Introduction to Big Data Analytics:

Lecture Notes_1_1: Simple Overview of Big Data Analytics

Lecture Notes_1_2: Introduction to Big Data, Big Data Processing and Big Data Analytics

Lecture Notes_1_3: Big Data, Data Scientist and Research on Big Data Analytics at CSU

What is Data Mining?

Chapter 1: Overview of Data Mining: What is Data Mining and Data Mining Steps


Basic Data Structures and Data Properties in Data Mining:

Chapter 2_1: What is Data Structure and Properties of DATA in Data Mining? (Kumar's Chapter 2_1)*****************

Chapter 2_1: Your DATA in Properties and Data Structures for Data Mining (Part 1) (J Han's Chapter 2_1)



Example of Data Preprocessing and Transformation Steps For a Data Mining with Classification


Data Preprocessing Steps: 1. Feature Selection, 2. Preprocessing:Cleaning, 3. Exploring to Get to Know Data, 4. Reduction/Integration, 5.Transformation, 6. Correct Measuring


1. Feature Selection Methods: (To Be Covered at the end)





2. Data Cleaning:

Chapter2_1: Data Quality and Data Cleaning (Kumar's Chapter2_1)

Chapter3_1: Data Cleaning for Null and Outliers in Data Preprocessing (J Han's Chapter3_1)*****************




2_1: Getting to Know Your Data with Basic Stats for Cleaning


Chapter 2_2 : Knowing Your Data for Data Disperse/Distribution (Basic Statistics Review) - Part 1 (J Han's Chapter2_2)***************

Grouped Data Calculation


Measure and Visualization of Data Disperse (Distribution) of Each Feature in Data Set:

Unstructured Text Processing for Text Analytics

Lexicon Based Approach with Document(Text) as Bag of Words Model:

Lecture Notes_18: Lecture Notes on IR TF-IDF for Text/Web Mining ******************

Lecture Notes_18: Lecture Notes on Inverted Index


Example of Building Inverted Index on State Union Addresses for Text Analysis

Machine Learning for Data Anaytics

Supervised Learning

Classification with Machine Learning:

Error Estimation and Model Evaluation

Lecture Notes_14: Lecture Notes (Combined from Both Textbooks) on Error estimation and Validation
Corrections in Lecture Notes !
Multi Class Model Evaluation and Comparison: Macro and Micro F1: Precisions/Recall

Useful Tool for Model Evaluation:

Model Evaluation and Comparison: Example of ROC with Precision and Recall
Model Evaluation and Comparison: Macro and Micro F1: Precisions/Recall

Machine Learning with Numerical Approach:

Upsupervised Learning



Clustering


Lecture Notes_14: Lecture Notes on Clustering Analysis

Lecture Note on K-Mean Algorithm



scikit-learn Clustering


Tutorial on Clustering Analysis with Mixed Data Types and Visulaization in t-SNE (t-distributed stochastic neighborhood embedding)







Hot Spot Anlaysis for Geospatial Data Analysis

Lecture:

Lecture: Hot Spot Analysis in PAI and PEI Metrics to Evaluate Hot Spot/Cold Spot


What is Hotspot Analysis and Z-score and p value based metric


Wiki on Hot Spot Analysis



National Institute of Justice (NIJ) Crime HotSpot Analysis Challenging:

NIJ (National Institute of Justice) Crime Forecasting Challenge Overview



Research Project Report on PREDICTIVE HOTSPOT MAPPING ANALYSIS (The Paper that Created PAI and PEI Metrics to Evaluate Hot Spot Analysis)

Documents/Related Materals for NIJ Crime Forecasting Challenges:


Documentation in Depth for Geospatial Statistics Analysis

Data Sets:

NIJ Challenging Data Set: Crime Data
NIJ Challenging Data Site: NIJ Crime Location Data Set for Hot Spot Analysis
NIJ Challenging Data Set: Crime locations in Portland


For Those Who Want To Apply for NIJ Crime Forecasting Challenges:
FAQs for NIJ Crime Forecasting Challenges





GIS Data Visualization API:

Advanced Text Analysis with Natural Language Processing

Semi-Supervised Learning (Self Learning) for Text Analysis:

Context Aware NLP Methods Using Machine Learning with Semi-Supervised Learning: Word2Vec (Google)

Problems of Learning a Sequence of Tokens in a Sentence/Phrase in the Lexicon Based Approach

