Index of Lessons in Technology

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Algorithms: Analysis And Design

From Codility

Course Link: By James Cutajar
  1. Binary Gap (Problem of Iterations)
  2. Arrays
  3. Time Complexity
  4. Counting Elements
  5. Prefix Sums
  6. Sorting
  7. Stacks and Queues
  8. Leader using Boyer-Moore Majority Vote Algo
  9. Maximum Slice Problem (using Kadane's Algo leveraging Dynamic Programming)
  10. Recursion (An Introduction)
  11. Substring in another string (Lesson in Algorithms)

Anaconda

Android Development

Artificial Intelligence

Cloud

Cyber Security

CS3: Torrent, Magnet Links and Tor Browser

  1. Want a virus, here's one (Ref # Microsoft Security Intelligence)
  2. Tor Browser, Anonymity and Your IP Address
  3. Censorship in India, Torrenting and Tor Browser
  4. Censorship in India, Torrenting and Tribler (Aug 2021)
  5. Before you continue to YouTube, Google uses cookies and data to... [Jul 2021] [Tags: Cyber Security, Web Development, Web Scraping]
  6. Google Sites, Tor Exit Nodes and Captcha
  7. SaveFrom to exit from USA (Jan 2022)

CS4: Potentially Unwanted Software

  1. Want a virus, here's one (uTorrent -- Ref # Microsoft Security Intelligence)
  2. Potentially Unwanted Application detected by MS Defender - JDownloader
  3. Testing Anti-virus Functionality of Google Drive (2022-May-20) [Tags: Cloud, Cyber Security]

Databases

D1: Excel

  1. Getting Started with Spreadsheet (MS Excel and Google Sheets)
  2. Simple app demonstrating VBA, SQL like Querying and Python Package XLWings in MS Excel
  3. Taking table data from Excel to SQL based database

D2: SQL

  1. Basics of Database Management System (by Yaju Gupta)
  2. Basics of RDBMS (Relational Database Management System)
  3. Create Table Statement and Constraints (Microsoft SQL Server and Oracle DB)
  4. Simple application demonstrating Spring Rest, Spring Data JPA and H2 database in Spring Boot [Tags: Databases, Web Development]
  5. Creating an OLAP Star Schema using Materialized View (Oracle DB 10g)
  6. MySQL Installation, Queries, ODBC Setup and Views in Alteryx
  7. Hour One With SQLite Database

D3: PostgreSQL

  1. Pentaho and PostgreSQL Installation on Ubuntu (2021 Oct)
  2. PostgreSQL (15.1) Installation on Ubuntu (2022 Dec 13)
  3. Installing PostGRE 14 Database on Windows 10
  4. Setting up PostGRE SQL Database on Windows 10
  5. Loading data from Pandas to PostgreSQL [Tags: Databases, Machine Learning]
  6. Accessing PostgreSQL from PySpark.Pandas

D4: MongoDB

  1. MongoDB and Node.js Installation on Ubuntu (Oct 2022)
  2. Basic Q-and-A around MongoDB
  3. Demo of file operations in GridFS and MongoDB using Python
  4. Creating a dummy database and collection in MongoDB Cloud and reading the dummy document using PyMongo [Tags: Cloud, Databases, Python]
  5. Node.js and MongoDB Solved Interview Problem (Oct 2022)
    I'm making 2 queries (or a single query) to the database. What I want to achieve: If one of them is null, I want to add the value 'nil'. Example: field1: nil, field2: 'value'. If both are null, then I want it to respond with the 'not found' message. What's a good approach for this?

D5: Not RDBMS

  1. Getting started with Neo4j - The Graph Database
  2. Basic Q&A around graph-databases
  3. Setting up Windows 10 for Android development using Flutter, Firebase database and Firebase Cloud Messaging [Tags: Databases, Flutter]

D6: Our Database Videos on YouTube

  1. Basics of DBMS 20211225
  2. SQL (Structured Query Language) Hour 1 (2022-Feb-05)
  3. Create Table Statement and Constraints (2022-Feb-06)
  4. SQL Operations (as, union, like, inner join, where, and, count, get, select) [2022-Feb-7]

Databricks (Big Data and Machine Learning)

Deep Learning

Flask

Free and Open-Source Software

GitHub

Hadoop / Spark

HS1: Installation of Hadoop

  1. Getting started with Hadoop on Ubuntu in VirtualBox
  2. Setting up three node Hadoop cluster on Ubuntu using VirtualBox

