{"cells":[{"metadata":{"_uuid":"9760b625456aefbdc3d888fac42bf749f5c64e78"},"cell_type":"markdown","source":"# Hands-On Machine Learning with Scikit-Learn and TensorFlow Notebooks (TBU)\n\n*Welcome to the Machine Learning Notebooks!*\n\n    This kernel has a series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.\n     \n> #### **Credits**: Thanks to **Hands-On ML & ML 2 - Aurélien Geron** and other contributers for such wonderful curated collections!\n\n### Here are some of *my kernel notebooks* for **Machine Learning and Data Science** as follows, ***Upvote*** them if you *like* them\n\n> * [Awesome Deep Learning Basics and Resources](https://www.kaggle.com/arunkumarramanan/awesome-deep-learning-resources)\n> * [Data Science with R - Awesome Tutorials](https://www.kaggle.com/arunkumarramanan/data-science-with-r-awesome-tutorials)\n> * [Data Science and Machine Learning Cheetcheets](https://www.kaggle.com/arunkumarramanan/data-science-and-machine-learning-cheatsheets)\n> * [Awesome ML Frameworks and MNIST Classification](https://www.kaggle.com/arunkumarramanan/awesome-machine-learning-ml-frameworks)\n> * [Awesome Data Science for Beginners with Titanic Exploration](https://kaggle.com/arunkumarramanan/awesome-data-science-for-beginners)\n> * [Tensorflow Tutorial and House Price Prediction](https://www.kaggle.com/arunkumarramanan/tensorflow-tutorial-and-examples)\n> * [Practical Machine Learning with PyTorch](https://www.kaggle.com/arunkumarramanan/practical-machine-learning-with-pytorch)\n> * [Data Scientist's Toolkits - Awesome Data Science Resources](https://www.kaggle.com/arunkumarramanan/data-scientist-s-toolkits-awesome-ds-resources)\n> * [Awesome Computer Vision Resources (TBU)](https://www.kaggle.com/arunkumarramanan/awesome-computer-vision-resources-to-be-updated)\n> * [Machine Learning and Deep Learning - Awesome Tutorials](https://www.kaggle.com/arunkumarramanan/awesome-deep-learning-ml-tutorials)\n> * [Data Science with Python - Awesome Tutorials](https://www.kaggle.com/arunkumarramanan/data-science-with-python-awesome-tutorials)\n> * [Awesome TensorFlow and PyTorch Resources](https://www.kaggle.com/arunkumarramanan/awesome-tensorflow-and-pytorch-resources)\n> * [Awesome Data Science IPython Notebooks](https://www.kaggle.com/arunkumarramanan/awesome-data-science-ipython-notebooks)\n> * [Machine Learning Engineer's Toolkit with Roadmap](https://www.kaggle.com/arunkumarramanan/machine-learning-engineer-s-toolkit-with-roadmap) \n> * [Hands-on ML with scikit-learn and TensorFlow](https://www.kaggle.com/arunkumarramanan/hands-on-ml-with-scikit-learn-and-tensorflow)\n\n> ***Practical Machine Learning ML with TensorFlow***\n\n> The above highlighted work will soon be released and also be based on the below coursework;\n\n* [Google Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course/) - Machine Learning ML Crash Course with TensorFlow APIs (Google's fast-paced, practical introduction to machine learning, A self-study guide for aspiring machine learning practitioners: Machine Learning Crash Course features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises.) is highly recommended by Google as it's developed by googlers along with Notebooks for exercises.\n       \n## Hands-On Machine Learning with Scikit-Learn and TensorFlow through IPython Notebooks\n\n> [Prerequisites](#Prerequisites) (see below)\n\n### Notebooks are as follows,\n\n* [The Machine Learning landscape v1](https://github.com/ageron/handson-ml/blob/master/01_the_machine_learning_landscape.ipynb)\n* [The Machine Learning landscape v2](https://github.com/ageron/handson-ml2/blob/master/01_the_machine_learning_landscape.ipynb)\n* [End-to-end Machine Learning project v1](https://github.com/ageron/handson-ml/blob/master/02_end_to_end_machine_learning_project.ipynb)\n* [End-to-end Machine Learning project v2](https://github.com/ageron/handson-ml2/blob/master/02_end_to_end_machine_learning_project.ipynb)\n* [Classification v1](https://github.com/ageron/handson-ml/blob/master/03_classification.ipynb)\n* [Classification v2](https://github.com/ageron/handson-ml2/blob/master/03_classification.ipynb)\n* [Training Linear Models v1](https://github.com/ageron/handson-ml/blob/master/04_training_linear_models.ipynb)\n* [Training Linear Models v2](https://github.com/ageron/handson-ml2/blob/master/04_training_linear_models.ipynb)\n* [Support Vector Machines v1](https://github.com/ageron/handson-ml/blob/master/05_support_vector_machines.ipynb)\n* [Support Vector Machines v2](https://github.com/ageron/handson-ml2/blob/master/05_support_vector_machines.ipynb)\n* [Decision Trees v1](https://github.com/ageron/handson-ml/blob/master/06_decision_trees.ipynb)\n* [Decision Trees v2](https://github.com/ageron/handson-ml2/blob/master/06_decision_trees.ipynb)\n* [Ensemble Learning and Random Forests v1](https://github.com/ageron/handson-ml/blob/master/07_ensemble_learning_and_random_forests.ipynb)\n* [Ensemble