deadSwank001

EtherPricePrediction

credit to: Namanjeet Singh, Kaggle , https://www.kaggle.com/code/namanjeetsingh/ethereum-cryptocurrency-prediction-using-rnn/notebook

MemorySystemsStudy

Approaching Python storage systems in the context of human-like ability, focus on AI

Simple-Python-PortScanner

AppliedML-July

Mercedes-Benz Greener Manufacturing, Income Qualifications, DEL_SLR is salary and insurance,

AppliedML-July2

Song Classification, Employer Turnover

Clustering

Cluster Set (Clustering + Set Theory), Holomorphic Encryption, K-Means, centroid-based algos, Image Data (by PCA), Big Data Clustering, Hierarchical, Two-Phase, DBScan

Data-in-Action

Part 2: Chapter 9) Contextualizing Problems and Data, The 5 Mistruths, Researching Solutions, Formulating a Hypothesis, Preparing Data, Feature Generation, Combining Variables, Understanding Binning and Discretization, Indicator Variables, Transforms, Array Operations, Vertices and Matrices

Data-Plan

Part 2: Chapter 7) Validating, Removing Duplicates, Creating a Data Map and Data Plan, Manipulating Categorical Variables, Renaming Levels, Combining Levels, Dealing with Dates, Formatting date.time/ time transforming, Missing Data, Encoding Missingness, Imputing Missing Data, Slicing Dicing Filtering n' Selecting, Concatenating and Transforming,

Detecting-Outliers

Part 4: Chapter 16) anomalies and novel data, Gaussian distros, Assumptions, Multivariate Approach, PCA, Cluster Analysis for Outliers, Automating Detection with Isolation Forests

DogeNFT

EtherTrendAnalysis

credit to: Ayman Lafaz; Kaggle , https://www.kaggle.com/code/aymanlafaz/ethereum-trends-analysis/notebook

EtherWithPython

Few price and sentiment analysis scripts in Python; that can be used on ANY stock or coin.

Exploring-Data-Analysis

Iris Dataset, Kurtosis, Boxplots, Correlation, Chi-Test, Z-Score Distributions

Four-Simple-and-Effective-algos

Linear Regression; family of linear models, adding variables, limitations, Logistic Regression, Naive Bayes, Predictive Text, KNN (K-Nearest Neighbors), predictions and k-parameters

HashlibModule

All Python Encryption Services

Increasing-Complexity

Chapter 19 Continued

LinRegPractice2

Advertising dataset

MatPlotLib-CrashCourse

MovieReviewProfiling

PandasServ

311 Service Records

Performing-Cross-Validation-Selection-and-Optimization.py

How to fit a model, Understanding bias and variance, Strategy for picking models, Dividing Training/Testing, Cross-Validating, on K-Folds, Sampling stratification, Variable Selection, Greedy Search, Hyperparameters

PuppetCase

Puppeteer; Docker, NodeJS, npm, no Debian/Alpine, Express Server build

PythonPractice

Captain's log: day 249; all the Python modules I had skipped over from my original textbook, random.seed(), email, sms, FTP, news search, source clear, etc.

Reducing-Dimensionality

SVD, Factor Analysis, Hidden Factors, Dimensionality Reduction, TSNE, Facial Rec with PCA, NMF, Random Forest Recommender

Representing-SVM-Boundaries

Part 5: Chapter 19) Non-Linear and variable Transforms, Linear Models, L2 Ridge Regression, L1 Lasso, Regularization, L1 + L2: ElasticNet, Chunking Big Data, SGD, SVM> SVC, nonlinear w/ SVR, Stochastic SVM, Intro to NNs,

SciPy-and-ML-notes-LinReg-basics-

SkLearn's Imputer, Numpy and Panda's Imputer function (glazed over), imputation and extraction of null values, Ordinals, OneHot Encoders(OHE), Training/Testing data, LinReg

Shaping-Data

Part 2: Chapter 8) Shaping Data, Parsing XML and HTML, Using XPath for data extraction, Working with Raw Text, Stemming and Removing stop words, Intro to Regex, BOW model, n-grams, TF-IDF transforms, Graph data (adjacency matrix, Networkx basics)

Stretching-Pythons-Capabilities-Boston-Deprecated-Dataset-

Boston Real Estate Dataset deprecated for racial disparity [Multi-Threading practice]

The-Power-of-Many

Decision Trees for different purposes, Accessible Machine Learning, Random Forest Classifier/Regressor; Optimizing, Boosting Predictions, GB Regressor, GBM Hyperparameters

Visualizing-Data

Pie Charts, Bar Charts, Showing Distributions using Histograms, Boxplots, Scatterplots, Correlations, Time Series, Geographical Data, Basemap Toolkit, Visualizing Graphs (undirected and directed)