HS2: Installation of Spark

  1. Getting started with Spark on Ubuntu in VirtualBox
  2. Setting up a three node Spark cluster on Ubuntu using VirtualBox (Apr 2020)
  3. Basic Q-and-A surrounding Spark technology
  4. Notes on setting up Spark with YARN three node cluster
  5. Creating Two Node Spark Cluster With Two Worker Nodes and One Master Node Using Spark's Standalone Resource Manager on Ubuntu machines (2022 Oct 23)
  6. Installing Spark on Windows (Nov 2022)

HS3: Installation of PySpark

  1. Installing PySpark on Ubuntu And Basic Testing (2022 Oct 8)
  2. Installing PySpark on Ubuntu And Basic Testing (2022 Dec 13)

HS4: Installation Failures

  1. Spark installation on 3 RHEL based nodes cluster (Issue Resolution in Apr 2020)
  2. Beginner issues while working with Hadoop and Spark (May 2020)
  3. Spark Installation on Windows (2022-Oct-07, Status Failure, Part 1)
  4. Spark Installation on Windows (2022-Oct-07, Status Failure, Part 2)

HS5: Applications of spark-submit

  1. Launching a PySpark program on (Spark + YARN) cluster using "spark-submit"
  2. spark-submit For Two Node Spark Cluster With Spark's Standalone RM For Pi Computation (2022 Oct 23)

HS6: Distributed Deep Learning

  1. Installation of Elephas (for distributed deep learning) on Ubuntu through archives (Apr 2020) [Tags: Hadoop / Spark, Machine Learning]
  2. Distributed Deep Learning Using Python Packages Elephas, Keras, Tensorflow and PySpark [Tags: Anomaly Detection, Deep Learning, Spark]

HS7: Resilient Distributed Dataset (RDD)

  1. What is an RDD in PySpark?
  2. Demonstrating count(), take() and collect() for printing contents of a PySpark RDD
  3. Transformations in PySpark, two types of transformations and an example of flatMap method
  4. PySpark's reduceByKey Method, Word count and Line count Programs
  5. Counting and filtering blank lines from input file in PySpark
  6. A Solved Exercise in RDD Filter and Join Operations (Interview Preparation)
  7. Broadcasting variables to nodes to reduce data shuffling from RDD joins
  8. 5 Questions on PySpark Technology

HS8: Ways in which Pandas API on PySpark differs from Plain Pandas

  1. Way 1: In Reading null and NA values
  2. Way 2: Difference in how access to str representation is provided
  3. Way 3: How isin() works for Plain Pandas and how we have to use to_numpy() for it in PySpark's Pandas API
  4. Way 4: With respect to DataFrame.replace() Method
  5. Way 5: WRT Cell Value Replacement Through Assignment

HS9: Basic tasks done using all the three: RDD, SQL DataFrame, and PySpark.Pandas DataFrame

  1. Four Ways to Read a CSV in PySpark (v3.3.0)
  2. Printing contents of RDD, SQL DataFrame, and PySpark.Pandas DataFrame
  3. Data Filtering in PySpark Using RDD, SQL DataFrame and Pandas On PySpark
  4. Grouping Data using RDD, SQL DataFrame and PySpark.Pandas
  5. Joins in PySpark using RDD, SQL DataFrame and PySpark.Pandas

HS10: Use Cases

  1. Big Data Analytics & Business (By Yajuvendra Gupta)
  2. Working with HDFS (ls, mkdir, copyFromLocal, rm)
  3. kNN classification parallelized using MapReduce [Tags: Hadoop / Spark, Machine Learning]
  4. Demostrating PySpark's mapPartitions() using Pi calculation and Avg calculation
  5. Spark SQL in Images
  6. Web Scraping using PySpark (standalone mode) and BeautifulSoup [Tags: BeautifulSoup, Spark]
  7. Web Scraping using PySpark (with 3 nodes) and BeautifulSoup [Tags: BeautifulSoup, Spark]
  8. Comparing StringIndexer (PySpark), LabelEncoder (skLearn), OrdinalEncoder (skLearn), OrdinalEncoder (category_encoders) [Tags: Machine Learning, Spark]
  9. Effect of PySpark's StringIndexer on clustering of data [Tags: Machine Learning, Spark]
  10. Importance of posing right question for machine learning, data analysis and data preprocessing [Tags: Machine Learning, Spark]

