Gain insights into data science fundamentals, explore machine learning and big data, and delve into real-time projects. Discover systematic approaches to data acquisition, wrangling, and solving diverse problems.

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There is a lot of dispersed, and somewhat conflicting information on the internet when it comes to data science, making it tough to know where to start. Don't worry. 

This course will get you familiar with the state of data science and the related fields such as machine learning and big data. You will be going through the fundamental concepts and libraries which are essential to solve any problem in this field. 

You will work on real-time projects from Kaggle while also honing your mathematical skills which will be used extensively in most problems you face. You will also be taken through a systematic approach to learning about data acquisition to data wrangling and everything in between. This is your all-in-one guide to becoming a confident data scientist.

An Introductory Guide to Data Science and Machine Learning



## Dummy Regressors

In Machine Learning, **we start working on the problem, by developing a baseline model** and then improving upon the model using other models or other Feature Selection or Engineering Techniques. There is a famous saying 

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<center> 

**All models are wrong, but some are useful.**

</center>

---

Scikit Learn provides us with `DummyRegressor`, which involves using simple rules to make predictions and developing a baseline model for the regression problem at hand. It is not recommended for real time problems. The `DummyRegressor` class takes in a parameter `str`, which stands for strategy. This parameter decides which technique to use for making predictions. It can take one of the following values: 


* **"Mean"**: Always predicts the mean of the training targets.

* **"Median"**: Always predicts the median of the training targets.

* **"Quantile"**: always predicts a user-provided quantile of the training targets.

* **"Constant"**: always predicts a constant value that is provided by the user.

In all these strategies, the model's predict method completely ignores the input data.






# Dummy Regressors

In Machine Learning, **we start working on the problem, by developing a baseline model** and then improving upon the model using other models or other Feature Selection or Engineering Techniques. There is a famous saying 

---


<center> 

**All models are wrong, but some are useful.**

</center>

---

Scikit Learn provides us with `DummyRegressor`, which involves using simple rules to make predictions and developing a baseline model for the regression problem at hand. It is not recommended for real time problems. The `DummyRegressor` class takes in a parameter `str`, which stands for strategy. This parameter decides which technique to use for making predictions. It can take one of the following values: 


* **"Mean"**: Always predicts the mean of the training targets.

* **"Median"**: Always predicts the median of the training targets.

* **"Quantile"**: always predicts a user-provided quantile of the training targets.

* **"Constant"**: always predicts a constant value that is provided by the user.

In all these strategies, the model's predict method completely ignores the input data.




Learn about Dummy Regressors, which enable us to build baseline Regression models.  

What is Data Science ?

Applications of Data Science

Overview of Libraries

Probability and Statistics

Machine Learning Part-1

Machine Learning Part-2

Machine Learning Part-3

Deep Learning

Machine Learning Tools and Libraries

Big Data Tools and Technologies

Where to go next ?

Dummy Regressors

Dummy Regressors