Practical Machine Learning With Python [Part - 1]
In this part, we discussed about what is machine learning, types of machine learning, linear regression, logistic regression, cross validation and overfitting. In this lab session, I will demonstrate these concepts in Python code. Python is widely used programming language in the field of scientific computing. And the reason is the awesome libraries such as numpy, scikit-learn, matplotlib, etc. We are also going to use these libraries in the lab sessions. Check out github repository of this series here.
Linear Regression
We will start with very simple algorithm called Linear Regression. In the blog post, I explained in-depth - what is linear regression and how it works. In this session, we will focus on implementation rather than theory. We will follow the standard procedure of training machine learning models.
In practice, most of the time we spent behind getting dataset ready for a model, that is, preprocessing and all stuff. Here, I will use preprocessed dataset.
We will use a python library called scikit-learn which is the widely used machine learning lib. For installation process please visit - scikit-learn website. You can install it with pip - pip install -U scikit-learn
链接：
https://savan77.github.io/blog/lab-machine-learning-part1.html
Practical Machine Learning With Python - Part 2
In the previous post, I explained what is machine learning, types of machine learning, linear regression, logistic regression, various issues that we need to consider such as overfitting and at last I explained what really learning is in machine learning. In lab session, I explained how to implement algorithms and concepts that I explained in theory session using Python. I have also uploaded jupyter notebooks on github. Check out this github repository.
In this session, I will explain some easy yet powerful machine learning algorithms such as naive bayes, support vector machine and decision trees. From now onwards, I will not make seperate part for theory and lab session. Instead, I will integrate theory with code in jupyter notebook. If you are unfamiliar with Jupyter notebooks, please go through Jupyter Notebook Basics Guide .
Index
Naive Bayes
Support Vector Machines
Decision Tree
Ensemble Methods
Exercise
链接：
https://savan77.github.io/blog/machine-learning-part2.html
Practical Machine Learning With Python - Part 3
In part-1 and part-2, I explained various supervised learning algorithms. In this part, we will see few unsupervised learning algorithms and the popular supervised learning algorithm called Neural Networks. Check out Github repository of this series for more.
Index
链接：
https://savan77.github.io/blog/machine-learning-part3.html
原文链接：
https://m.weibo.cn/1402400261/4122450956951325