Large Language Models

Machine Learning

ML1: Category Encoding

  1. Installing 'Category Encoders' Python Package Using Pip And Conda
  2. Category Encoders Analysis (in Python)
  3. One Hot Encoding Using Pandas' get_dummies() Method on Titanic Dataset
  4. Do we need all the one hot features?
  5. One Hot Encoding from PySpark, Pandas, Category Encoders and skLearn
  6. Comparing StringIndexer (PySpark), LabelEncoder (skLearn), OrdinalEncoder (skLearn), OrdinalEncoder (category_encoders) [Tags: Machine Learning, Spark]
  7. Effect of PySpark's StringIndexer on clustering of data [Tags: Machine Learning, Spark]
  8. Two ways to get Frequency Based Order of Categorical Data (Python)

ML2: Data Preprocessing

  1. Binning (of a column or 1D Numerical Data)
  2. Feature Scaling in Machine Learning (when to use which among MinMaxScaler and StandardScaler)
  3. Similarity and Dissimilarity for Numerical (interval-scaled) variables, Asymmetric binary variables, Categorical variables, For text
  4. Correlation between continuous-numeric columns, and between categorical columns
  5. Importance of posing right question for machine learning, data analysis and data preprocessing [Tags: Machine Learning, Spark]
  6. Working with skLearn's MinMax scaler and defining our own
  7. Data Preprocessing Using Python package Pandas (Use case: Creating a Nifty50 SIP Simulator)
  8. Loading data from Pandas to PostgreSQL [Tags: Databases, Machine Learning]
  9. Exploring skLearn's CountVectorizer
  10. Using Snorkel to create test data and classifying using Scikit-Learn
  11. Pandas DataFrame Filtering Using eval()
  12. Three Types of Input Data Format For Apriori Algorithm (Association Analysis)
  13. Bagging in overcoming variance of a classifier, clustering algorithm or regressor

ML3: Data visualization

ML3.1: Misc

  1. Data Visualization's Basic Theory
  2. Box Plot and Anomaly Detection in 1D [Tags: Data Visualization, Anomaly Detection]
  3. Social Analysis (SOAN using Python 3) Report [Tags: Data Visualization, NLP]
  4. Plotting Correlation Matrix in Three Ways Using Pandas, Matplotlib and Seaborn
  5. Creating Heatmap from Pandas DataFrame correlation matrix
  6. Binomial Probability Distribution (visualization using Seaborn) [Tags: Machine Learning, NumPy]
  7. Google Analytics for Beginners (Assessments Dump, Oct 2020)
  8. survival8 Audience Around The World (Jun 2021)
  9. Topic modeling using Latent Dirichlet Allocation from sklearn and visualization using pyLDAvis
    [Tags: Data Visualization, Natural Language Processing]

ML3.2: Using PowerBI

  1. PowerBI's HTML Content Visualization [Tags: Data Visualization, PowerBI]
  2. Sorting an 'HTML Content' Visual in PowerBI [Tags: Data Visualization, PowerBI]
  3. Concatenate Two Tables using R in PowerBI [Tags: Data Visualization, PowerBI, R Language]
  4. Timeline View Using HTML Content Visual in PowerBI [Tags: Data Visualization, PowerBI, Python]

ML3.3: Line Chart

  1. Creating and Editing Line Chart in LibreOffice Calc [Tags: Data Visualization, FOSS]
  2. Line plot with multiple lines for page views for survival8 (Sep 2022)

ML3.4: Pie Plot

  1. Stratified sampling and fixed size sampling plus visualization using pie plot (Nov 2022)
  2. Plotting changes in Nifty50's top-5 sectors after last three market crashes Using Pie Plot, Bar Chart and Grouped Bar Chart

ML3.5: Choropleth and Cartography

Choropleth: A choropleth map is a type of statistical thematic map that uses pseudocolor, i.e., color corresponding with an aggregate summary of a geographic characteristic within spatial enumeration units, such as population density or per-capita income. 
Cartography: the science or practice of drawing maps.