Lecture on Earlier Approach: Positioning Index for Phrase Query (Stanford)






Natural Language Processing (NLP) with Deep Learning for Word Embeddings:

Word2Vec:

Context Aware NLP Methods Using Machine Learning with Semi-Supervised Learning: Word2Vec

Lectures:

Lecture Notes_19_1: Lecture Notes on Natural Language Processing (Stanford): Word to Vector Model (Word2Vec) in Skip Gram Model

Tutorial on Word to Vector (Word2Vec): Skip Gram Model




Google Site for Word2Vec Implementation and Word2Vec Papers by Google AI

Word-2-Vec Optimization of Training Computation

Tutorial on Optimization of Word to Vector (Word2Vec) Training



Extended Word2Vec: Glovec
Sites for Glove Word Vector by Stanford NLP Team

Word2Vec Implementation Sites:

To get an Executable Binary of word2Vec Model Implementation and Training Data sets:

Sites for Word To Vector by Google

Sites for Word To Vector for npmjs
Sites for Glove Word Vector by Stanford NLP Team
CNTK: Stanford NLP Sentiment Analysis



Tutorial Sites for Word2Vec Implementation:

Tutorials for Word To Vector: Skipgram Model
Tutorials for Word To Vector with Google Tensorflow
Tutorials for Word Embeddingd with Google Tensorflow

Tutorials for Word To Vector and related APIs, Gensim (LDA)
Tutorials for Bag of Words, Word2Vec and related APIs








BERT (Bi-Directional Transformer)for Deep Learning :

State of Art NLP AI: Google BERT Transformer
(The Most Recent and Most Superior Word Vector Generator: Google BERT

Tutorial: What is Google AI BERT Transformer?

BERT from Google AI




Tutorails:

Tutorials on BERT

Simple Tutorial for Sentiment Analysis with BERT


Code Tutorial Site to Use Pretrained BERT Transformer:

Good Tutorial Site to Use Pretrained BERT Transformer

Good Tutorial to Use Pretrained BERT Transformer for Question Answering System

Good Tutorial to Use Pretrained BERT Transformer for Domain Specific System

Good Tutorial Site to Use BERT Transformer for Sentence Classification

SpaCy Embeddings for BERT Transformer

Deep Learning:


Basic Review For Multimedia Files:
Basics on Color Representation: 24 bit RGB Code in 3 Color Channels (One Byte Code (2^8: 0 ~ 255) per Each Channel)
Basics on Digitizing Images with Resolution
Basics on Color Image Quantization
Basics on Digitizing Sound



Deep Learning for Image Recognition

Lecture Notes:

Basics Methods BEFORE Deep Leraning with CNN:

Lecture on Basic Notation, Image File as Input with Logistic Regression and intro to Gradient Descent and Neural Network (From DeepLearing.AI by Stanford Coursera)

Tutorial on Basic Image Recognition with Classifier



Tutorial for Multi-Class Classification with Multinomial logistic regression for Object Identification

Easy Tutorial on Multi Layor Neural Network with Softmax Function

Easy Tutorial on Softmax Function in NN




Lecture on Deep Learning with CNN *****************************************************

Deep Learning with Convolution Neural Networks (CNN) for Classification of Image Identification (Stanford) **********************************




Good Image Classification Tutorial sites:

Tutorials for 10 image classification

OpenCV Tutorial: Open Source Computer Vision in C (Python or JavaScript Wrapper Available)

Python Deeep Learning API: Keras Tutorial

Tensorflow with Keras Tutorial
Recurrent Neural Network(RNN) in Tensorflow with Keras Tutorial


Examples of Image Classification Basics to Start With:

Tutorials for Google Tensorflow Image Recognition with CNN



CIFAR-10 and CIFAR-100 Image data set

Example of a Filter for CNN


Tutorials for Google Tensorflow with CNN

Google Analytics and AI

Google AI Tools

Google Xception for Image Recognition


Useful Sites for Face Recognition:
Open Face Research Site on Face Recognition Techniques Using Machine Learning
Step by step Tutorials on Face Recognition Techniques Using Machine Learning
Python with Fully Pre-configured VM for Face Recognition Data Analytics
Simple Tutorial Site for Face Recognition With Deep Learning CNN

Deep Learning Data Set Sites:

Good Deep Learning Data Sets
Open Image Data sets
Kaggle Data sets

Presentation of Projects