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Machine Learning for Developers (Updated Guide)
Learning about different algorithms to get into machine learning is the biggest challenge at the start. But most experts believe that you only require one technique initially ie. random forests. You don’t need to know all the methods and workings of the random forests and you need not be an expert from day one in learning all the algorithms.
A right level of understanding of these algorithms and their intuitions is enough to get you started. You can practice and experiment with machine learning at a beginner level with enough knowledge and can look for open-source ML libraries and cloud platforms from where you can create data models.
When you deploy machine learning applications in real-world problems, most applications are made before and after the deployment. The end-to-end view of how ML is used in an application is an essential aspect of learning. As a developer, one should be able to figure out how ML is deployed in the application in ML libraries using various programming languages such as Python, R Matlab or Ruby, swift, C++ etc. There are many libraries such as scikit-learn, Pandas, SKLL, where languages can be implemented and Microsoft Azure ML and Amazon ML are some of the cloud platforms that are beneficial for companies and real-world problems.
Workflow of Machine Learning
Apart from deployment, there are other post-learning obstacles in real-world ML applications as well. As a developer, your role is of evaluating and monitoring the impact of performance of your machine learning models before and after deploying them.
The above diagram of machine learning workflow depicts some of the steps to keep in mind before learning any Machine learning model, which consists of arranging corrects datasets to run the algorithms. Before running the algorithm, you should be:
- Defining the actual ML barrier to take care of for your company.
- Engineering machine learning features, which means to showcase the objects on which the predictions in ML will be made.
- Figuring out how and when will you have to make predictions and what will be the duration of the same. Also figuring out if the predictions needs to be real-time or if they can be made in batches.
- Collecting data.
- Preparing real datasets to run the ML algorithms, which ultimately means to extract different features out the raw data and cleaning it.
- Figuring out how and when there will be a need for new and updated data models and its duration.
Operational Machine Learning
The main aim of Machine Learning is to be operational for data engineers or developers to make use of the new Machine Learning techniques that they have studied. Developers must create value out of the data with machine learning by creating, operating and evaluating predictive models. After learning the basics, an advanced level of Machine learning such as unsupervised learning, deep learning and neural network machine learning should also be practised by the developers.
Some of the real-life machine learning examples may include:
- Image recognition
- Personal assistants in cellphones
- Chatbots and online customer service
- Recommendation of products