Machine Learning for Beginners

Data Science And Machine Learning Machine Learning Data Dashboard

Google Cloud Machine Learning Landing Page
1

Google Cloud Machine Learning

Google Cloud Machine Learning is a service that enables user to easily build machine learning models, that work on any type of data, of any size.
Pricing:
- Open Source
There are various tools that developers and data scientists use to implement Machine Learning algorithms. Often, engineers will use python with libraries such as scikit-learn, TensorFlow, and Keras to build their models, and rely on pandas and numpy to work with their data. There are also a suite of automated Machine Learning tools which further abstract away the nitty gritty. The most popular of these are Google Cloud AutoML, Amazon SageMaker, and Microsoft Azure AutoML. Almost every tool you will find requires you to have some knowledge of the intuition behind Machine Learning. Tools like SageMaker can be really difficult to implement because they require you to understand how to improve your model on your own, which takes time. If you are looking for an end-to-end platform that takes away this hassle, check out Telepath AI’s AutoML tools. In the field of data science, tools such as JupyterLab, Jupyter Notebook, and Anaconda are widely used and they are often prerequisites for tutorials online. If your aim is to build models yourself, you might also consider using a high level language such as Octave/MATLAB because this allows you to get started implementing algorithms much more quickly.

#Data Science And Machine Learning #Data Science Tools #Python Tools 21 social mentions
Amazon SageMaker Landing Page

2

Amazon SageMaker

Amazon SageMaker provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.

There are various tools that developers and data scientists use to implement Machine Learning algorithms. Often, engineers will use python with libraries such as scikit-learn, TensorFlow, and Keras to build their models, and rely on pandas and numpy to work with their data. There are also a suite of automated Machine Learning tools which further abstract away the nitty gritty. The most popular of these are Google Cloud AutoML, Amazon SageMaker, and Microsoft Azure AutoML. Almost every tool you will find requires you to have some knowledge of the intuition behind Machine Learning. Tools like SageMaker can be really difficult to implement because they require you to understand how to improve your model on your own, which takes time. If you are looking for an end-to-end platform that takes away this hassle, check out Telepath AI’s AutoML tools. In the field of data science, tools such as JupyterLab, Jupyter Notebook, and Anaconda are widely used and they are often prerequisites for tutorials online. If your aim is to build models yourself, you might also consider using a high level language such as Octave/MATLAB because this allows you to get started implementing algorithms much more quickly.

#Data Science And Machine Learning #Data Science Tools #Machine Learning 35 social mentions