➦ But What is Machine Learning?

Machine learning is an Application of AI(Artificial Intelligence) which gives a system to learn. Machine Learning is mainly used for the development of computer programs to use data for learning. Nowadays many companies are using machine learning stuff to increase working speed and decrease human efforts.

In small and simple example, machine learning can be described as, “One small baby and some different colors balls are kept in a room, the baby will pick up any ball. The baby picks up red color ball and it's mother says this is a red ball. This time the details of the ball like its color and shape get stored in that baby's brain that it is a red ball. After this incident, if the baby is asked for the second time to pick up red ball from random color balls, he will be able to pick it up easily because this time those details are already stored in that baby's brain. Using this details, baby will find the perfect one.”

➦ Machine Learning Model

- P : Improve their performance
- T : Executing some Task
- E : Over Time with experiences

- Object Recognition
- Summarization
- Prediction
- Classification
- Clustering
- Recommender systems
- and many more..

➦ Requirement

- Python
- Anaconda
- Jupyter

- Python libraries

- Numpy
- Scipy
- Matplotlib
- Scikit-learn
- Pandas

import sysprint('Python: {}'.format(sys.version))import scipyprint('scipy: {}'.format(scipy.__version__))import numpyprint('numpy: {}'.format(numpy.__version__))import matplotlibprint('matplotlib: {}'.format(matplotlib.__version__))import pandasprint('pandas: {}'.format(pandas.__version__))import sklearnprint('sklearn: {}'.format(sklearn.__version__))

Single-dimensional Array:import numpy as nparr=np.array([1,2,3])print(arr)Output:[1 2 3]

Multi-dimensional Array:import numpy as nparr=np.array([(1,2,3),(4,5,6)])print(a)Output:[[ 1 2 3][4 5 6]]

import numpy as npimport timeimport sysS= range(1000)print(sys.getsizeof(5)*len(S))D= np.arange(1000)print(D.size*D.itemsize)Output:140004000

➦ Machine Learning Algorithms

**Supervised Learning algorithms**

- Linear Regression
- Logistic Regression
- K-Nearest Neighbors
- Decision Tree
- Random Forest
- Support Vector Machine

**Unsupervised Learning algorithms**

- Dimension Reduction
- Density Estimation
- Market Basket Analysis
- Generative adversarial networks
- Clustering

➦Conclusion:

**What we covered:**

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