  1. Drawing a world heat map using 'cartopy'
  2. Ownership of Bicycle, 2-wheeler and car in India by percentages (2022)
  3. Global Footprint of Survival8 (Nov 2022)

ML3.6: Histogram

  1. Differences between 'bar graph' and histogram
  2. Histogram report and binning on Sales data
  3. An exercise in visualization (plotting line plot and multicolored histogram with -ve and +ve values) using Pandas and Matplotlib

ML4: Outlier Detection / Anomaly Detection

  1. Box Plot and Anomaly Detection in 1D [Tags: Data Visualization, Anomaly Detection]
  2. DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles (Research paper, 2018)
  3. Unsupervised Outlier Detection Using PyOD
  4. Isolation based anomaly detection using iForest (2133360.2133363 / Research Paper)
  5. Density-based algorithm for anomaly detection (Adeel Hashmi / Research Paper)
  6. Isolation Forest Implementation using skLearn, PyOD, and spark-iForest
  7. Anomaly Detection using Scikit-Learn and "eif" PyPI package (for Extended Isolation Forest)
  8. Distributed Deep Learning Using Python Packages Elephas, Keras, Tensorflow and PySpark [Tags: Anomaly Detection, Deep Learning, Spark]
  9. Anomalies in 'survival8' Viewers' Stats (Mar 2022)

ML5: Classification

ML5.1: Decision Tree

  1. Decision Tree Learning
  2. Interpretation of Decision Tree J48 Classifier output in Weka [Tags: FOSS, Machine Learning, Weka]
  3. Calculations for Info Gain and Gini Coefficient for Building Decision Tree

ML5.2: Miscellaneous of Classification

  1. Creating ML model, saving it, and creating Flask API [Tags: Flask, Machine Learning, Classification]
  2. kNN classification parallelized using MapReduce [Tags: Hadoop / Spark, Machine Learning]
  3. Elbow Method for identifying k in kMeans (clustering) and kNN (classification)
  4. Snorkel's Analysis Package Overview (v0.9.6, Sep 2020). This dicusses how to interpret classification results
  5. Improving a Classifier (ML) Using Snorkel's Slicing Technique
  6. Multi-label Classification using Python
  7. Naïve Bayes Classifier for Spam Filtering
  8. Weka classification experiment on Iris dataset [Tags: FOSS, Machine Learning, Weka]

ML6: Clustering

  1. Elbow Method for identifying k in kMeans (clustering) and kNN (classification)
  2. Weka clustering experiment on Iris dataset [Tags: FOSS, Machine Learning, Weka]
  3. 'Supervised classification-oriented measures' for Cluster Analysis
  4. Similarity-oriented measures for cluster analysis

ML7: Regression

  1. Linear Regression (Theory)
  2. Improvements over OLS (Forward Stepwise, Ridge, Lasso and LARS forms of Regression)
  3. Descriptive Statistics and Linear Regression Using 'statistics' module and 'statsmodels' module
  4. Hands-on 5 Regression Algorithms Using Scikit-Learn
  5. Demo of Linear Regression on Boston Housing Data Using Weka [Tags: FOSS, Machine Learning]
  6. Saving Model, Loading Model and Making Predictions for Linear Regression (in Weka)
  7. Linear Regression Using Java Code And Weka JAR [Tags: FOSS, Java, Machine Learning, Weka]

ML8: Association Mining Between Attributes

  1. Three Types of Input Data Format For Apriori Algorithm (Association Analysis)
  2. The Concept of Lift in Association Rules Mining
  3. Apriori Algorithm For Association Mining Using Weka's Supermarket Dataset
  4. Running Weka's Apriori on 9_TXN_5_ITEMS Dataset
  5. Interpretation of output from Weka for Apriori Algorithm

ML9: Weka Tool

  1. Demo of Linear Regression on Boston Housing Data Using Weka [Tags: FOSS, Machine Learning, Weka]
  2. Interpretation of Decision Tree J48 Classifier output in Weka [Tags: FOSS, Machine Learning, Weka]
  3. Weka classification experiment on Iris dataset [Tags: FOSS, Machine Learning, Weka]
  4. Weka clustering experiment on Iris dataset [Tags: FOSS, Machine Learning, Weka]
  5. Demo of Linear Regression on Boston Housing Data Using Weka [Tags: FOSS, Machine Learning]
  6. Saving Model, Loading Model and Making Predictions for Linear Regression (in Weka)
  7. Linear Regression Using Java Code And Weka JAR [Tags: FOSS, Java, Machine Learning, Weka]
  8. Apriori Algorithm For Association Mining Using Weka's Supermarket Dataset
  9. Running Weka's Apriori on 9_TXN_5_ITEMS Dataset
  10. Interpretation of output from Weka for Apriori Algorithm
  11. Machine Learning and Weka Interview (5 Questions) [Tags: Machine Learning Q&A, Weka Tool]

