Some common machine learning algorithm to achieve the code, I am learning to do the process of summary, qualifications are still shallow, if any mistakes please stingy. It will help you to learn Machine Learning algorithms with all terminologies and concepts! Assignments and their Answers are also given, as I believe without implementation, we cannot understand anything.
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- Python. Python 3 is the best option.
- Some scientific computing packages:
- numpy
- pandas
- scikit-learn
- matplotlib
- nltk
You can install Python 3 and all of these packages in a few clicks with the Anaconda Python distribution. Anaconda is popular in Data Science and Machine Learning communities.
If you're using Python 2.7, don't worry. You don't have to migrate to Python 3 just for this guide. Also, if you're using pip/virtualenv instead of Anaconda, that's alright too! Cf. conda vs. pip vs. virtualenv if you're curious.
Guide on installation. We will use pip to install some packages (If you are using anaconda, then no need for all this).
- First get and install pip from here. Using pip, install a bunch of python packages:
- go to your terminal line (don’t open python, just the command prompt)
- install sklearn: pip install scikit-learn
- install natural language toolkit: pip install nltk
You only have to do this once, the code base contains the starter code for all the mini-projects. Go into the Assignments/tools directory, and run startup.py. It will first check for the python modules, then download and unzip a large dataset that we will use heavily later.The download/unzipping can take a while, but you don’t have to wait for it to finish in order to get started on this.
Supervised Learning : Supervised Learning is a learning that helps the machine to learn from its data(input) and will be able to predict output based on the given inputs.
- Examples:
- from an album of tagged photos, recognize someone in a picture
- given someone's music classes choices, and bunch of features of that music.
Types:
- Classification Problem - Treat number of items as continious values.
- Regression Problem - Treat values as discrete ones.(Eg 0,1,2,3 and so on.).
Unsupervised Learning :
Clustering - Do not having information about the type of data, and telling the algorithm to structure(or divide)
them into a particular manner.
Eg- Social Network Analysis - Based on emails or chat,identifying which are group of friends that know each other.
Further description is provided according to the topics(below), to let you understand better!
Refer these for more information.
You can find Solutions here. Questions are uploaded in Assignment Section