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.
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.
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.
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
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.
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.
In this final chapter you will be given a taste of more advanced hyperparameter tuning methodologies known as ''informed search''. This includes a methodology known as Coarse To Fine as well as Bayesian & Genetic hyperparameter tuning algorithms. You will learn how informed search differs from uninformed search and gain practical skills with each of the mentioned methodologies, comparing and contrasting them as you go. This is the Summary of lecture "Hyperparameter Tuning in Python", via datacamp.
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.
In this chapter you will be introduced to another popular automated hyperparameter tuning methodology called Random Search. You will learn what it is, how it works and importantly how it differs from grid search. You will learn some advantages and disadvantages of this method and when to choose this method compared to Grid Search. You will practice undertaking a Random Search with Scikit Learn as well as visualizing & interpreting the output. This is the Summary of lecture "Hyperparameter Tuning in Python", via datacamp.
This chapter introduces you to a popular automated hyperparameter tuning methodology called Grid Search. You will learn what it is, how it works and practice undertaking a Grid Search using Scikit Learn. You will then learn how to analyze the output of a Grid Search & gain practical experience doing this. This is the Summary of lecture "Hyperparameter Tuning in Python", via datacamp.
In this introductory chapter you will learn the difference between hyperparameters and parameters. You will practice extracting and analyzing parameters, setting hyperparameter values for several popular machine learning algorithms. Along the way you will learn some best practice tips & tricks for choosing which hyperparameters to tune and what values to set & build learning curves to analyze your hyperparameter choices. This is the Summary of lecture "Hyperparameter Tuning in Python", via datacamp.