ML10: Traffic Prediction on my Blog

  1. Traffic Prediction on my Blog (Oct 2023)
  2. When not to use Poisson Distribution for prediction?
  3. Time Series Analysis and Forecasting Using Exponential Moving Average (A use case of traffic prediction on my blog)

ML11: Questions and Answers

  1. Machine Learning dose with ten Q&A (Set 1)
  2. Machine Learning Q&A (Set 2)
  3. Machine Learning Q&A (Set 3)
  4. LinkedIn Machine Learning Assessment Dump (Aug 2021)
  5. Machine Learning and Weka Interview (5 Questions) [Tags: Machine Learning Q&A, Weka Tool]
  6. ARIMA forecast for timeseries is one step ahead. Why? (Solved Interview Problem)

ML12: Miscellaneous

  1. Simple demonstration of how important data is for machine learning
  2. Reading a JSON file from the Google Drive in the Google Colab
  3. A case of cyclic dependencies between PyPI packages [Tags: Machine Learning, Python]
  4. Extracting Information From Search Engines [Tags: Machine Learning, Python]
  5. Digging deeper into your toolbox (Viewing LDiA code of sklearn)
    [Tags: FOSS, Machine Learning, Natural Language Processing]

ML13: Articles

  1. Machine Learning Resources (Dec 2019)
  2. Machine Learning Evolution (Jan 2020)
  3. Data Science Timeline (Aug 2020)

ML14: Our 'Machine Learning' Videos on YouTube

  1. Session 1 - Linear Regression (OLS Method and Theory) - 20210716
  2. Session 2 - Improvements over Linear Regression method of OLS (Forward Stepwise, Ridge, Lasso, LARS)
  3. Linear Regression Theory (2022-02-15)
  4. Pandas and Linear Regression in Code (Dated: 2022-Feb-16)
  5. Naive Bayes Classifier / Application: Spam Filtering / Dated: 2022 Feb 17
  6. Decision Trees Learning (2022 Feb 22)
  7. Perceptron in Machine Learning (24 Apr 2022)

Mathematical Foundations for Data Science

NumPy

Natural Language Processing

NLP1: Extracting Text

  1. Convert MS Word files into PDF format using Python on Windows
  2. Extracting Text from Docx, Doc and Pdf files Using Python
  3. Getting Wikipedia data as text [Tags: Python, Machine Learning]

NLP2: Word Embeddings

NLP3: Named Entity Recognition

  1. Python code to create Annotations required by a custom SpaCy NER
  2. Creating custom NER using SpaCy for Programming Languages

NLP4: Chatbot Development Using Python

  1. Chatbot Examples in Use in Different Business Domains
  2. Hello World Chatbot Using Rasa
  3. Getting utterances for FAQ type chatbot using Edge, Selenium and Quillbot webapp [Tags: NLP, Web Scraping]
  4. Session 1 on 'Understanding, Analyzing and Generating Text' wrt a Chatbot
  5. The RulePolicy of Rasa (Oct 2022)

NLP5: Sentiment Analysis

  1. 6 Labeled Datasets For Sentiment Analysis
  2. VADER: Rule Based Approach to Sentiment Analysis
  3. Sentiment Analysis Using RNN with BOW indexing of words
  4. Sentiment Analysis using BERT, DistilBERT and ALBERT
  5. Sentiment Analysis Testing on Some Difficult Sentences

NLP6: Topic Modeling

  1. Latent Dirichlet Allocation (Algorithm for Topic Modeling of Text Data)
  2. Topic Modeling for BBC News Articles (Labeled Data)
    Part 1: Using Latent Dirichlet Allocation for topic modeling
  3. Part 2: Using POS Tagging as word filter with Latent Dirichlet Allocation
  4. Part 3: Topic modeling using Latent Dirichlet Allocation from sklearn and visualization using pyLDAvis
    [Tags: Data Visualization, Natural Language Processing]
  5. Digging deeper into your toolbox (Viewing LDiA code of sklearn)
    [Tags: FOSS, Machine Learning, Natural Language Processing]

NLP7: Taxonomy Generation for English Language News Articles

Creating a Taxonomy for BBC News Articles
  1. Part 1: Baseline Attempt
  2. Part 2: Cheating a little bit by looking at the data set.
  3. Part 3: Seeing why there many-to-many mapping between Cluster Labels and Article Categories.
  4. Part 4: Referring Research Papers
  5. Part 5 based on research paper using Cosine Distance: A Hybrid Approach to Hypernym Discovery
  6. Part 6 based on: A Hybrid Approach to Hypernym Discovery
    In Part 5, we were using Cosine Distance between input text and output label.
    In Part 6, we are first finding the dot product and then getting a probability using the sigmoid function similar to Logistic Regression.
  7. Part 7: Labeled Data for Taxonomy of News Articles from IPTC

