jashburn8020 / ml-course

Machine learning, data science, and deep learning with Python

Machine learning, data science, and deep learning with Python

Setup

• Install Anaconda
  • run conda init during installation
    • adds some code into your .bashrc file
    • causes the default conda base environment to be activated with every shell session
• Relaunch command line terminal
• Disable automatic base environment activation: conda config --set auto_activate_base False
  • adds auto_activate_base: false to the .condarc file
• Clone the base environment into a new environment, ml_course: conda create --name ml_course --clone base
  • convenient because the base environment already has a number of packages preinstalled, e.g., jupyter and matplotlib
  • to list all conda environments: conda info --envs
  • to remove the environment: conda remove --name myenv --all
  • to manually create a new conda environment with Python 3.8: conda create --name ml_course --python=3.8
    • you will also need to install the following packages:
      • jupyter
      • matplotlib
      • pandas
      • seaborn
      • scikit-learn
      • xlrd
      • statsmodels
  • to install packages into a conda environment: conda install [package [package ...]]
  • to remove packages: conda remove [package [package ...]]
• Activate the new environment: conda activate ml_course
  • to deactivate the environment: conda deactivate
• Install PyDotPlus: conda install pydotplus
• Install TensorFlow: conda install tensorflow
• Download course material: https://sundog-education.com/machine-learning/
  • a collection of Jupyter Notebook files (.ipynb)
  • unzip the course material
• Test run Jupyter Notebook
  • in the course material directory: jupyter notebook
  • select one of the notebook files, e.g., Outliers.ipynb
• Alternatively, you can view and run Jupyter Notebook files in VS Code; see
  • https://code.visualstudio.com/docs/python/jupyter-support
  • https://code.visualstudio.com/docs/python/data-science-tutorial

Introducing the Pandas Library

• See PandasTutorial.ipynb

Statistics and Probability Refresher

Types of Data

• See MLCourseSlides.pdf, slides 5-10

Using Mean, Median, and Mode in Python

• See:
  • MeanMedianMode.ipynb
  • MeanMedianExercise.ipynb

Standard Deviation and Variance

• See:
  • MLCourseSlides.pdf, slides 17-22
  • StdDevVariance.ipynb

Probability Density Function; Probability Mass Function

• Probability density function: probability of a range of values happening with continuous data
• Probability mass function: probabilities of given discrete values occurring in a data set
• See MLCourseSlides.pdf, slides 24-27

Common Data Distributions (Normal, Binomial, Poisson, etc.)

• See Distributions.ipynb

Percentiles and Moments

• See:
  • MLCourseSlides.pdf, slides 30-39
  • Percentiles.ipynb
  • Moments.ipynb

A Crash Course in matplotlib

• See MatPlotLib.ipynb

Advanced Visualisation with Seaborn

• See Seaborn.ipynb

Covariance and Correlation

• See
  • MLCourseSlides.pdf, slides 41-45
  • CovarianceCorrelation.ipynb

Conditional Probability

• See
  • MLCourseSlides.pdf, slides 48-49
  • ConditionalProbabilityExercise.ipynb
  • ConditionalProbabilitySolution.ipynb

Bayes' Theorem

• See MLCourseSlides.pdf, slides 52-54

Predictive Models

Linear Regression

• See
  • MLCourseSlides.pdf, slides 56-62
  • LinearRegression.ipynb

Polynomial Regression

• See
  • MLCourseSlides.pdf, slides 65-67
  • PolynomialRegression.ipynb

Multiple Regression

• See
  • MLCourseSlides.pdf, slides 69-70
  • MultipleRegression.ipynb

Multi-Level Models

• See MLCourseSlides.pdf, slides 73-75

Machine Learning with Python

Supervised vs. Unsupervised Learning, and Train/Test

Using Train/Test to Prevent Overfitting a Polynomial Regression

Bayesian Methods: Concepts

Implementing a Spam Classifier with Naive Bayes

K-Means Clustering

Clustering people based on income and age

Measuring Entropy

WINDOWS: Installing Graphviz

MAC: Installing Graphviz

LINUX: Installing Graphviz

Decision Trees: Concepts

Decision Trees: Predicting Hiring Decisions

Ensemble Learning

XGBoost

Support Vector Machines (SVM) Overview

Using SVM to cluster people using scikit-learn

Recommender Systems

User-Based Collaborative Filtering

Item-Based Collaborative Filtering

Finding Movie Similarities using Cosine Similarity

Improving the Results of Movie Similarities

Making Movie Recommendations with Item-Based Collaborative Filtering

Improve the recommender's results

More Data Mining and Machine Learning Techniques

K-Nearest-Neighbors: Concepts

Using KNN to predict a rating for a movie

Dimensionality Reduction; Principal Component Analysis (PCA)

PCA Example with the Iris data set

Data Warehousing Overview: ETL and ELT

Reinforcement Learning

Reinforcement Learning & Q-Learning with Gym

Understanding a Confusion Matrix

Measuring Classifiers (Precision, Recall, F1, ROC, AUC)

Dealing with Real-World Data

Bias/Variance Tradeoff

K-Fold Cross-Validation to avoid overfitting

Data Cleaning and Normalization

Cleaning web log data

Normalizing numerical data

Detecting outliers

Feature Engineering and the Curse of Dimensionality

Imputation Techniques for Missing Data

Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE

Binning, Transforming, Encoding, Scaling, and Shuffling

Apache Spark: Machine Learning on Big Data

Warning about Java 11 and Spark 3!

Spark installation notes for MacOS and Linux users

Installing Spark - Part 1

Installing Spark - Part 2

Spark Introduction

Spark and the Resilient Distributed Dataset (RDD)

Introducing MLLib

Introduction to Decision Trees in Spark

K-Means Clustering in Spark

TF / IDF

Searching Wikipedia with Spark

Using the Spark 2.0 DataFrame API for MLLib

Experimental Design / ML in the Real World

Deploying Models to Real-Time Systems

A/B Testing Concepts

T-Tests and P-Values

Hands-on With T-Tests

Determining How Long to Run an Experiment

A/B Test Gotchas

Deep Learning and Neural Networks

Deep Learning Pre-Requisites

The History of Artificial Neural Networks

Deep Learning in the Tensorflow Playground

Deep Learning Details

Introducing Tensorflow

Important note about Tensorflow 2

Using Tensorflow, Part 1

Using Tensorflow, Part 2

Introducing Keras

Using Keras to Predict Political Affiliations

Convolutional Neural Networks (CNN's)

Using CNN's for handwriting recognition

Recurrent Neural Networks (RNN's)

Using a RNN for sentiment analysis

Transfer Learning

Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters

Deep Learning Regularization with Dropout and Early Stopping

The Ethics of Deep Learning

Learning More about Deep Learning

Final Project

Your final project assignment: Mammogram Classification

Final project review

References

• https://conda.io/projects/conda/en/latest/user-guide/getting-started.html
• https://docs.anaconda.com/anaconda/install/linux/
• https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html
• https://stackoverflow.com/a/58068850/13709997
• https://pandas.pydata.org/pandas-docs/stable/reference/index.html
• https://numpy.org/doc/stable/reference/index.html
• https://matplotlib.org/3.3.3/api/pyplot_summary.html
• https://docs.scipy.org/doc/scipy/reference/stats.html
• https://seaborn.pydata.org/index.html

Main Source

• Sundog Education. "Machine Learning, Data Science and Deep Learning with Python." Udemy, Udemy, September 2020, www.udemy.com/course/data-science-and-machine-learning-with-python-hands-on/.