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

# Learning Curves

In the previous lessons, we trained our model on the training dataset and tested the trained model on the testing dataset. In the lessons on Regression Problems, we introduced another dataset named validation dataset. This validation dataset is also called a development dataset, as it is used during the development of a model. We have covered the intuition behind using the validation dataset. Let’s look at some Learning Curve plots that illustrate the model’s performance on the training and validation datasets and help avoid overfitting and other problems. Consider: how long should the model be trained? What is the size of the training dataset that we should be using? The model’s state can be evaluated on the training dataset to let us know how well the model is learning. The state of the model can be evaluated on the validation dataset to let us know how well the model is generalizing.


## Learning curve (Impact of the size of training dataset)


We train our models using different sizes ($m$) of the **training dataset** and record the **error** on the **validation dataset** and the **training dataset**. Then we will plot the respective errors (training and validation) against the training set size $m$. 


* Validation error decreases as the number of instances, m, in the training dataset increase.
* Training error increases as the number of instances, m, in the training dataset increase.
* The goal would be to achieve the desired performance by increasing the training dataset. If the curve flattens out before achieving the desired performance, there would be no advantage to collecting more training datasets to improve the performance.
 



Learning Curves help us evaluate the performance of the model and make necessary decisions. This lesson covers concepts about Learning Curves.



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 ?

Learning Curves

Learning Curves