

Requirements to Deploy Video Interview 2020/04/04

R and Python Video Interview 2020/04/04

Natural Language Processing Video Interview 2020/04/04

Model Performance Video Interview 2020/04/04

Model Deployment Video Interview 2020/04/04

Machine Learning Pipeline Video Interview 2020/04/04

Machine Learning Algorithms Video Interview 2020/04/04

Learning Rate Video Interview 2020/04/04

Gradient Descent Video Interview 2020/04/04

Feature Selection Video Interview 2020/04/04

Feature Engineering Video Interview 2020/04/04

Cloud Services Video Interview 2020/04/04

Activation Function in Neural Nets 2020/01/09

Ensemble Learning with Gradient Boosting 2020/01/03

Ensemble Learning with Adaptive Boosting 2020/01/03

Detect Scientific Topics using NLP 2020/01/03

2019

Page Rank in Network Analysis 2019/12/18

The Learning Rate 2019/12/17

Exploratory Data Analysis 2019/12/17

Social Network Analysis on Romania Elections 2019/12/13

Recommender System of Scientific Paper 2019/12/13

NLP Glossary 2019/12/11

Predict Breast Cancer using TensorFlow 2019/12/02

Logistic Regression as a Neuron 2019/12/02

Softmax Regression 2019/11/26

Hyperparameters Tuning in AI 2019/11/26

Root Mean Square Propagation 2019/11/25

Learning Rate Decay and Local Optima 2019/11/25

Adaptive Momentum 2019/11/25

Vanishing Gradient 2019/11/22

Mini-batch Gradient Descent 2019/11/22

Gradient Descent with Momentum 2019/11/22

Gradient Checking 2019/11/22

Azure Databricks and RStudio 2019/11/20

NLP Step by Step 2019/11/14

Graphical Representatioin of Missing Data 2019/11/12

Bias Variance Trade-Off 2019/11/07

Find Credit Card Info from Ecommerce using Python 2019/11/05

Vectorization in Python 2019/10/18

Sankey Diagram with D3js 2019/10/18

Introduction to Support Vector Machine 2019/10/02

Extract the Main Topics from Books 2019/08/09

Introduction to Topic Model 2019/08/08

The Beauty of Animation 2019/08/07

Life Expectancy based on World Bank Indicators 2019/08/07

Predict Movie Sentiment via DOC2VEC 2019/08/06

Introduction to Word2Vec 2019/08/06

Introduction to Naive Bayes 2019/08/06

Text Mining Clustering Tweets 2019/08/02

Introduction to K-Means and Hierarchical clustering 2019/08/02

Identify Spam Emails 2019/08/02

Feature of Automobiles via Web App 2019/07/31

Auto Encoder 2019/07/29

Boltzmann Machine 2019/07/28

Principal Component Analysis 2019/07/26

Ensemble Learning 2019/07/26

Self Organizing Maps 2019/07/23

CNN and Softmax 2019/07/23

Simple Regression with Python 2019/07/19

Recurrent Neural Network in Theory 2019/07/19

The Activation Function 2019/06/20

Alpha Beta Pruning 2019/06/20

Ridge and Lasso Regression 2019/06/18

Introduction to Convolutional Neural Networks 2019/06/18

Fitting a curve to data LOWESS and LOESS 2019/06/18

Synthetic Minority Oversampling Technique 2019/06/17

Backpropagation Intuition 2019/06/17

Cluster Analysis in Theory 2019/06/12

Stochastic Gradient Descent 2019/06/11

How to create 3D and 4D plot 2019/06/07

How to compare data at different scales 2019/06/04

The Learning Process 2019/05/20

Survival Trees 2019/05/13

Parametric Regression Model in Survival Analysis 2019/05/13

Cox Proportional Hazards Model 2019/05/13

Survival Analysis: Kaplan-Meier & Logrank test 2019/04/30

Time Series Classification 2019/04/05

Time Series Clustering 2019/04/04

Linear Discriminant Analysis 2019/04/04

Interactive Forecasting 2019/04/03

Classification and Prediction with Support Vector Machine 2019/04/03

Random Forest Hyperparameters Tuning 2019/04/02

Feature Selection using Boruta Algorithm 2019/04/02

Principal Component Analysis 2019/04/01

Polynomial Regression & Smoothing Splines 2019/03/27

Interactive Dashboard 2019/03/27

Fuzzy Matching Addresses to Prevent Fraudulent Application 2019/03/25

Generalized Addictive Models GAMs 2019/03/22

Deal Outliers with Robust Regression 2019/03/21

Deal Multicollinearity with Ridge Regression 2019/03/21

Deal Multicollinearity with LASSO Regression 2019/03/21

Quantile Regression in Medical Expenditures 2019/03/20

Interactive Graphs 2019/03/15

Naive Bayes Classification 2019/03/14

Interactive Tables 2019/03/14

Extreme Gradient Boosting Algorithm 2019/03/13

Interactive Network 2019/02/28

Forecasting Product Demand 2019/02/28

Neural Nets and Interactive Graphs 2019/02/27

Applied Time Series and Forecasting 2019/02/27

Supply Chain Foundations 2019/02/25

Gradient Descent Step by Step 2019/02/13

Data Science using Agile Methodology 2019/02/11

Statistical Background for Time Series 2019/02/06

Distinguish Benign and Malign Tumor via ANN 2019/02/05

Event Processing 2019/01/23

Continuous Probability 2019/01/22

The Monty Hall Problem 2019/01/20

How many Monte Carlo are enough? 2019/01/20

Automotive Multivariate Visualization 2019/01/19

How Neural Network learn? An example of risk of churn. 2019/01/14

Customer segmentation via K-Means & Hierarchical clustering 2019/01/12

Assessing the sucess of a new product via multiple classifiers 2019/01/10