The 6 Python Machine Learning Tools Every Data Scientist Should Know About

The 6 Python Machine Learning Tools Every Data Scientist Should Know About

Originally posted on kdnuggets.

Let’s look at six must-have tools every data scientist should use.

Machine learning is rapidly evolving and the crucial focus of the software development industry. The infusion of artificial intelligence with machine learning has been a game-changer. More and more businesses are focusing on wide-scale research and implementation of this domain.

Machine learning provides enormous advantages. It can quickly identify patterns and trends and the concept of automation comes to reality through ML. Businesses from every niche and industry are fast adopting ML to modernize their user interface, security, and AI needs.

For ML, Python is considered the best programming language. It is a user-friendly programming language and has easy multiple ways to load data. Many tools have made Python machine learning easy for data scientists. Let’s look at six must-have tools every data scientist should use.

1. TensorFlow

TensorFlow is a state-of-the-art Python framework for machine learning which carries out deep ML algorithms. It was developed by Google Brain Team as a second-generation, open source-based system.

What makes TensorFlow unique and favorable among developers is that it can create ML models not only for computers but also for smartphones. “TensorFlow Serving” offers ML models for high-performance servers.

It allows seamless distribution of data into various GPU and CPU cores. TensorFlow can be used with various programming languages like C++, Python, and Java. It uses tensors which are storages that contain n-dimensional data with their liner operations.

TensorFlow handles deep and neural networks, text, speech, and image recognition, which are the principal focus of all businesses. It can handle partial differential equations easily. You get new updates every two to three months, which can be annoying for the user to download and bind with the current system.

2. Keras

Keras was developed by Google Engineer François Chollet for project ONEIROS (open-ended neuro electronic intelligent robot operating system). TensorFlow is a powerful tool for DL and ML, but it does not have a user-friendly interface.

The Keras tool identifies itself as an API developed for human beings instead of machines. It is a user-friendly API, perfect for beginners. It is used to create neural networks and is supported in TensorFlow primary library. Keras is on top of TensorFlow and allows beginners to utilize many benefits efficiently. It is also helpful in making text and images faster.

ML and DL programming provide easy build and train models, and neural layers help in Objective ML, batch normalization, pooling layers, and dropout in neural networks.

Keras is ideal for beginners who want to start working on ML quickly. They have a large dedicated community with a slack channel. Getting support is easy when you are using Keras.

3. PyTorch

It is an open-source ML library based on Torch, made by the Facebook AI research lab in 2016.

We can consider PyTorch as a competitor of TensorFlow due to its ability to work with different programming languages and as a valuable tool for ML and DL learning. It is open-source like many ML libraries; like TensorFlow, PyTorch also uses Tensors. Moreover, it can support Python and C++ programming languages.

However, since PyTorch is relatively new, there is plenty of room for improvement. The good news is that it has a strong support community. It is more Python-friendly and supports both GPU and CPU. PyTorch is an easy debugging tool that has simple code, robust APIs, better optimization, and the benefit of computational graph support.

It has a good reputation for handling Deep learning, as it functions efficiently in training and building neural networks. Moreover, it can handle large-scale data used in vision and language-based cases. All SaaS providers, including medical software providers, can benefit from these ML tools to create web assistants for their businesses.

4. Scikit-Learn

This popular ML library for Python can easily be integrated with ML programming libraries. It focuses on several data modeling concepts, including clustering, regression, and classification. This library can be found on Matplotlib, Numpy, and Scipy.

Sickit-Learn is built on the element of “data modeling,” unlike many ML tools, prioritizing data modeling and data visualization. It is an open-source commercial library. Like Keras, it also has a user-friendly interface and easily integrates with other libraries like Panda and Numpy.

Simple commands of predict, fit and transform can benefit tuning, evaluation, data processing, and model interface via a simple user interface. Because of the interface, it is accessible and widely used in the market as a standard library for ML on tabular data.

5. Theano

Theano is a very popular ML library for Python that enables users to optimize and evaluate powerful mathematical expressions. Theano can work on large scientific equations and support GPUs to perform better when doing heavy computations. No matter how complex the operation is, Theano can perform it quickly and efficiently. It can also integrate with NumPy.

Theano has an additional fast GPU which helps in fast computation when doing experiments and tests. It doesn’t compromise the quality and efficiency of the machine learning algorithm. For computing gradients, Theano is smart and can automatically generate symbolic graphs. More and more mobile device security developers are adopting ML algorithms to make user data safer.

6. Pandas

Pandas is another ML open-source data analysis library for Python. It focuses on data analysis and data manipulation. For machine learning programmers who want effortless working with structured multidimensional and time-series data, Pandas is the ML library they need.

Pandas offers numerous features for data handling, which include

  • Data filtration
  • Aligning data
  • Handling data
  • Pivoting data
  • Data reshaping
  • Merging of datasets
  • