https://github.com/milovantomasevic/complete-2020-data-science-and-machine-learning-bootcamp
Learn Python, Tensorflow, Deep Learning, Regression, Classification, Neural Networks, Artificial Intelligence & more!
https://github.com/milovantomasevic/complete-2020-data-science-and-machine-learning-bootcamp
Last synced: 8 months ago
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Learn Python, Tensorflow, Deep Learning, Regression, Classification, Neural Networks, Artificial Intelligence & more!
- Host: GitHub
- URL: https://github.com/milovantomasevic/complete-2020-data-science-and-machine-learning-bootcamp
- Owner: MilovanTomasevic
- License: mit
- Created: 2021-04-26T16:37:02.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2021-05-11T18:26:29.000Z (over 4 years ago)
- Last Synced: 2024-12-29T05:11:45.307Z (9 months ago)
- Language: Roff
- Size: 49.8 MB
- Stars: 2
- Watchers: 2
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Complete-2020-Data-Science-and-Machine-Learning-Bootcamp
Learn Python, Tensorflow, Deep Learning, Regression, Classification, Neural Networks, Artificial Intelligence & more!
### Certifications
- [Data Science & Machine Learning](https://udemy.com/certificate/UC-ed77b5a5-d026-45a5-9efc-1680c2bb2626/)
### Awknowledgements
Course work and notes from Udemy Course by Philipp Muellauer and Dr. Angela Yu.
### Section 2: Predict Movie Box Office Revenue with Linear Regression
* [the-numbers.com](https://www.the-numbers.com/movie/budgets) - Movie budgets and revenue data
* [Try Jupyter Notebook Online](https://jupyter.org/try) (no installation)
* [pandas.pydata.org](https://pandas.pydata.org/) - the Pandas data analysis library
* [matplotlib.org](https://matplotlib.org/) - the Matplotlib graphing and plotting library
### Section 3: Python Programming for Data Science and Machine Learning
* [Install Jupyter with the Python Anaconda distribution](https://www.anaconda.com/distribution/)
* [The original research paper on test scores and LSD tissue concentration](https://www.ncbi.nlm.nih.gov/pubmed/5676802) (for the more curious)
* [Raw Experiment Data](http://users.stat.ufl.edu/~winner/data/lsd.dat)
* [Exercise 1 Solution - Python Variables](https://gist.github.com/TheMuellenator/cbcd9cbc2d2c36b652ca1337248b8201)
* [Exercise 2 Solution - Python Lists](https://gist.github.com/TheMuellenator/d98dacca633022999f5368e693f02b5d)
* [Exercise 3 Solution - Python Functions Part 1](https://gist.github.com/TheMuellenator/dc4d84419c38a5aa023f85c26bea2dc7)
* [Exercise 4 Solution - Python Functions Part 2](https://gist.github.com/TheMuellenator/fc9f7ffb3075da52a5953f859e01aee0)
* [Exercise 5 Solution - Python Functions Part 3](https://gist.github.com/TheMuellenator/2adad8377efd023d7c6e01537d8143f6)
### Section 4: Introduction to Optimisation and the Gradient Descent Algorithm
* [Symbolab.com](https://www.symbolab.com/solver/derivative-calculator) - an online derivative calculator
* [SymPy Homepage](https://www.sympy.org/en/index.html) - a Python library for symbolic mathematics
* [Exercise 6 Solution - Python Loops](https://gist.github.com/TheMuellenator/a36e7edadcf4b38aff1d759dad737526)
### Section 5: Predict House Prices with Multivariable Linear Regression
* [load_boston() documentation](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_boston.html) - available through scikit learn's website
