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Posts

  • Exploration in Biomedical Image Analysis

    Prepare to conquer the Nth dimension! To begin the course, you'll learn how to load, build and navigate N-dimensional images using a CT image of the human chest. You'll also leverage the useful ImageIO package and hone your NumPy and matplotlib skills. This is the Summary of lecture "Biomedical Image Analysis in Python", via datacamp.

    Aug 13, 2020

  • Feature Engineering

    You will now get exposure to different types of features. You will modify existing features and create new ones. Also, you will treat the missing data accordingly. This is the Summary of lecture "Winning a Kaggle Competition in Python", via datacamp.

    Aug 12, 2020

  • Modeling

    Time to bring everything together and build some models! In this last chapter, you will build a base model before tuning some hyperparameters and improving your results with ensembles. You will then get some final tips and tricks to help you compete more efficiently. This is the Summary of lecture "Winning a Kaggle Competition in Python", via datacamp.

    Aug 12, 2020

  • Dive into the Competition

    Now that you know the basics of Kaggle competitions, you will learn how to study the specific problem at hand. You will practice EDA and get to establish correct local validation strategies. You will also learn about data leakage. This is the Summary of lecture "Winning a Kaggle Competition in Python", via datacamp.

    Aug 12, 2020

  • Kaggle competitions process

    In this first chapter, you will get exposure to the Kaggle competition process. You will train a model and prepare a csv file ready for submission. You will learn the difference between Public and Private test splits, and how to prevent overfitting. This is the Summary of lecture "Winning a Kaggle Competition in Python", via datacamp.

    Aug 12, 2020

  • Regression in PySpark

    Next you'll learn to create Linear Regression models. You'll also find out how to augment your data by engineering new predictors as well as a robust approach to selecting only the most relevant predictors. This is the Summary of lecture "Machine Learning with PySpark", via datacamp.

    Aug 11, 2020

  • Object Recognition with ALL-CNN

    In this project, it will show CNN model for object recognition. The Original Paper is "Striving for simplicity - The all convolutional net" from ICLR 2015.

    Aug 11, 2020

  • Ensembles and Pipelines in PySpark

    Finally you'll learn how to make your models more efficient. You'll find out how to use pipelines to make your code clearer and easier to maintain. Then you'll use cross-validation to better test your models and select good model parameters. Finally you'll dabble in two types of ensemble model. This is the Summary of lecture "Machine Learning with PySpark", via datacamp.

    Aug 11, 2020

  • Machine Learning with PySpark - Introduction

    Spark is a framework for working with Big Data. In this chapter you'll cover some background about Spark and Machine Learning. You'll then find out how to connect to Spark using Python and load CSV data.

    Aug 10, 2020

  • Classification in PySpark

    Now that you are familiar with getting data into Spark, you'll move onto building two types of classification model - Decision Trees and Logistic Regression. You'll also find out about a few approaches to data preparation. This is the Summary of lecture "Machine Learning with PySpark

    Aug 10, 2020

  • Model tuning and selection in PySpark

    In this last chapter, you'll apply what you've learned to create a model that predicts which flights will be delayed. This is the Summary of lecture "Introduction to PySpark", via datacamp.

    Aug 10, 2020

  • Getting started with machine learning pipelines in PySpark

    PySpark has built-in, cutting-edge machine learning routines, along with utilities to create full machine learning pipelines. You'll learn about them in this chapter. This is the Summary of lecture "Introduction to PySpark", via datacamp.

    Aug 9, 2020

  • Manipulating data in PySpark

    In this chapter, you'll learn about the pyspark.sql module, which provides optimized data queries to your Spark session. This is the Summary of lecture "Introduction to PySpark", via datacamp.

    Aug 9, 2020

  • Getting to know PySpark

    In this chapter, you'll learn how Spark manages data and how can you read and write tables from Python. This is the Summary of lecture "Introduction to PySpark", via datacamp.

    Aug 7, 2020

  • Policy Gradient with gym-MiniGrid

    In this session, it will show the pytorch-implemented Policy Gradient in Gym-MiniGrid Environment. Through this, you will know how to implement Vanila Policy Gradient (also known as REINFORCE), and test it on open source RL environment.

    Aug 6, 2020