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Posts

  • Exploring Linear Trends

    We start the course with an initial exploration of linear relationships, including some motivating examples of how linear models are used, and demonstrations of data visualization methods from matplotlib. We then use descriptive statistics to quantify the shape of our data and use correlation to quantify the strength of linear relationships between two variables. This is the Summary of lecture "Introduction to Linear Modeling in Python", via datacamp.

    Jun 19, 2020

  • Validating and Inspecting Time Series Models

    Once you've got a model for predicting time series data, you need to decide if it's a good or a bad model. This chapter coves the basics of generating predictions with models in order to validate them against "test" data. This is the Summary of lecture "Machine Learning for Time Series Data in Python", via datacamp.

    Jun 18, 2020

  • Predicting Time Series Data

    If you want to predict patterns from data over time, there are special considerations to take in how you choose and construct your model. This chapter covers how to gain insights into the data before fitting your model, as well as best-practices in using predictive modeling for time series data. This is the Summary of lecture "Machine Learning for Time Series Data in Python", via datacamp.

    Jun 18, 2020

  • Temporal Difference Learning TD(λ)

    A summary of "Understanding Deep Reinforcement Learning"

    Jun 17, 2020

  • Temporal Difference Learning TD(0)

    A summary of "Understanding Deep Reinforcement Learning"

    Jun 17, 2020

  • Time Series as Inputs to a Model

    The easiest way to incorporate time series into your machine learning pipeline is to use them as features in a model. This chapter covers common features that are extracted from time series in order to do machine learning. This is the Summary of lecture "Machine Learning for Time Series Data in Python", via datacamp.

    Jun 17, 2020

  • Time Series and Machine Learning Primer

    This chapter is an introduction to the basics of machine learning, time series data, and the intersection between the two. This is the Summary of lecture "Machine Learning for Time Series Data in Python", via datacamp.

    Jun 17, 2020

  • Monte Carlo Policy Evaluation

    A summary of "Understanding Deep Reinforcement Learning"

    Jun 16, 2020

  • Seasonal ARIMA Models

    In this final chapter, you'll learn how to use seasonal ARIMA models to fit more complex data. You'll learn how to decompose this data into seasonal and non-seasonal parts and then you'll get the chance to utilize all your ARIMA tools on one last global forecast challenge. This is the Summary of lecture "ARIMA Models in Python", via datacamp.

    Jun 16, 2020

  • The Best of the Best Models

    In this chapter, you will become a modeler of discerning taste. You'll learn how to identify promising model orders from the data itself, then, once the most promising models have been trained, you'll learn how to choose the best model from this fitted selection. You'll also learn a great framework for structuring your time series projects. This is the Summary of lecture "ARIMA Models in Python", via datacamp.

    Jun 16, 2020

  • Fitting the Future with time series analysis

    What lies ahead in this chapter is you predicting what lies ahead in your data. You'll learn how to use the elegant statsmodels package to fit ARMA, ARIMA and ARMAX models. Then you'll use your models to predict the uncertain future of stock prices! This is the Summary of lecture "ARIMA Models in Python", via datacamp.

    Jun 15, 2020

  • Monte Carlo method - Intro

    A summary of "Understanding deep reinforcement learning"

    Jun 15, 2020

  • ARMA Models

    Dive straight in and learn about the most important properties of time series. You'll learn about stationarity and how this is important for ARMA models. You'll learn how to test for stationarity by eye and with a standard statistical test. Finally, you'll learn the basic structure of ARMA models and use this to generate some ARMA data and fit an ARMA model. This is the Summary of lecture "ARIMA Models in Python", via datacamp.

    Jun 15, 2020

  • Case Study in time series analysis

    This chapter will give you a chance to practice all the concepts covered in the course. You will visualize the unemployment rate in the US from 2000 to 2010. This is the Summary of lecture "Visualizing Time-Series data in Python", via datacamp.

    Jun 14, 2020

  • Work with Multiple Time Series

    In the field of Data Science, it is common to be involved in projects where multiple time series need to be studied simultaneously. In this chapter, we will show you how to plot multiple time series at once, and how to discover and describe relationships between multiple time series. This is the Summary of lecture "Visualizing Time-Series data in Python", via datacamp.

    Jun 13, 2020