* [scikit learn regression metrics documentation](https://scikit-learn.org/stable/modules/model_evaluation.html#regression-metrics)
* [US inflation calculator](https://www.usinflationcalculator.com/)
* [Exercise 7 Solution - Conditional Statements](https://gist.github.com/TheMuellenator/35d8c02838fb3728c3252d793e5d764b)
### Section 6: Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails (Part 1)
* [Spam Assassin data description](https://spamassassin.apache.org/old/publiccorpus/readme.html)
* [VS Code](https://code.visualstudio.com/) - optional Text Editor (my current favourite)
* [Atom](https://atom.io/) - optional Text Editor
* [Spam Assassin Public Corpus](https://spamassassin.apache.org/old/publiccorpus/) - the original email dataset source
* [Detailed description of the email data](https://spamassassin.apache.org/old/publiccorpus/readme.html)
* [XKCD on Unicode](https://xkcd.com/1953/)
* [The Timewaster Letters](https://www.amazon.co.uk/Timewaster-Letters-Robin-Cooper-ebook/dp/B005G14LSA/) by Robin Cooper
* [jsonmate.com](http://jsonmate.com/) - visualise JSONs
* [flatuicolors.com](https://flatuicolors.com/) - colours that make pie charts look pretty
* [www.nltk.org](https://www.nltk.org/) - Natural Language Toolkit (NLTK)
* [Documentation on Python sets](https://docs.python.org/3.7/library/stdtypes.html#set-types-set-frozenset)
* [Word stemmers on nltk.org](https://www.nltk.org/api/nltk.stem.html)
* [Martin Porter's (rather humorous) homepage](https://tartarus.org/martin/) - creator of the Porter Stemmer
* [example.com](http://example.com/) - clear and beautiful HTML on the web. Right-click to view source
* [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/bs4/doc/) - a tool to pull data out of HTML and XML files
* [github.com/amueller/word_cloud](https://github.com/amueller/word_cloud) - Andreas Mueller's WordCloud Github Repo
* [WordCloud documentation](https://amueller.github.io/word_cloud/)
* [Pillow documentation](https://pillow.readthedocs.io/en/stable/reference/index.html) - a module for image manipulation module
* [Colormap reference](https://matplotlib.org/examples/color/colormaps_reference.html) (Matplotlib)
* [Font Awesome](https://fontawesome.com/icons?d=gallery&m=free) - free icons for masks
* [Google Fonts](https://fonts.google.com/) - free fonts for your projects
* [www.wordclouds.com](https://www.wordclouds.com/) - online word cloud generator
* [What is mojibake?](https://en.wikipedia.org/wiki/Mojibake)
### Section 7: Train a Naive Bayes Classifier to Create a Spam Filter (Part 2)
* [Laplace Smoothing](https://en.wikipedia.org/wiki/Additive_smoothing) (for the more curious)
* [Numpy savetxt() documentation](https://docs.scipy.org/doc/numpy/reference/generated/numpy.savetxt.html)
* [Numpy loadtxt() documentation](https://docs.scipy.org/doc/numpy/reference/generated/numpy.loadtxt.html)
### Section 8: Test and Evaluate a Naive Bayes Classifier (Part 3)
* [Numpy .dot() product documentation](https://docs.scipy.org/doc/numpy-1.15.0/reference/generated/numpy.dot.html)
* [Matplotlib marker types](https://matplotlib.org/api/markers_api.html) for data points
[](https://seaborn.pydata.org/tutorial/color_palettes.html)
* [Seaborn colour palettes](https://seaborn.pydata.org/tutorial/color_palettes.html) for graphs and charts
* [Color Lisa](http://colorlisa.com/) - curated colours from famous artists
* [webmd symptom checker](https://symptoms.webmd.com/default.htm#/info) - the perfect tool for every hypochondriac?
