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Defination: Machine learning is the scientific study ofalgorithms andstatistical models thatcomputer systems use in order to perform aspecific task effectivelywithout using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence.When applying machine learning to real-world data, there are a lot of steps involved in the process -- starting with collecting the data and ending with generating predictions.

• Step 1: Gather the data In industry, there are important considerations you need to take into account when building a dataset, such as target.
• Step 2: Prepare the data Deal with missing values and categorical data. (Feature engineering,Feature Selection,Feature Transformation).
• Step 3: Select a model There are a lot of different types of models. Which one should you select based on Your business problem?
• Step 4: Train the model Fit Regression and Classifiaction models to patterns in training data.
• Step 5: Evaluate the model Use a validation set to assess how well a trained model performs on unseen data.
• Step 6: Tune parameters Tune parameters to get better performance from XGBoost models.
• Step 7: Get predictions Generate predictions with a trained model

scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license.

The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. See theAbout us <https://scikit-learn.org/dev/about.html#authors>__ page

for a list of core contributors.

It is currently maintained by a team of volunteers.

Website:https://scikit-learn.org

• Python (>= 3.6)

• NumPy (>= 1.13.3)

• SciPy (>= 0.19.1)

• joblib (>= 0.11)

• Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4.

• scikit-learn 0.23 and later require Python 3.6 or newer.

Scikit-learn plotting capabilities (i.e., functions start withplot_ and classes end with "Display") require Matplotlib (>= 2.1.1). For running the examples Matplotlib >= 2.1.1 is required. A few examples require scikit-image >= 0.13, a few examples require pandas >= 0.18.0, some examples require seaborn >= 0.9.0.

If you already have a working installation of numpy and scipy, the easiest way to install scikit-learn is usingpip ::

pip install -U scikit-learn

orconda::

conda install scikit-learn

The documentation includes more detailedinstallation instructions <https://scikit-learn.org/stable/install.html>_.

Scholeaofai

• Python
• Data Science With Python
• Machine Learning with Python
• Deep learning With python
• Data Visulization
• Life Cycle of Data Science
• Statistics