NLP8: Social Analysis (SOAN) based on Whatsapp data

  1. Social Analysis (SOAN) based on Whatsapp data (Project Setup)
    Dated: October 5, 2022
  2. Social Analysis (SOAN) of WhatsApp Chats (an NLP and Pandas application)
    Dated: Jan 2020
  3. Social Analysis (SOAN using Python 3) Report [Tags: Data Visualization, NLP]
    Dated: 21 Apr 2020

NLP9: Bot detection on Twitter (Jul 2022)

  1. Using Sentiment to Detect Bots on Twitter Are Humans more Opinionated than Bots (Dickerson, Jul 2022)
  2. Classification of Twitter Accounts into Automated Agents and Human Users (Zafar Gilani, Jul 2022)
  3. Accessing Twitter API From Two Systems. One With Firewall and Second Without Firewall [Tags: Cyber Security, Natural Language Processing]
  4. Using Twitter API to fetch trending topics, tweets and users posting them
  5. Baseline Model For Bot Detection on Twitter Using VADER and RandomForestClassifier
  6. BERT Embedding Based Model For Bot Detection on Twitter (Sep 2022)

NLP10: Machine Translation

  1. Environment Setup For Machine Translation (from Hinglish to English)
  2. Hinglish to English Machine Translation Using Transformers

NLP11: Text Summarization

  1. Text Summarization (Project Setup)
  2. Summary of Biography of Elon Musk (Fixing Mistakes of Pegasus using ChatGPT)

NLP12: Other Projects

  1. Recociling two documents with transactional data (Automated Account Reconciliation Method. Patent: Chew US8639596)
  2. A Dynamic Strategy Coach for Effective Negotiation (a Natural Language Processing application) 2019-Sep-30

NLP14: Activities That Are Common to NLP Pipelines

  1. Normalizing your vocabulary (lexicon) for NLP application
  2. Stop Words Removal (NLTK, SpaCy, Gensim)
  3. Lemmatization using NLTK's WordNetLemmatizer

NLP15: Questions

  1. Infosys Certified Natural Language Processing Professional (Cheat Sheet / Jun 2022)
  2. Natural Language Processing Questions and Answers (Set 1 of 11 Ques)
  3. Natural Language Processing Questions and Answers (Set 2 of 8 Ques)
  4. NLP Questions and Answers (Set 3 of 6 Questions)
  5. Natural Language Processing Questions and Answers (Set 4 of 7 Questions)
  6. Infosys Certified Expert - Natural Language Processing Professional (Jul 2022)
  7. Machine Translation (NLP) Quiz of 12 Questions

NLP16: Misc

  1. Natural Language Toolkit (NLTK) - Highlights (Book by Steven Bird) [Tags: Natural Language Processing, Python]
  2. Characteristics of NLTK, SpaCy, CoreNLP and Spark NLP
  3. There are literally infinite number of words! (Heap's Law in Text Mining)
  4. Using Snorkel, SpaCy to augment text data
  5. Text to Speech APIs in different browsers for Ubuntu 21.10 [Tags: FOSS, Natural Language Processing, JavaScript]
  6. Python Packages Useful For Natural Language Processing (Oct 2022) [Tags: Natural Language Processing, Technology Listing]

NLP17: Our NLP Videos on YouTube

  1. Session 1 on 'Understanding, Analyzing and Generating Text'
  2. Session 2 - Normalizing Vocabulary and Intro to Sentiment Analysis package VADER
  3. Session 3 - Document Parsing, Document based Embeddings and Word Embeddings
  4. Session 4 - Math With Words (2022 Jan 08)

Python

P1: TensorFlow Installation

  1. Setting Up TensorFlow Using Archives
  2. Installing TensorFlow and setting up the kernel for Jupyter Notebook
  3. TensorFlow 2.1.0 Installation (Issues and Fixes)
  4. Installing TensorFlow 2.0.1 on Windows 10 in Anaconda Prompt
  5. Failing Installation of TensorFlow (2.3.0-cp37) on Windows 10 in Anaconda Prompt
  6. Differences between Conda and Pip installation. Installing TensorFlow 2.1.0 using Conda on Windows 10 And 'Hello World' program.
  7. Using Conda to install and manage packages through YAML file and installing kernel

