Enrolling in Machine Learning courses that provide comprehensive coverage is the best way to enter the machine learning field. The high demand for ML professionals in the job market makes it a lucrative career option for many. Machine Learning is one of the fastest-growing technologies today with applications in almost every industry. From healthcare to the banking and finance sector, machine learning is changing the way businesses work.
If you are also interested in building a career in machine learning, you must gain the required skills first. Although you can enter the field even after having a non-technical background, you will have to learn mathematics, programming, etc, to get a job in this field. A well-structured machine learning course can be helpful in this. When looking for the best courses in machine learning, make sure that it covers all the essential topics along with the important ML tools.
Machine Learning is a subdomain of Artificial Intelligence that aims to make machines learn on their own. ML uses different mathematical and programming techniques to enable computers to learn automatically without any human assistance. The three major types of machine learning include:
Machine Learning has so many applications in different industries. Some most common examples of ML applications include voice recognition, image recognition, self-driving cars, recommendation engines, virtual assistants, etc.
If you want to dive deep into the ML field, you must be familiar with the following machine-learning tools and their applications:
TensorFlow is an open-source ML software library for dataflow programming and large-scale machine learning. It was originally designed by Google for research and production. Tensorflow can be used for a wide range of tasks, however, it majorly focuses on developing and training machine learning models. It allows ML developers to visualize neural networks easily, which is one of its most attractive features.
TensorFlow.js is a model converter, which you can use to run your existing ML models. One of the greatest advantages of TensorFlow is that it can run on CPUs, GPUs, and on mobile computing platforms as well. Though it is a relatively new ML tool in the market, it is evolving really fast.
Keras is a high-level neural network API, which helps in performing quick research and is written in Python. It is open-source software that allows fast experimentation with deep neural networks. It aims to be extensible, user-friendly, and modular. Keras is not designed to handle low-level computations. It transfers such computations to another library known as the backend.
Though Keras was integrated into TensorFlow in 2017, its library can still function independently. Keras is useful for fast prototyping and makes it easier. It supports convolution networks and also a combination of two networks. You can run it on GPU as well as CPU.
Watson ML is a cloud service offered by IBM that allows you to build ML models with faster optimization. It puts deep learning and machine learning models into production using data. Watson Studio provides you with various tools that you can use to solve business problems and make data-driven decisions. You can find tools for data cleaning and shaping, data ingestion, data visualization, and building and training Ml algorithms.
Pytorch is an open-source ML framework based on Python language and the Torch library. The library Torch is used to create deep neural networks and is easy to learn for Ml developers. It offers various optimization algorithms that can be used in building neural networks. Moreover, Pytorch provides several tools, libraries, and distributed training. It can be used on cloud platforms.
Pytorch was initially developed by Facebook’s AI research group and offered as open-sourced by GitHub in 2017. It is majorly useful in NLP (Natural Language Processing) applications. The framework is popular for being simple, flexible, making programming easier, and increasing the overall processing speed.
Microsoft Azure ML Studio is a cloud service by Microsoft. It allows you to build, deploy, and train ML models based on predictive analytics. It is an easy-to-use drag-and-drop tool, which you can use to test, deploy, and share predictive analytics solutions on your data. With Azure ML Studio, you can publish ML models as web services to ease consumption by BI tools and custom apps.
The greatest advantage of this tool is that it demands zero programming. It connects the modules and datasets visually to build your Prediction Analytics model.
Amazon offers various ML tools and Amazon Machine Learning is one of them. ML developers all over the world use it to build machine learning models and make predictions. With AML, you can build three types of Ml models, including binary classification, multi-class classification, and regression models. It allows you to integrate data from multiple sources and create new data sources from the MySQL database.
BigML is one of the most important machine learning platforms. It is popular for its user-friendly interface and powerful automation tools. Some of the key features of BigML include model explainability, automated ML, and seamless integration. The platform provides you with several Ml algorithms to solve complex problems. You can load your sets of data, build and train your models, evaluate and share your ML models, and make new predictions.
Apache Spark is a Machine Learning library that runs on Hadoop, Apache Mesos, and Kubernetes. Some most popular utilities and algorithms in the Spark MLib include regression, clustering, classification, basic statistics, recommendation engines, optimization, feature extraction, etc. The aim of Apache Spark MLib is to make practical ML easy and adaptable. The platform offers several tools, for featurization, ML algorithms, Pipelines, etc.
Google Cloud AutoML is an amazing tool that allows you to use the power of machine learning even if you have limited knowledge of the ML field. It is a collection of different ML products that allow developers to train high-quality ML models as per the business requirements. The platform has a simple GUI (graphical user interface) for evaluating, training, deploying, and enhancing ML models on the basis of given data.
MLflow is an open-source machine learning software with four key components, including projects, tracking, models, and model registry. All these components have unique functions to perform. The platform focuses on managing the entire machine learning lifecycle, which consists of experimentation, reproducibility, and deployment of ML models.
A machine learning course can help you start a career in machine learning with the right approach. Edvancer is a career-oriented learning platform and offers the following courses in Machine Learning:
All three courses provide you with comprehensive coverage of machine learning topics along with the most important ML tools. You get theoretical as well as practical knowledge by learning through classes and working on real industry projects. The best feature of these ML courses by Edvancer is that you get to choose from two learning options (self-paced and live online classes) at your convenience.
Ans. Some most popular tools used for AI and ML include TensorFlow, Keras, Apache Spark, IBM Watson Studio, BigML, MS Azure Machine Learning, Google Cloud AutoML, etc.2. Which technology is used in ML?
Ans. Machine Learning makes use of artificial intelligence and computer science to make machines learn on their own.3. What are Python tools for machine learning?
Ans. Some important and most popular Python tools for Machine Learning are: NumPy, SciPy, Scikit-learn, Theano, TensorFlow, and Keras.4. What are machine learning tools?
Ans. Machine learning tools are the software that allows computers to learn and improve on their own without any human assistance.Share this on