Tutorials

In the following sections, we will more systematically introduce the following concepts:

📄️ Amazon Personalize

| Description | Notebook |

📄️ Data Loading

Load text files into pandas dataframe

📄️ Database Connections

Mongodb

📄️ Email Classification

Fetching data from MS-Sql

📄️ Google Cloud Big Data and Machine Learning Fundamentals

Exploring a BigQuery Public Dataset

📄️ Graph-based Modeling

General

📄️ Kubernetes

AWS EKS

📄️ Mathematics

Probability and Statistics

📄️ Matrix Factorizations

Neural Matrix Factorization (NMF)

📄️ MLOps

| Description | Notebook |

📄️ Model Optimization

Keras Model Pruning

📄️ Multi-Touch Attribution

Abstract

📄️ Name and Address Parsing

Recognising Person names and Addresses in a text using NER and NLP modeling techniques

📄️ Negative Implicit Feedback in Recommendations

A tutorial to demonstrate the process of training and evaluating various recommender models on a online retail store data. Along with the positive feedbacks like view, add-to-cart, we also have a negative event 'remove-from-cart'.

📄️ Outliers Handling

Remove categorical outliers

📄️ PDF to WordCloud via Email

Receive a pdf via outlook mail and send back the wordcloud of that pdf in the reply

📄️ Python code

Clean filenames in a folder

📄️ Recommender System Evaluation

| Description | Notebook |

📄️ Regression

Sklearn Regression Model

📄️ Resale Price Prediction

| | BRAND | PARTNO | QUANTITY | UNITRESALE | UNITCOST |

📄️ Text Cleaning

📄️ Text Embeddings

Utilities

📄️ Text Processing

Tokenization

📄️ Unix shell

Replace file extension of all files in a folder

📄️ Vector Search

Faiss

📄️ Word2vec

| Description | Notebook |