Learning and Random Forests v2](https://github.com/ageron/handson-ml2/blob/master/07_ensemble_learning_and_random_forests.ipynb)\n* [Dimensionality Reduction & Unsupervised Learning v1](https://github.com/ageron/handson-ml/blob/master/08_dimensionality_reduction.ipynb)\n* [Dimensionality Reduction](https://github.com/ageron/handson-ml2/blob/master/08_dimensionality_reduction.ipynb)\n* [Up and running with TensorFlow v1](https://github.com/ageron/handson-ml/blob/master/09_up_and_running_with_tensorflow.ipynb)\n* [Unsupervised Learning](https://github.com/ageron/handson-ml2/blob/master/09_unsupervised_learning.ipynb)\n* [Introduction to Artificial Neural Networks v1](https://github.com/ageron/handson-ml/blob/master/10_introduction_to_artificial_neural_networks.ipynb)\n* [Neural Networks with Keras](https://github.com/ageron/handson-ml2/blob/master/10_neural_nets_with_keras.ipynb)\n* [Deep Learning v1](https://github.com/ageron/handson-ml/blob/master/11_deep_learning.ipynb)\n* [Distributed TensorFlow v1](https://github.com/ageron/handson-ml/blob/master/12_distributed_tensorflow.ipynb)\n* [Convolutional Neural Networks v1](https://github.com/ageron/handson-ml/blob/master/13_convolutional_neural_networks.ipynb)\n* [Recurrent Neural Networks v1](https://github.com/ageron/handson-ml/blob/master/14_recurrent_neural_networks.ipynb)\n* [Autoencoders v1](https://github.com/ageron/handson-ml/blob/master/15_autoencoders.ipynb)\n* [Reinforcement Learning v1](https://github.com/ageron/handson-ml/blob/master/16_reinforcement_learning.ipynb)\n\n## Scientific Python tutorials\n\n* [NumPy](https://github.com/ageron/handson-ml/blob/master/tools_numpy.ipynb)\n* [Matplotlib](https://github.com/ageron/handson-ml/blob/master/tools_matplotlib.ipynb)\n* [Pandas](https://github.com/ageron/handson-ml/blob/master/tools_pandas.ipynb)\n\n## Math Tutorials\n\n* [Linear Algebra](https://github.com/ageron/handson-ml/blob/master/math_linear_algebra.ipynb)\n* Calculus (coming soon)\n\n## Extra Material\n\n* [Capsule Networks](https://github.com/ageron/handson-ml/blob/master/extra_capsnets.ipynb)\n* [TensorFlow Reproducibility](https://github.com/ageron/handson-ml/blob/master/extra_tensorflow_reproducibility.ipynb)\n* [Google Machine Learning Crash Course with TensorFlow APIs](https://developers.google.com/machine-learning/crash-course/)\n\n## Misc.\n\n* [Equations](https://github.com/ageron/handson-ml/blob/master/book_equations.ipynb) (list of equations in the book)"},{"metadata":{"collapsed":true,"_uuid":"32d45fec923043d991207c78f0a3f0712a8a722d"},"cell_type":"markdown","source":"## Prerequisites\n### To understand\n* **Python** – you don't need to be an expert python programmer, but you do need to know the basics. If you don't, the official [Python tutorial](https://docs.python.org/3/tutorial/) is a good place to start.\n* **Scientific Python** – We will be using a few popular python libraries, in particular NumPy, matplotlib and pandas. If you are not familiar with these libraries, you should probably start by going through the tutorials in the Tools section (especially NumPy).\n* **Math** – We will also use some notions of Linear Algebra, Calculus, Statistics and Probability theory. You should be able to follow along if you learned these in the past as it won't be very advanced, but if you don't know about these topics or you need a refresher then go through the appropriate introduction in the Math section."},{"metadata":{"collapsed":true,"_uuid":"f879823142dc60196b350887e77b573a1c7a7a93"},"cell_type":"markdown","source":"## Credits (Reference)\n> - [HandsOn ML - Aurélien Geron](https://github.com/ageron/handson-ml)\n> - [HandsOn ML 2 - Aurélien Geron](https://github.com/ageron/handson-ml2)\n> - [GitHub Awesome Lists Topic](https://github.com/topics/awesome)\n> - [GitHub Machine Learning Topic](https://github.com/topics/machine-learning)\n> - [GitHub Deep Learning Topic](https://github.com/topics/deep-learning)\n> - [GitHub Awesome Lists Topic](https://github.com/topics/awesome)\n\n## License\n\n[![CC0](http://mirrors.creativecommons.org/presskit/buttons/88x31/svg/cc-zero.svg)](https://creativecommons.org/publicdomain/zero/1.0/)\n\n### Please ***UPVOTE*** my kernel if you like it or wanna fork it.\n\n##### Feedback: If you have any ideas or you want any other content to be added to this curated list, please feel free to make any comments to make it better.\n#### I am open to have your *feedback* for improving this ***kernel***\n###### Hope you enjoyed this kernel!\n\n### Thanks for visiting my *Kernel* and please *UPVOTE* to stay connected and follow up the *further updates!*"}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"nav_menu":{},"toc":{"navigate_menu":true,"number_sections":true,"sideBar":true,"threshold":6,"toc_cell":false,"toc_section_display":"block","toc_window_display":false}},"nbformat":4,"nbformat_minor":1}