As we advance in the world of digitization, there is going to be a growing demand for technologies that can simplify human life, and one such technology is machine learning. It is the subset of AI, which helps the business examine the data, learn, and adapt it. Based on its analysis of data, machine learning enables the devices to make a decision and work accordingly. Another aspect that you need to know about is deep learning, which is the subdomain of machine learning. It ensures efficiency and accuracy. 

With the help of best frameworks for Machine learning and Deep Learning, you can easily construct ML and DL models, which are accurate and efficient. Moreover, these frameworks nullify the need to understand the details of ML algorithms. Machine learning is divided into the following types:

1. Supervised- It helps in predicting data based on previous experiences. For example, filtering spam.

2. Unsupervised-ML framework deciphers hidden patterns and recognizes facial features in pictures and friend lists to make recommendations.

3. Reinforcement- It is concerned with boosting efficacy. 

Machine learning has emerged as a popular career option. In the times to come, we are going to witness a huge demand for Machine learning experts. If you are planning to learn machine learning, then you must also know about the popular machine learning frameworks. Here we are discussing the top 5 machine learning framework that you need to learn to become a machine learning expert: 

Top Machine Learning Frameworks

1. TensorFlow- It is one of the most popular open-source platforms by Google. This framework is based on JavaScript, and this framework is equipped with different tools and community resources, which helps in easy deployment to the machine learning models. The core tool allows your deployment for the browsers, and the TensorFlow Lite is used to deploy models on mobile. You can also use the TensorFlow Extended for deploying ML models in large production environments. 

If you are planning to learn the TensorFlow framework, then you need to have expertise in Julia, Go, and Python. You would be required to do lengthy coding, and it works with a static computation graph. 

Benefits:

• It is good for developing ML models

• Used for data integration function, SQL Tables and images together 

2. PyTorch or Torch- Another popular framework for machine learning is PyTorch or Torch. Facebook developed it. This framework is based on the Torch library and was designed to advance the entire process from research prototyping to production deployment. It has a C++ frontend atop and a Python interface. PyTorch makes use of standard debuggers like PDB or PyCharm.

Benefits:

• You can use standard debuggers like PyCharm or PDB

• It works dynamically with an updated graph

• It is widely used for natural language Processing

3. Keras- This yet another open-source machine learning framework. It is much faster than the other frameworks. It comes with in-built support for data parallelism. Thus, it can handle a large volume of data while expediting the training time. This framework is written in Python and is easy-to-use. You can use this for high-level computation. 

Benefits:

• Good for beginners

• Promotes faster experimentation

• It helps in writing readable and precise code

4. Sonnet- If you are looking for a high-end framework for machine learning, then you must consider learning Sonnet. It is used for building complex neural network structures in TensorFlow. It is a simple but powerful programming model. It is based on a single concept –snt.Module.

Benefits:

• It is simple and easy to use the framework

• You can write modules which can detect other submodels internally

• It is easy to integrate models created with Sonnet with raw TF code and those written with high-level libraries.

5. MXNet- The 5th entrant in our list of most preferred machine learning frameworks for Machine Learning experts. It is highly scalable and works very fast. One of the notable features of this framework is that it supports multiple programming languages and has a flexible programming model. Thus, making it an absolutely useful framework.  Some of the programming languages that it supports are Matlab, Java, Python, Perl, Wolfram, Go, R, etc. It is portable and can scale to different GPUs.

Benefits :

• It supports different GPUs 

• It promotes faster context switching

• It supports different programming languages

• It allows the developers to choose their desired programming language for building machine learning models. 

These are a few of the many machine learning frameworks that every machine learning expert needs to know. 

Conclusion