This blog is part of MSP Developer Stories initiative by Microsoft Students Partner (India) Program - https://studentpartners.microsoft.com/ which is aimed for student communities to Learn, Lead and Empower.
The goal of this post is to give a better understanding of what Machine Learning is with a detailed case study walk-through and how we can start learning using Python and Azure Notebooks.
Machine Learning, is a means of building models of data – finding, discovering and creating insights from data. It is a suite of statistical methods that are used in conjunction to either 'predict' or 'fill in' a solution based on known parameters. Machine learning does take a lot of burden off of humans (prone to error) and works through data in an incredibly fast rate to really give an impressive result.
Key Terms that are often used in Machine Learning:
Training & Test Data Split usually at 80% - 20%. The training data is used to make sure the machine recognizes patterns in the data and the test data is used to see how well the machine can predict new answers based on its training.
Sentiment Analysis commonly used in marketing and customer service to answer questions such as "Is a product review positive or negative?" and "How are customers responding to a product release?"
Confusion Matrix also known as an error matrix. The confusion matrix quantifies the number of times each answer was classified correctly or incorrectly.
Typically the ML Process consists of
- Gathering data from various sources
- Cleaning data to have homogeneity
- Selection of right ML algorithm model building
- Gaining insights from the model’s results
- Transforming results into visual graphs
Now let’s talk technical and get our hands dirty with Machine Learning using Python and Azure Notebooks
Azure Notebooks is a cloud-based platform for building and running Jupyter notebooks. Jupyter is an environment based on IPython that facilitates interactive programming and data analysis using Python and other programming languages. Azure Notebooks provide Jupyter as a service for free. Jupyter notebooks are composed of cells to enter text / code / data.
Case Study : Machine Learning to create a model that predicts which passengers survived the Titanic shipwreck
Let’s start building the project - Hypothesis for the survival on the Titanic which can be determined by various parameters from the data set.
That’s great, Jupyter Notebooks are highly interactive, and since they can include executable code, they provide the perfect platform for manipulating data and building predictive models from it. Develop and run code from anywhere with Jupyter notebooks on Azure. Azure Notebooks helps to get started quickly on prototyping, data science and also for academic research.