Скачать или смотреть Python for Machine Learning - Part 50 - Supervised | Unsupervised | Semi-Supervised Machine Learning

Supervised learning is where you have input variables (x) and an output variable (Y) and
you use an suitable algorithm to train the modle and predict the output variable

we use Regression and Classification model for supervised learning

The output of Regression Algorithm is a continous variables like the salary or the outsales or

Whereas the output of Classification is a discrete number
1. where a person died or survived in a ship
2. Whether the loan will be passed or not
3. Whether India will win or not


Unsupervised is something in which we have the input variable and we dont know the output variable

We use algorithms for unsupervised learning

Basically we use clustering algorithms like KMeans / H.C / Apriori to cluster the data and get the insghts.

These are called unsupervised learning because unlike supervised learning above there is no correct answers
and there is no teacher. Algorithms are left to their own devises to discover and present the interesting
structure in the data.


Unsupervised learning problems can be further grouped into clustering and association problems.

Clustering: A clustering problem is where you want to discover the pattern in the data,
such as grouping fishes based on the length, breadth, weight OR
grouping the grain using its dimensions
Association: Association rule learning is a rule-based machine learning method for discovering interesting
relations between variables in large databases.
It is intended to identify strong rules discovered in databases using some measures of
interestingness.


Semi-Supervised Machine Learning

Problems where you have a large amount of input data (X) and only some of the data is labeled (Y)
are called semi-supervised learning problems.

These problems lies in between both supervised and unsupervised learning.

A good example is a photo archive where only some of the images are labeled, (e.g. apple, tomato, oranges)
and the majority are unlabeled.

Many real world machine learning problems fall into this area.
This is because it can be expensive or time-consuming to label
data as it may require access to domain experts. Whereas unlabeled data is cheap and easy to collect and store.

Since it can be expensive and time-consuming to label the data as it requires domain experts, so many
real world machine learning problems fall into this area.
so its cheap and easy to collect and store the unlabelled data.

All Playlist of this youtube channel
====================================

1. Data Preprocessing in Machine Learning
   • Data Preprocessing in Machine Learning| Li...  

2. Confusion Matrix in Machine Learning, ML, AI
   • Confusion Matrix in Machine Learning, ML, AI  

3. Anaconda, Python Installation, Spyder, Jupyter Notebook, PyCharm, Graphviz
   • Anaconda | Python Installation | Spyder | ...  

4. Cross Validation, Sampling, train test split in Machine Learning
   • Cross Validation | Sampling | train test s...  

5. Drop and Delete Operations in Python Pandas
   • Drop and Delete Operations in Python Pandas  

6. Matrices and Vectors with python
   • Matrices and Vectors with python  

7. Detect Outliers in Machine Learning
   • Detect Outliers in Machine Learning  

8. TimeSeries preprocessing in Machine Learning
   • TimeSeries preprocessing in Machine Learning  

9. Handling Missing Values in Machine Learning
   • Handling Missing Values in Machine Learning  

10. Dummy Encoding Encoding in Machine Learning
   • Label Encoding, One hot Encoding, Dummy En...  

11. Data Visualisation with Python, Seaborn, Matplotlib
   • Data Visualisation with Python, Matplotlib...  

12. Feature Scaling in Machine Learning
   • Feature Scaling in Machine Learning  

13. Python 3 basics for Beginner
   • Python | Python 3 Basics | Python for Begi...  

14. Statistics with Python
   • Statistics with Python  

15. Sklearn Scikit Learn Machine Learning
   • Sklearn Scikit Learn Machine Learning  

16. Python Pandas Dataframe Operations
   • Python Pandas Dataframe Operations  

17. Linear Regression, Supervised Machine Learning
   • Linear Regression | Supervised Machine Lea...