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

# Semi-supervised learning 

Supervised learning involves the usage of labelled data and unsupervised learning works without labelled data. **Semi-supervised learning** lies between the two fields. It makes use of both labelled and unlabelled dataset. We looked into the technique of **Pseudo-Labeling** in the initial lesson of this chapter. 

Labeling data is a costly process. *The biggest benefit of semi-supervised learning is that it requires a small amount of labelled data.* In most of cases labelled data is very short, and we can leverage semi-supervised Learning to increase the amount of labelled data.


# Assumptions about the data

Semi-supervised learning makes the following assumption about the dataset at hand: 

* **Continuity Assumption**: 
The Continuity Assumption states that points that are close to each other are more likely to share label.

* **Cluster Assumption**: 
The Cluster Assumption states that data forms a discrete cluster, and points lying in the same cluster are more likely to share a label.

* **Manifold Assumption**: 
It states that data lies on a manifold of a much lower dimension than the input space.


# Applications of semi-supervised learning 

* Text Classification is a natural application of semi-supervised learning as we don't have a large amount of labelled text to train a classifier. The same goes for Speech Analysis.

* Semi-supervised Learning is used extensively in the field of Bioinformatics, where we process large DNA strands. 


# Pseudo Labeling 

One great type of semi-supervised learning is **Pseudo-Labeling**, and participants in kaggle competitions also use them extensively. 

Pseudo Labeling involves the following steps. 

1. First, we train different models on the training dataset and choose the one that gives us the best results. 

2. Next, we use the model trained in Step 1 to predict the test dataset. We don't know if these predictions(**pseudo-labels**) are correct but we do know that we have quite accurate labels because the model performed well on the training dataset. 

3. In step 3, we combine the training and the pseudo-labelled test datasets. Then again, train the model as we did in step 1. This is how pseudo labeling works. 

The above steps are also referred to as **self-training** in literature. 






Semi-supervised Learning is a class of Machine Learning that involves using both labelled and unlabelled data for learning problems. We will dive into the details of this concept in this lesson.


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 ?

Semi-Supervised Learning Techniques

Semi-supervised learning