12 Most Important Machine Learning Tools You Should Master.
Table of Contents
Machine Learning provides insights in Customer Behavior and Business Operations, that being the reason many top companies have started including machine learning in their core part of operations. In this article we are going to cover 12 most important machine learning tools. The review will consist of ML libraries, ML Platforms and ML Frameworks.
Machine Learning Platforms
1. AI Platform and datasets on google cloud
Many times a major issue in any Machine Learning Project is the need for the correct dataset to train your model. Some of the Google Cloud Datasets are public and are regularly updated by google. The data sets are available for different formats like texts, audio, images and videos. Google Cloud provides basic format of Prepare, Build, Validate and Deploy in ML Modeling
2. Amazon Web Services
Amazon Sagemaker helps to build, train and deploy ML models with fully managed Infrastructure, tools and workflows. They have set up Integrated working environments for big teams to work effectively. Also, they have no code visual interfaces for non-coders.
3. Microsoft Azure
Azure Machine Learning also provides drag and drop functionality to build ML models. The major advantage of using Microsoft Azure is that you can instantly create BI applications from Models. The Cortana Intelligence tool offered by Microsoft makes it easy to work on data based on Big Data Analytics.
4. IBM Watson
IBM Watson is a platform that is recommended when you need tools for research as well as enterprises. Watson platform is known for building virtual agents. It has a chatbot creation platform that can be used by ML beginners for faster and better ML modeling.
Machine Learning Frameworks
1. TensorFlow
This framework is basically used for deep learning applications. Tensorflow allows building large scale neural networks with multiple layers. It can be used for classification, perception, understanding, discovering, prediction and creation etc.
2. Sci-kit Learn
It is the most robust library for machine learning. It provides a selection of efficient tools for ML and statistical modeling. Instead of focusing on loading, manipulating and summarizing this library mainly focuses on modeling the data. It can be used for clustering, cross validation, dimensionality reduction, classification, feature extraction etc.
3. Pytorch
Pytorch was developed by the Facebook Research AI team. Pytorch is a library that makes processing of tensors easy, Tensors are generally small units of data. Unlike other ML libraries pytorch doesn’t provide static graphs. The graphs made with pytorch are dynamic and are built on the go.
4. Keras
Keras is a neural network that provides deep learning solutions. It is the most chosen library by winning teams of Kaggle. Keras is easier to understand and at a high level compared to other libraries. That being the reason it is possible to understand the bigger picture using keras as a library.
5. Apache Spark
Apache Spark is used for data processing and is based on a cluster computing framework. The main advantage of using Apache Spark is it distributes computing power between computers.
Machine Learning Tools
1. Anaconda
Anaconda is an ML platform for data analytics that works with Python as well as R. The major focus of this tool is package management and deployment. It also supports more than 1500 data science packages in Python and R. For people who can have disk space issues,a smaller version also comes with only conda and python. This smaller version is called Miniconda.
2. Jupyter Notebook
Jupyter Notebook is a web based interactive environment for computational needs. It is also used to share documents that contain live code, plots, visualizations, maps, graphics etc. It is very easy to convert a jupyter notebook into other formats like HTML or PDFs.Jupyter Notebook is an Language independent tool.
3. Colab
Colab is a product of Google Research team and allows you to write and execute python codes through the browser. It is mostly used for Machine Learning and Data analysis. No configuration is required for using this product.The sharing is very easy and access to GPUs is free of charge.
ML tools, libraries or platforms are considered smarter when they help to develop models quicker and provide impactful research, analyzing and reporting opportunities. If any other tool fits in the above mentioned line then you can consider that tool to learn even if it is not mentioned in this list.