**Question 1: What is Machine Learning ? **

Answer: Machine Learning is a subset of **artificial intelligence.** In simple terms we study computer algorithms in machine learning and performance of these algorithms **depends on type and amount of data **(used to train a machine learning model). And these algorithms learn with past experiences which help in improvement of the performance of a computer program like human being.

Example: In robotics, to make future prediction based on available data, self driving car etc...

**Question 2: Types of Machine Learning ? **

Answer: When we are talking about types of machine learning, we are basically talking about different ways in which a machine can learn.

Basically there are three ways in which machine learn:

- Supervised machine learning
- Unsupervised machine learning
- Reinforcement machine learning

**Question 3: How these three type of machine learning algorithms differ each other ?**

**Answer :**

Supervised Learning | Unsupervised Learning | Reinforcement Learning |
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Machine learn with labelled data under external supervision. For example; teaching a child what is a apple and what is banana. | Machine learn with unlabelled data under minimum supervision . for example; Let's take a case where no one to tell a child what is the difference between an apple and a banana ,In this case, he will find out by looking at the patterns of things himself and will teach himself by recognising pattern. | This the most advance learning in machine learning . In this case an agent interact with unknown environment and learn through trials and errors using previous feedbacks. For example self driving car, in NLP. A simple example; In ancient time evolution of human from an animal where he learn from his trial and punishment method. |

Problems related to Regression and Classification | Problems Related to association and clustering | Negative and Positive Reinforce |

**Note:** It is true that reinforcement learning is best out of three but it is difficult to deal with reinforcement learning in practical. Or learn it when you mastered the basic learnings.

**Further Classification of supervised and unsupervised machine learning:**

**Question 4: Python Libraries need to be know to start with Machine Learning ?**

**Answer: **

**Pandas : Pandas full tutorial **

Python Pandas library is a fast , flexible and easy to use library to manipulate ,merging, sorting data for data analysis.

**Numpy: Numpy Tutorial **

Numpy library in python deals with all mathematical and scientific computing include all matrix/ array calculations.

**Matplotlib:** **Matplotlib tutorial **

Matplotlib library is a plotting library in python. We can make static plots using maplotlib but not interactive.

Advance plotting libraries: **Plotly , Seaborn , Folium Library , Geopandas (**geospatial data**)**

**Scipy: Scipy Library tutorial **

Scipy in a computation library that uses NumPy. Basically advance form of Numpy.

**scikit or sklearn : **

Scikit-learn (known as sklearn) is a free software machine learning library for the Python programming language. Basically we can directly use some features of machine learning (for example: regression , classification etc..) and also can download some famous datasets using **sklearn **directly.

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