P2: Rasa Chatbot API Installation

  1. Installing Rasa and Spacy (alongside Anaconda)
  2. Installing RASA using YML File in Anaconda [Tags: Python, Natural Language Processing]
  3. % Issues in RASA CLI installation in Conda Env using YML file (and workarounds) [Tags: Python, Natural Language Processing]
    % With Less Logs
  4. Installing Rasa 3.2.8 on Ubuntu using Conda

P3: Python Shell (or CLI)

  1. Removing .pyc files from under current directory (Python CLI)
  2. Collecting wheel (.WHL) files from the Pip Cache using Python CLI
  3. Python CLI to copy contents of all the files in the current directory
  4. Converting image to text, saving to disk, reading text from disk and displaying image
  5. Working with log files in Bash shell [Tags: Bash Shell, Python]

P4: Package Installation and Environment Management

  1. Pip, what is that?
  2. Python Package for Outlier Detection PyOD Setup (Apr 2020)
  3. A case of cyclic dependencies between PyPI packages [Tags: 'Machine Learning', Python]
  4. Compare pip and conda installations [Tags: 'Anaconda', 'Python Package Installation and Environment Management']
  5. pip's dependency resolver and dependency conflicts, also logs from pipdeptree
  6. Steps toward: virtualenv, jupyter notebook, jupyterlab, ipykernel, SpaCy model (en_core_web)
  8. Creating environment and kernel for sentence_transformers

P5: Live Python Sessions

  1. Python (1) (virtualenv and condaenv) [20210516]
  2. Python (2) String and related packages [20210523]
  3. Python (3) RegEx in Python [20210606]
  4. Python (4) Exception Handling [20210613]

P6: Solved Interview Problems on Python

  1. Python Quiz (13 Questions, May 2023)
  2. Compare two dictionaries in Python
  3. Check in Python if a key is present in a JSON object
  4. Python Interview Questions (2022 Oct, Week 3)

P7: Miscellaneous

  1. Python - 10 Tips and Tricks (Set 1)
  2. Python - Tips and Tricks (Set 2)
  3. Creating a Windows service using Python, PyInstaller and NSSM
  4. Getting Wikipedia data as text [Tags: 'Python', 'Machine Learning']
  5. Set up Google OAuth 2.0 Authentication For Blogger and Retrieve Blog Posts via Blogger API (v3) [Tags: Cloud, Python, Web Development]
  6. Extracting Information From Search Engines [Tags: Machine Learning, Python]
  7. Working with compiled Python code (.pyc file)
  8. Sending emails using Python
  9. Creating a dummy database and collection in MongoDB Cloud and reading the dummy document using PyMongo [Tags: Cloud, Databases, Python]

P8: Our Python Videos on YouTube

  1. Session 1 - Python: (virtualenv and condaenv) [20210516]
  2. Session 2 - GitHub Intro [20210523]
  3. Session 3 - Conda Environment, String and List [20210530]
  4. Session 4 - RegEx in Python [20210605]
  5. Session 5 - Part 1/2 - Exception Handling in Python [20210613]
  6. Session 5 - Part 2 of 2 - Exception Handling in Python [20210613]
  7. Session 6: Inheritance in Python (OOP Part 1) 20210908
  8. Session 7: NumPy Part 1 (Array Multiplication and random package) - 2021-Sep-20
  9. Session 8: Testing in Python

Snorkel (Weak Supervision Based ML)

Testing Automation Using Selenium (Python)

Technology Listings

Linux / Ubuntu / VirtualBox / RHEL / Termux

Web Development

WD1: Web Development (JavaScript)

  1. JavaScript Intro (Dev Console, Data Types and Operators)
  2. Browser detection in JavaScript
  3. 12 'ECMAScript 6' Tips (Sep 2020)
  4. Web Security: Prevent Website from opening in an IFrame
  5. Debugger statement in JavaScript
  6. 'Microsoft Visual Studio Code' Shortcuts
  7. Before you continue to YouTube, Google uses cookies and data to... [Jul 2021] [Tags: Cyber Security, Web Development, Web Scraping]
  8. English Questionnaire Application (Level Earth, Hour 4)
  9. AngularJS Saying Goodbye [Tags: FOSS, JavaScript, Web Development]
  10. Text to Speech using JavaScript in Firefox and Chrome (Feb 2022)
  11. No voice in Firefox on Ubuntu 2022-Feb-23 [Tags: FOSS, JavaScript, Ubuntu]
  12. Text to Speech APIs in different browsers for Ubuntu 21.10 [Tags: FOSS, Natural Language Processing, JavaScript]
  13. Ad-Serving Limit Applied Second Time (Mar 2022) [Tags: Web Development, Web Scraping]
  14. JavaScript RegEx Solved Interview Problem (Oct 2022)
  15. 20 Quiz Questions For JavaScript Beginners with Easy Difficulty (Oct 2023)
  16. Learning JavaScript as a Second Language

