Currently Migrating into Quarto platform. See the progress on here :)

Posts

• 

  Regular expressions and word tokenization

  This chapter will introduce some basic NLP concepts, such as word tokenization and regular expressions to help parse text. You'll also learn how to handle non-English text and more difficult tokenization you might find. This is the Summary of lecture "Introduction to Natural Language Processing in Python", via datacamp.

  Jul 15, 2020

• 

  Cross Validation

  Holdout sets are a great start to model validation. However, using a single train and test set if often not enough. Cross-validation is considered the gold standard when it comes to validating model performance and is almost always used when tuning model hyper-parameters. This chapter focuses on performing cross-validation to validate model performance. This is the Summary of lecture "Model Validation in Python", via datacamp.

  Jul 14, 2020

• Selecting the best model with Hyperparameter tuning.

  The first three chapters focused on model validation techniques. In chapter 4 we apply these techniques, specifically cross-validation, while learning about hyperparameter tuning. After all, model validation makes tuning possible and helps us select the overall best model. This is the Summary of lecture "Model Validation in Python", via datacamp.

  Jul 14, 2020

• 

  Validation Basics

  This chapter focuses on the basics of model validation. From splitting data into training, validation, and testing datasets, to creating an understanding of the bias-variance tradeoff, we build the foundation for the techniques of K-Fold and Leave-One-Out validation practiced in chapter three. This is the Summary of lecture "Model Validation in Python", via datacamp.

  Jul 13, 2020

• Basic Modeling in scikit-learn

  Before we can validate models, we need an understanding of how to create and work with them. This chapter provides an introduction to running regression and classification models in scikit-learn. We will use this model building foundation throughout the remaining chapters. This is the Summary of lecture "Model Validation in Python", via datacamp.

  Jul 13, 2020

• Dealing with Messy Data

  This chapter introduces you to the reality of messy and incomplete data. You will learn how to find where your data has missing values and explore multiple approaches on how to deal with them. You will also use string manipulation techniques to deal with unwanted characters in your dataset. This is the Summary of lecture "Feature Engineering for Machine Learning in Python", via datacamp.

  Jul 12, 2020

• Creating Features

  In this chapter, you will explore what feature engineering is and how to get started with applying it to real-world data. You will load, explore and visualize a survey response dataset, and in doing so you will learn about its underlying data types and why they have an influence on how you should engineer your features. Using the pandas package you will create new features from both categorical and continuous columns. This is the Summary of lecture "Feature Engineering for Machine Learning in Python", via datacamp.

  Jul 12, 2020

• Dealing with Text Data

  Finally, in this chapter, you will work with unstructured text data, understanding ways in which you can engineer columnar features out of a text corpus. You will compare how different approaches may impact how much context is being extracted from a text, and how to balance the need for context, without too many features being created. This is the Summary of lecture "Feature Engineering for Machine Learning in Python", via datacamp.

  Jul 12, 2020

• 

  Conforming to Statistical Assumptions

  In this chapter, you will focus on analyzing the underlying distribution of your data and whether it will impact your machine learning pipeline. You will learn how to deal with skewed data and situations where outliers may be negatively impacting your analysis. This is the Summary of lecture "Feature Engineering for Machine Learning in Python", via datacamp.

  Jul 12, 2020

• Selecting features for modeling

  This chapter goes over a few different techniques for selecting the most important features from your dataset. You'll learn how to drop redundant features, work with text vectors, and reduce the number of features in your dataset using principal component analysis (PCA). This is the Summary of lecture "Preprocessing for Machine Learning in Python", via datacamp.

  Jul 10, 2020

• Preprocessing - Putting it all together

  Now that you've learned all about preprocessing you'll try these techniques out on a dataset that records information on UFO sightings. This is the Summary of lecture "Preprocessing for Machine Learning in Python", via datacamp.

  Jul 10, 2020

• Standardizing Data

  This chapter is all about standardizing data. Often a model will make some assumptions about the distribution or scale of your features. Standardization is a way to make your data fit these assumptions and improve the algorithm's performance. This is the Summary of lecture "Preprocessing for Machine Learning in Python", via datacamp.

  Jul 9, 2020

• Feature Engineering

  In this section you'll learn about feature engineering. You'll explore different ways to create new, more useful, features from the ones already in your dataset. You'll see how to encode, aggregate, and extract information from both numerical and textual features. This is the Summary of lecture "Preprocessing for Machine Learning in Python", via datacamp.

  Jul 9, 2020

• Introduction to Data Preprocessing

  In this chapter you'll learn exactly what it means to preprocess data. You'll take the first steps in any preprocessing journey, including exploring data types and dealing with missing data. This is the Summary of lecture "Preprocessing for Machine Learning in Python", via datacamp.

  Jul 9, 2020

• 

  Feature extraction

  This chapter is a deep-dive on the most frequently used dimensionality reduction algorithm, Principal Component Analysis (PCA). You'll build intuition on how and why this algorithm is so powerful and will apply it both for data exploration and data pre-processing in a modeling pipeline. You'll end with a cool image compression use case. This is the Summary of lecture "Dimensionality Reduction in Python", via datacamp.

  Jul 9, 2020