Resource for Machine Learning

Comprehensive Guides

1. A Complete Tutorial to learn Data Science in R from Scratch

For a complete beginner in R, if there is one resource you can read – read this resource. The article assumes no background in machine learning, provides basics of R, performs exploratory analysis & data manipulation on a dataset and ends up with building a predictive model on a dataset. I assure you this is one of the best hands-on guides to learn data science & machine learning in R.

Tool: R

Techniques: Complete case study on a dataset

Level: Beginner

 

2. A Complete Tutorial to learn Data Science in Python from Scratch

If you want to start your machine learning and data science journey in Python, this is the place to start. The guide assumes no prior knowledge in Python. It starts with basics of Python language, provides details of popular libraries in data science and data structures in Python. Once the basics are covered, a case study is used to show data exploration, data munging and predictive model building.

Tool: Python

Techniques: Complete case study on a dataset including Logistics Regression, Decision Tree and Random Forest.

Level: Beginner

 

 

3. A Complete Tutorial on Tree Based Modeling from Scratch (in R & Python)

This guide will teach you Tree based algorithms from scratch. Algorithms like decision tree, random forest and gradient boosting are widely used to solve several data science problems. Hence, it is important for any analyst to have a thorough understanding of them. In this guide, you will learn about these algorithms and how they are being used in modeling. This guide assumes no prior knowledge of machine learning, but one must have familiarity with R or Python.

Tools: R & Python

Techniques: Tree based algorithms

Level: Intermediate

 

4. A Comprehensive beginner’s guide to Create Time Series Forecast (with Codes in Python)

Time series is an important concept in data science. This guide will walk you through various techniques of time series with end-to-end problem solving along with codes in Python. You will learn about what makes time series special, loading & handling time series in Pandas, how to check stationarity of time series, how to make time series stationary and forecasting a time series. By the end of this guide, you will be able to forecast using time series techniques.

Tools: Python

Techniques: Time series forecasting

Level: Intermediate

 

5. Practical Guide to Principal Component Analysis (PCA) in R & Python

Sometimes you might come across a dataset which happens to have too many variables. To find right variables for computation purpose can be both confusing and cumbersome. To tackle this problem you have Principal Component Analysis (PCA) at your rescue. Principal component analysis is a method of extracting important variables from a large set. In this guide, you will learn what are principle components, normalization of variables, implementation of PCA in R or Python and predictive modeling using PCA. This guide assumes some prior knowledge of statistics.

Tools: R & Python

Techniques: Principal Component Analysis

Level: Intermediate

 

6. Complete Guide to Parameter Tuning in XGBoost (with codes in Python)

XGBoost is considered as one of the most powerful algorithms by any data scientist. Building a model using XGBoost is easy but to improve the accuracy of the model using XGBoost can be a challenging. Here is a guide for you on parameter tuning using XGBoost in Python. You will learn about the advantages of using XGBoost, various parameters of XGBoost and tuning parameters using examples. One must have working knowledge of Python for data science for this guide.

Tools: Python

Techniques: XGBoost

Level: Intermediate

 

7. A Complete Tutorial on Ridge and Lasso Regression in Python

People often restrict their understanding of regression to only linear and logistic regression. But regression is much more than that. This is a complete guide on Ridge and Lasso regression, which use fundamental regularization techniques. In this guide you will learn about the intricacies of Ridge and Lasso regression techniques, peep into the statistics behind dealing with a regression problem and the advantages of using Ridge & Lasso over linear regression. I am certain by the end of this guide you will be able to use ridge and lasso regression in action.

Tools: Python

Techniques: Ridge & Lasso regression

Level: Intermediate

 

8. Complete Guide to Parameter Tuning in Gradient Boosting (GBM) in Python

Gradient Boosting algorithms are easy to apply, but difficult to tune. This guide will take you through the science behind using GBM in Python. You will learn how boosting works, GBM parameters and hands-on experience for tuning parameters using machine learning problem dataset. After you have a basic understanding of parameter tuning in GBM, the guide will also walk you through the general approach for parameter tuning.

Tools: Python

Techniques: Gradient Boosting Model

Level: Intermediate

 

9. A Comprehensive Guide to Data Exploration

Your predictive models can only be as good as your understanding of the data. Data exploration helps you understand the domain, build those awesome features and marry your domain thinking with the data. This guide teaches you the steps for data exploration & preparation, missing value treatment, techniques of outlier detection & treatment and art of feature engineering. I bet with the help of this guide you will be able to improve your model performance in the next machine learning competition.

Tools: Agnostic

Techniques: Exploratory Data Analysis, Missing value imputation, Outlier detection

Level: Beginner

 

10. A Comprehensive beginner’s guide to start ML with Amazon Web Services (AWS)

Cloud computation is an integral part of any data scientist work flow. If you have to handle data which is much larger than what your laptop / desktop can handle – cloud is the way to go. Here’s a complete guide on how to use AWS. This guide will make you familiar with the terminologies used and the interface of AWS. Then you will learn how to configure and launch an instance.

Once you are familiar with how AWS works, it’s time to build your first machine learning model on AWS using Python. The guide will also be helpful for any R user, all you have to do is change the line of codes. By following this guide you can start building models on AWS.

Tools: R & Python, Cloud

Techniques: NA

Level: Beginner