### Section 9: Introduction to Neural Networks and How to Use Pre-Trained Models
* [Google Translate singing](https://youtu.be/9mD-ZmWuFTQ)
* [Google Colab Notebooks](https://colab.research.google.com/notebooks/welcome.ipynb) - getting started
* [ModelZoo.co](https://modelzoo.co/) - list of pre-trained models
* [TensorFlow Hub](https://www.tensorflow.org/hub) - list of pre-trained models
* [Sample Images for Image Classification](https://drive.google.com/open?id=1JtiBoNIEQYEMdDfOR-yh8f9kdGeNgvdo)
* [Unsplash.com](https://unsplash.com/) - high quality, royalty free images
* [Pre-trained Models available through Keras](https://keras.io/applications/)
* [Keras InceptionResNetV2 documentation](https://keras.io/applications/#inceptionresnetv2)
* [Keras models and their methods](https://keras.io/models/model/)
* [Keras VGG19 documentation](https://keras.io/applications/#vgg19)
* [Google Blog Post on NasNet and Image Classification](https://ai.googleblog.com/2017/11/automl-for-large-scale-image.html)
### Section 10: Build an Artificial Neural Network to Recognise Images using Keras and Tensorflow
* [Canadian Institute for Advanced Research (CIFAR)](https://www.cifar.ca/)
* [CIFAR 10 Dataset and Description by Alex Krizhevsky](https://www.cs.toronto.edu/~kriz/cifar.html)
* [Keras Activation Functions](https://keras.io/activations/)
* [Keras Optimizers](https://keras.io/optimizers/)
* [Python strftime() documentation](https://docs.python.org/2/library/datetime.html#strftime-and-strptime-behavior)
* [Fitting a Keras Model](https://keras.io/models/model/#fit)
* [Keras Dropout Layer documentation](https://keras.io/layers/core/#dropout)
* [predict() method documentation](https://keras.io/models/model/#predict)
* [evaluate() method documentation](https://keras.io/models/model/#evaluate)
* [matplotlib colormaps](https://matplotlib.org/2.0.2/examples/color/colormaps_reference.html) - examples
* [Python itertools](https://docs.python.org/2/library/itertools.html) - efficient looping
### Section 11: Use Tensorflow to Classify Handwritten Digits
* [The MNIST Database](http://yann.lecun.com/exdb/mnist/)
* [Numpy eye() documentation](https://docs.scipy.org/doc/numpy/reference/generated/numpy.eye.html)
* [Tensorflow Placeholder documentation](https://www.tensorflow.org/api_docs/python/tf/placeholder)
* [Tensorflow matmul documentation](https://www.tensorflow.org/api_docs/python/tf/linalg/matmul)
* [Tensorflow relu documentation](https://www.tensorflow.org/api_docs/python/tf/nn/relu)
* [Tensorflow softmax documentation](https://www.tensorflow.org/api_docs/python/tf/nn/softmax)
* [Softmax Cross Entropy with Logits](https://www.tensorflow.org/api_docs/python/tf/nn/softmax_cross_entropy_with_logits_v2)
* [Adam Optimizer](https://www.tensorflow.org/api_docs/python/tf/train/AdamOptimizer)
* [MNIST handwritten digit database model comparison](http://yann.lecun.com/exdb/mnist/)
### Section 12: Serving a Tensorflow Model through a Website
* [TF Session Object documentation](https://www.tensorflow.org/versions/r1.14/api_docs/python/tf/Session)
* [Saved Model documentation](https://www.tensorflow.org/guide/saved_model)
* [Saved Model load() documentation](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/saved_model/load)
* [Session run() documentation](https://www.tensorflow.org/versions/r1.14/api_docs/python/tf/Session#run)
* [Model conversion](https://www.tensorflow.org/js/guide/conversion)
* [Tensorflow.js converter](https://github.com/tensorflow/tfjs-converter)
* [Managing environments with Anaconda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html)
* [Atom text editor](https://atom.io/)
* [VS Code](https://code.visualstudio.com/)
* [MDN - Web Development Documentation](https://developer.mozilla.org/en-US/)
* [W3 Schools](https://www.w3schools.com/tags/default.asp)
* [FlatUI Colors](https://flatuicolors.com/palette/defo)
* [Getting started with Tensorflow.js](https://www.tensorflow.org/js/tutorials)
* [Favicon Generator](https://www.favicon-generator.org/)
* [MDN events](https://developer.mozilla.org/en-US/docs/Web/Events)
* [W3 Schools HTML Canvas reference](https://www.w3schools.com/tags/ref_canvas.asp)
* [Download the OpenCV.js file](https://docs.opencv.org/4.1.0/opencv.js)
* [Geometric transformations for images](https://docs.opencv.org/trunk/da/d6e/tutorial_py_geometric_transformations.html)
* [Contour Features & the Centre of Mass](https://docs.opencv.org/3.4/dd/d49/tutorial_py_contour_features.html)
* [Javascript map() documentation](https://www.w3schools.com/jsref/jsref_map.asp)
* [Tensorflow.js operations](https://www.tensorflow.org/js/guide/tensors_operations)
* [Tensorflow.js dataSync](https://js.tensorflow.org/api/0.6.1/#tf.Tensor.dataSync)
* [Window setTimeout() documentation](https://www.w3schools.com/jsref/met_win_settimeout.asp)
* [Github Pages](https://pages.github.com/)