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ai-with-tensorflow

Image & Speech Processing | Perceptrons (MLP) | Autoencoders - seq2seq | Generative Adversarial Networks (GAN) | Convolutional Neural Network (CNN & DCNN) | Recurrent Neural Networks (RNN): LSM | LSTM | GRU

apache-spark-scala

Functional Programming | Apache Spark Framework | Spark RDDs | DataFrames and Spark SQL | Machine Learning using Spark MLlib | Apache Kafka and Apache Flume | Apache Spark Streaming

datascience-with-python

datasets

This folder have variety of Datasets to use & practise Data Science Model, Analytics & Insight, Visualization in Tableau!

ds_ml_projects

Machine Learning Projects

eas503

Python_Learning

machine_learning_in_python_n_r

Machine Learning in Python & R | All Model from A to Z

machine_learning_model_n_techniques

microsoft_certification_associate_data_scientist

Files from Microsoft Certified: Azure Data Scientist Associate

ml_model_deployment

ML Models Deployment in production/prediction phase.

multiplemodeltesting

Experimenting by using Multiple ML Model

python-for-data-science-ml

SUPERVISED LEARNING: REGRESSION: Linear - Polynomial - Ridge/Lasso CLASSIFICATION: K-NN - Naïve Bayes - Decision Tree - Logistic Regression - Confusion Matrix - SVM TIME SERIES ANALYSIS: Linear & Logistic Regr. - Autoregressive Model - ARIMA - Naïve - Smoothing Technique UNSUPERVISED LEARNING: CLUSTERING: K-Means - Agglomerative - Mean-Shift - Fuzzy C-Mean - DBSCAN - Hierarchical - Canopy DIMENSION REDUCTION: PCA - LSA - SVD - LDA - t-SNE PATTERN SEARCH: Apriori - FP-Growth - Euclat RECOMMENDATION ENGINE: Association Rules - Market Basket Analysis - Apriori Algorithm - Real Rating Matrix - IBCF - (Item) - User-Based Collaborative Filtering UBCF - Method & Model ENSEMBLE METHODS: BOOSTING: AdaBoost - XG Boost - LightGBM - CatBoost. BAGGING: Random Forest STACKING

python_basics

Basic Python - Every Champion is a beginner sometime, don't underestimate the POWER of BASICS!

python_spark_using_pyspark

PYTHON for APACHE SPARK [PYSPARK] | Big Data Hadoop & Spark | Apache Spark Framework | Spark RDDS | Dataframes & Spark SQL | ML using Spark MLLIB | Apache Kafka & Flume | Spark Streaming

r-programming

R Programming: ML, Text Mining, WordCloud - Uni/Bi/Tri-Graph, Sentiment Analysis, SVM, Recommendation Engine, TF-IDF, Time Series, UBCF, Association Rule, Market Basket Analysis,

tableau

Tableau Analysis & Visualization with Business Problem & Dataset: Aggregation | Granuality | Customer Segmentation Dashboard | Data Blending | Table Calculation | Data Joining & Connection | Level of Details (LOD) Expressions Geographic Visualizations.