anhnguyendepocen / advancedmethodsdataanalysisclass-1

Advanced Methods in Data Analysis

Instructor: Alejandro Correa Bahnsen

• email: [email protected]
• twitter: @albahnsen
• github: albahnsen

The use of statistical models in computer algorithms allows computers to make decisions and predictions, and to perform tasks that traditionally require human cognitive abilities. Machine learning is the interdisciplinary field at the intersection of statistics and computer science which develops such algorithms and interweaves them with computer systems. It underpins many modern technologies, such as speech recognition, internet search, bioinformatics, computer vision, Amazon’s recommender system, Google’s driverless car and the most recent imaging systems for cancer diagnosis are all based on Machine Learning technology.

This course on Time Series Analysis, Machine Learning and Natural Language Processing will explain how to build systems that learn and adapt using real-world applications. Some of the topics to be covered include time series analysis, machine learning, python data analysis, natural language processing models and recurrent models. The course will be project-oriented, with emphasis placed on writing software implementations of learning algorithms applied to real-world problems, in particular, churn modeling, natural language processing, sentiment detection, among others.

Requiriments

• Python version 3.7;
• Numpy, the core numerical extensions for linear algebra and multidimensional arrays;
• Scipy, additional libraries for scientific programming;
• Matplotlib, excellent plotting and graphing libraries;
• IPython, with the additional libraries required for the notebook interface.
• Pandas, Python version of R dataframe
• Seaborn, used mainly for plot styling
• scikit-learn, Machine learning library!

A good, easy to install option that supports Mac, Windows, and Linux, and that has all of these packages (and much more) is the Anaconda.

GIT!! Unfortunatelly out of the scope of this class, but please take a look at these tutorials

Evaluation

• 75% Projects (3 projects, 25% each)
• 15% Exercises
• 10% Class participation

Schedule

Time Series Analysis

Date	Session	Notebooks/Presentations	Exercises
June 16th	ARIMA Processes	1 - Intro to TSA 2 - ARIMA processes	E1 - TSA Applications E2 - Python TSA Analysis E3 - ARIMA
June 18th	Working with TSA	3 - Prophet	E4 - Panel Data E5 - Prophet P1 - TSA

Machine Learning Systems

Date	Session	Notebooks/Presentations	Exercises
June 23rd	Decision Trees & Ensembles	4 - Decision Trees 5 - Bagging	E6 - DT E7 - DT & Ensembles
June 24th	Random Forest and XGBoost	6 - Random Forests 7 - XGBoost	E8 - Ensembles E9 - Random Forest & Gradient Boosting
June 25th	Machine Learning as a Service	8 Introduction to Rest APIs 9 - Model Deployment 10 - APIs in AWS	E10 - API Review P2 - Trees and APIs

Natural Language Processing

Date	Session	Notebooks/Presentations	Exercises
June 30th	Natural Language Processing	11 - Introduction to NLP 12 - Introduction to NLP II	E12 - NLP Review E13 - Sentiment Analysis
July 1st	Sentiment Analysis	13 - Sentiment Analysis	E14 - Homeworks Analysis
July 2nd	NLP using Neural Networks	14 - Introduction to NN - MLP 15 - Keras 16 - LSTM	P3 - NLP Movies Analysis API