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Why learn Machine Learning (ML)
Machine Learning is a subset of Artificial Intelligence that develops computer programs to predict outcomes and learn without human involvement. It analyses patterns in data to learn, reason, and make decisions.
With the lines between man and machine blurring, we are witnessing a new revolution where Machine Learning is being integrated into businesses and IT operations. The powerful processing helps to make better decisions, the reason why it is used in everyday lives.
There are several reasons why Machine Learning is a key disruptive technology.
Machine Learning achieves the following outcomes:
- Increases efficiency of processes
- Helps to understand and serve customers better
- Customises marketing and advertising campaigns for hyper-personalization
- Supports product recommendation
- Aids fraud detection
A report from Transparency Market Research mentions that the global Machine Learning-as-a-service market will reach $19.86 billion in 2025, growing at a CAGR of more than 38%. Most sectors are applying Machine Learning in their domain and using sophisticated Machine Learning applications across industries such as banking and finance, healthcare, cybersecurity, media and entertainment, and social media. In particular, customer-facing organizations are using Machine Learning algorithms for recommendation engines and image recognition, to name a couple of use cases.
With most companies adopting Machine Learning for their processes, the demand for Machine Learning engineers is at an all-time high.
What are the tools used by the Machine Learning Engineer?
Machine Learning has a wide range of tools and software. Ideally, the learning curve includes at least one tool on which to gain expertise.
Here are some of the popular tools for Machine Learning practitioners:
1. Scikit-Learn
Scikit-Learn is a unified platform available as an open-source package. It is built on top of the three Python libraries: NumPy, Matplotlib, and SciPy and supports Python, Cython, C, and C++. Scikit-Learn is used for many purposes, including testing and training models. Its techniques include regression, clustering, classification, model selection, dimensionality reduction, and preprocessing.
2. TensorFlow
TensorFlow is an open-source framework ideally applied in large-scale as well as numerical Machine Learning. It supports Python, C++, and CUDA. TensorFlow is a mix of Machine Learning and neural network models with a library for dataflow programming. The tool is commonly applied in natural language processing and image classification.
3. Knime
Knime is an open-source tool based on the graphical user interface, not requiring any programming knowledge. It is written in Java. The tool’s redeeming feature is the ability to work with large data volumes like data mining. Popular uses of the Knime tool are text mining and image mining.
4. PyTorch
PyTorch is a fast and flexible deep learning framework. It supports Python, C++, and CUDA. Because of its ability to build deep neural networks and tensor calculations, PyTorch is one of the most popular Machine Learning tools, often used interchangeably with NumPy. Its features include Autograd Module, Optim Module, and the nn Module.
5. Weka
Weka is also open-source software, written in Java and accessed through a graphical user interface. It is an easy-to-use tool allowing access to other Machine Learning tools like Scikit-learn. The tool is commonly used in academia for research and teaching. Its capabilities include Data preparation, Classification, Regression, Visualisation, and Clustering.
6. Rapid Miner
Rapid Miner is a platform-independent data science platform written in Java. Its easy-to-use drag and drop interface makes it the non-programmer’s choice of tool for machine learning tasks. Rapid Miner also allows testing of its own data and models. Some of its techniques include Data loading and Transformation and Data preprocessing and visualization.
7. Accord.net
Accord.net is a platform-independent computational Machine Learning tool with both image and audio packages. The base library is C# language. The packages in the tool are useful in training models and creating interactive applications. The capabilities include Classification,
Regression, Clustering, Hypothesis Tests and Kernel Methods Image, etc. Accord libraries are applied in testing and manipulating audio files.
8. Apache Mahout
The offering by Apache is a platform-independent open-source framework based on Hadoop that supports Java and Scala. Techniques it lends itself to are Preprocessors, Regression, Classification, Clustering, Recommenders, and more. The tool uses math-based functions such as vectors. Apache Mahout is used for data mining tasks.
9. Jupyter Notebook
Jupyter notebook is another Machine Learning tool that allows storing and sharing live code in notebooks. It supports Julia, R, and Python and is accessed through the graphical user interface.
10. Shogun
Shogun is open-source software written in C++ with an interface of many languages like C#, Python, Java, Ruby, Octave, and R. The tool provides algorithms and data structures for solving Machine Learning problems like Regression, Classification, Clustering, and Dimensionality Reduction. It offers support for vector machines and is used for large-scale and online learning. Shogun links with many other Machine Learning libraries like LibLinear, and LibOCAS. Machine Learning engineers use Shogun to process large amounts of data.
Machine Learning tools ease many problems of software development. Customers can be delivered software development solutions that satisfy their requirements. A Machine Learning engineer will master at least one or more tools according to his organizational needs and the problems he is solving.
Conclusion
If you are a wannabe Machine Learning engineer, or a developer or data scientist working on Machine Learning problems, you must take a course that equips you with all the Machine Learning techniques and trains you on the tools required for Machine Learning tasks. Get an online certification that trains you on the tools required for Machine Learning tasks and adds power to your resume.
