Machine-learning-mastery-with-python

In this repository you can see the coding examples form book machine learning mastery with python by Jason Brownlee

A predictive machine learning can be broken down into 6 parts.

1. Define Problem: Investigate and characterize the problem in order to better understand the goals of the project.
2. Analyze Data: Use descriptive statistics and visualization to better understand the data you have available.
3. Prepare Data: Use data transforms in order to better expose the structure of the prediction problem to modeling algorithms.
4. Evaluate Algorithms: Design a test harness to evaluate a number of standard algorithms on the data and select the top few to investigate further.
5. Improve Results: Use algorithm tuning and ensemble methods to get the most out of well-performing algorithms on your data.
6. Present Results: Finalize the model, make predictions and present results.

lesson plan:

1. Lesson 1: Python Ecosystem for Machine Learning.
2. Lesson 2: Python and SciPy Crash Course.
3. Lesson 3: Load Datasets from CSV.
4. Lesson 4: Understand Data With Descriptive Statistics. (Analyze Data)
5. Lesson 5: Understand Data With Visualization. (Analyze Data)
6. Lesson 6: Pre-Process Data. (Prepare Data)
7. Lesson 7: Feature Selection. (Prepare Data)
8. Lesson 8: Resampling Methods. (Evaluate Algorithms)
9. Lesson 9: Algorithm Evaluation Metrics. (Evaluate Algorithms)
10. Lesson 10: Spot-Check Classification Algorithms. (Evaluate Algorithms)
11. Lesson 11: Spot-Check Regression Algorithms. (Evaluate Algorithms)
12. Lesson 12: Model Selection. (Evaluate Algorithms)
13. Lesson 13: Pipelines. (Evaluate Algorithms)
14. Lesson 14: Ensemble Methods. (Improve Results)
15. Lesson 15: Algorithm Parameter Tuning. (Improve Results)
16. Lesson 16: Model Finalization. (Present Results)

outcomes from reading this book

1. How to work through a small to medium sized dataset end-to-end.
2. How to deliver a model that can make accurate predictions on new unseen data.
3. How to complete all subtasks of a predictive modeling problem with Python.
4. How to learn new and different techniques in Python and SciPy.
5. How to get help with Python machine learning.

scipy

SciPy is an ecosystem of Python libraries for mathematics, science and engineering. It is an add-on to Python that you will need for machine learning. The SciPy ecosystem is comprised of the following core modules relevant to machine learning:

1. NumPy: A foundation for SciPy that allows you to efficiently work with data in arrays. (you can see the required basics of numpy in my repository !!!linear Algebra!!!)
2. Matplotlib: Allows you to create 2D charts and plots from data.
3. Pandas: Tools and data structures to organize and analyze your data.

Into New file Load_ML_data.ipynb

How to load ML Data

1. Load CSV Files with the Python Standard Library.
2. Load CSV Files with NumPy.
3. Load CSV Files with Pandas.

loading dataset IRIS flower

It is avialable for free on UCI machine learning repository. https://archive.ics.uci.edu/ml/index.php

else we can find any github repositories that contain such datasets.

Prepare data for machine learning

1. Rescale data.
2. Standardize data.
3. Normalize data.
4. Binarize data.

chapter 8 - Feature selection for machine Learning

1. Univariate Selection.
2. Recursive Feature Elimination.
3. Principle Component Analysis.
4. Feature Importance.