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



# Hyper-Parameter Optimization

Choosing the optimal value of hyper-parameters, like below involves investigations:

* Learning rate $\alpha$.
* Regularization Parameter $\lambda$.
* $k$ in Nearest Neighbor Regression.
* Hyper-parameters meant for classification algorithms. 

We investigate to find the best score on the training and the test sets. In the classification chapter, we will be looking into the automated techniques that give us the best values of the hyper-parameters above.



# Evaluating models using Learning Curves

In Machine Learning we use the Learning Curves to assess the performance of the models as the epochs increase and while training the model. **Epoch** is one whole pass of the training Dataset. There are certain curves which help us know the model's performance and choose the optimal numbers of epochs. We will look into the details of these Learning Curves in the Classification chapter. 


# Dealing with overfitting and underfitting

Overfitting and underfitting are the problems that a Data Scientist comes across more often and there are certain tips and tricks for dealing with them. We will look into the details in the Classification chapter.


# Kaggle competition

We will be looking into the Kaggle Competition "House Prices: Advanced Regression Techniques". We will apply the concepts that you have learned so far and build the model. Then, we will submit our model to the competition. 


In this lesson, we will be looking into different concepts related to Machine Learning and we will also be participating in a Kaggle competition. 

Case Study: House Prices Prediction using Advanced Regression

In this lesson, we will be looking into different concepts related to Machine Learning and we will also be participating in a Kaggle competition.

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 ?

Case Study: House Prices Prediction using Advanced Regression

Hyper-Parameter Optimization