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README.md

 ## pycaret
 pycaret is the free software and open source machine learning library for python programming language. It is built around several popular machine learning libraries in python. Its primary objective is to reduce the cycle time of hypothesis to insights by providing an easy to use high level unified API. pycaret's vision is to become defacto standard for teaching machine learning and data science. Our strength is in our easy to use unified interface for both supervised and unsupervised machine learning problems. It saves time and effort that citizen data scientists, students and researchers spent on coding or learning to code using different interfaces, so that now they can focus on business problem and value creation. 
 
-## Key Features
-* Ease of Use
-* Focus on Business Problem
-* 10x efficient
-* Collaboration
-* Business Ready
-* Cloud Ready
-
 ## Current Release
-The current release is beta 0.0.5 (as of 23/12/2019). A full release for public is targetted to be available by 31/12/2020.
+The current release is beta 0.0.6 (as of 24/12/2019). A full release is targetted to be available by 31/01/2020.
+
+## Features Currently Available
+As per beta 0.0.6 following modules are generally available:
+*pycaret.classification
+*pycaret.regression
+*pycaret.nlp
+*pycaret.arules
+
+## Future Release
+Following features are targetted for future release (beta 0.0.7 & beta 0.0.8):
+*pycaret.anamoly
+*pycaret.clustering
+*pycaret.preprocess
 
 ## Installation
 
@@ -25,20 +30,22 @@ pip install pycaret
 ```
 
 ## Quick Start
-As of beta 0.0.5 classification, regression and nlp modules are available. Future release will be include Anamoly Detection, Association Rules, Clustering, Recommender System and Time Series.
+As of beta 0.0.6 classification, regression and nlp modules are available. Future release will be include Anamoly Detection, Association Rules, Clustering, Recommender System and Time Series.
 
-### Classification
+### Classification / Regression
 
 Getting data from pycaret repository
 
 ```python
 from pycaret.datasets import get_data
-juice = get_data('juice')
+juice = get_data('juice') #classification dataset
 ```
 
 1. Initializing the pycaret environment setup
 
 ```python
+from pycaret.classification import * #for classification
+from pycaret.regression import * #for regression
 exp1 = setup(juice, 'Purchase')
 ```
 
@@ -67,7 +74,12 @@ Extra Trees Classifier (et) <br/>
 Extreme Gradient Boosting - xgboost (xgboost) <br/>
 Light Gradient Boosting - Microsoft LightGBM (lightgbm) <br/>
 
-3. Tuning a model using inbuilt grids.
+3. Compare all models at once
+```python
+compare_models()
+```
+
+4. Tuning a model using pre-built search grids.
 ```python
 tuned_xgb = tune_model('xgboost')
 ```
@@ -171,18 +183,35 @@ m = load_model('lr_23122019')
 e = load_experiment('expname1')
 ```
 
-14. Compare all Models
-```python
-compare_models()
-```
+## AutoML
+All modules also have AutoML module built-in. It is very easy to run AutoML. 
 
-15. AutoML
 ```python
-aml1 = automl()
+from pycaret.datasets import get_data
+juice = get_data('juice')
+
+from pycaret.classification import *
+exp1 = setup(data, 'Purchase')
+
+aml1 = automl() #same for regression
 ```
 
+## Getting Started Tutorials
+Tutorials are work in progress. Will be uploaded on our git page by 07/01/2020.
+
 ## Documentation
 Documentation work is in progress. They will be uploaded on our website http://www.pycaret.org as soon as they are available. (Target Availability : 21/01/2020)
 
 ## Contributions
 Contributions are most welcome. To make contribution please reach out moez.ali@queensu.ca
+
+## License
+
+Copyright 2019 pycaret
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+© 2019 GitHub, Inc.
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requirements.txt

@@ -4,7 +4,7 @@ seaborn
 matplotlib
 IPython
 joblib
-sklearn
+scikit-learn
 shap
 ipywidgets
 yellowbrick
@@ -18,4 +18,5 @@ umap-learn
 pyLDAvis
 gensim
 spacy
-nltk
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+nltk
+mlxtend
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setup.py

@@ -8,7 +8,7 @@ def readme():
 
 setup(
     name="pycaret",
-    version="0.0.5",
+    version="0.0.6",
     description="A Python package for supervised and unsupervised machine learning.",
     long_description=readme(),
     long_description_content_type="text/markdown",
@@ -24,7 +24,7 @@ setup(
     packages=["pycaret"],
     include_package_data=True,
     install_requires=["pandas", "numpy", "seaborn", "matplotlib", "IPython", "joblib", 
-                     "sklearn", "shap", "ipywidgets", "yellowbrick", "xgboost==0.90",
+                     "scikit-learn", "shap", "ipywidgets", "yellowbrick", "xgboost==0.90",
                      "wordcloud", "textblob", "plotly==4.4.1", "cufflinks", "umap-learn",
-                     "lightgbm==2.3.1", "pyLDAvis", "gensim", "spacy", "nltk"]
+                     "lightgbm==2.3.1", "pyLDAvis", "gensim", "spacy", "nltk", "mlxtend"]
 )
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