.
├── analytics
├── aws
├── books
├── data manipulation
│   └── micro
│       ├── DATAFRAME AS A DICTIONARY.ipynb
│       ├── Handlling Missing Data.ipynb
│       ├── Hierarchical Indexing.ipynb
│       ├── INDEXERS LOC ILOC AND IX.ipynb
│       ├── Series and Dictionary.ipynb
│       ├── testing.ipynb
│       └── Ufuncs Index Preservation, axis.ipynb
├── deep learning
│   ├── dlaz
│   │   └── Artificial Neural Network - Bank Churn Data.ipynb
│   ├── handon-dl
│   └── nano-dl
│       ├── Sentiment_Classification_Projects.ipynb
│       └── Sentiment_Classification_Solutions.ipynb
├── finance
│   ├── Calculating Beyda.ipynb
│   ├── Multivariate Regression.ipynb
│   ├── Pandas rolling and expanding.ipynb
│   ├── time resampling.ipynb
│   └── time shifting.ipynb
├── Google Colab
├── images
├── kaggle
├── machine learning
├── mapreduce
├── matplotlib
│   └── micro
│       ├── 1D and 2D Histograms, Binnings, and Density.ipynb
│       ├── Customizing Plot Legends.ipynb
│       ├── Density and Contour plots.ipynb
│       ├── errorbars and continuous error.ipynb
│       ├── iris dataset visualization.ipynb
│       ├── Legend for Size of Points.ipynb
│       └── matplotlib general.ipynb
├── micro_projects
│   ├── 50 startups - multi linear regression ML.ipynb
│   ├── Decision Tree Classification ML.ipynb
│   ├── Decision Tree Regression ML.ipynb
│   ├── deep_learning
│   ├── Hierarchial Clustering - Mall csv ML.ipynb
│   ├── Kernel SVM  ML.ipynb
│   ├── KNN for dataset Social Network Ads ML.ipynb
│   ├── Logistic Regression - Social Network Ads ML.ipynb
│   ├── Mall Customer Clusters - Kmeans ML.ipynb
│   ├── Position Salary - Polynomial regression ML.ipynb
│   ├── Position Salary Prediction - Random Forest ML.ipynb
│   ├── Random Forest Classifier ML.ipynb
│   ├── Salary Prediction ML.ipynb
│   └── Support Vector Machine (Classifier) - Network Ads ML.ipynb
├── NLP
│   ├── 16_nlp_with_rnns_and_attention - google colab.ipynb
│   ├── 16_nlp_with_rnns_and_attention.ipynb
│   ├── images
│   │   └── nlp
│   └── Shakespeare HOML _ local.ipynb
├── numpy
├── python
├── retail
│   └── five-point summary .ipynb
├── scikit-learn
├── scipy
└── spark

28 directories, 39 files