WD2: Web Development (using Java)

  1. iMAS 101 (XML (and Java) based framework for web-development)
  2. Hello World Application on JBoss
  3. Hello World Application in Spring MVC framework Using Technologies Maven (build tool), Eclipse (IDE) and JBoss (server)
  4. Debugging a Spring MVC application in standalone JBoss using Eclipse IDE
  5. Spring Framework - Inversion Of Control - Basic Example
  6. Hello World application demonstrating REST API and Spring Boot
  7. Spring Boot vs Spring MVC vs Spring
  8. Simple application demonstrating Spring Rest, Spring Data JPA and H2 database in Spring Boot [Tags: Databases, Web Development]
  9. Download JAR (Java Archives) files
  10. Omocron Webportal Setup (Jan 2020)

WD3: Web Development (Python)

  1. Set up Google OAuth 2.0 Authentication For Blogger and Retrieve Blog Posts via Blogger API (v3) [Tags: Cloud, Python, Web Development]
  2. HTTP Error Codes and REST APIs [Tags: Python, Web Development, Web Scraping]
  3. URL Redirection:
    What is this l.facebook.com in Blogger Top Referrers (2022-May-23) [Tags: Cyber Security, Web Development]

WD4: Web Development (KnockoutJS)

  1. Knockout based Counter Version 1
  2. Knockout based Counter Version 2
  3. Knockout based Counter Version 3
  4. Knockout based Counter Version 4 (Erroneous)
  5. Knockout based Counter Version 5 (Erroneous)

WD5: HTML

  1. LinkedIn's HTML Assessment Dump (Mar 2023)

Web Scraping

WS1: Scrapy

  1. Getting Started with Scrapy
  2. Creation of new directories and files by commands 'scrapy startproject' and 'scrapy genspider'
  3. Creating a Flask API for the Scrapy and dumping the parsed content into a JSON file
  4. Scrapy (Python package for web scraping) Q&A (Dec 2019)
  5. Recursive scraping with depth two using Scrapy
  6. Scrapy spiders for getting data about programming languages from Wikipedia
  7. Creating a Flask API for Scrapy that creates custom Spiders for every request
  8. Scrapy spider to retrieve URLs from any webpage URL passed by Flask API
  9. Generating Scrapy Error 'ValueError - signal only works in main thread'
  10. Comparing Scrapy Logs for the cases of Success and Failure for Wikipedia
  11. Demonstrating proxy authorization for a Scrapy spider through middleware and custom settings
  12. Creating an HTML form on Flask server and scraping it with Scrapy [Tags: 'Flask', 'Scrapy']
  13. Scrapy Q&A (Mar 2020)

WS2: BeautifulSoup

  1. Web Scraping using PySpark (standalone mode) and BeautifulSoup [Tags: BeautifulSoup, Spark]
  2. Web Scraping using PySpark (with 3 nodes) and BeautifulSoup [Tags: BeautifulSoup, Spark]

WS3: Selenium

  1. Starting With Selenium's Python Package (Installation and Novice Errors)
  2. Resolving Selenium error for WebDriver and Chrome Browser version mismatch (Jan 2023)
  3. Getting utterances for FAQ type chatbot using Edge, Selenium and Quillbot webapp [Tags: NLP, Web Scraping]
  4. Ad-Serving Limit Applied Second Time (Mar 2022) [Tags: Web Development, Web Scraping]

WS4: Requests

  1. Requests.get method, cleaning html and writing output to text file
  2. Getting a Web Server's Response Header Using Python
  3. HTTP Error Codes and REST APIs [Tags: Python, Web Development, Web Scraping]
  4. Before you continue to YouTube, Google uses cookies and data to... [Jul 2021] [Tags: Cyber Security, Web Development, Web Scraping]

Windows CMD

Certificates, Rewards and Recognitions

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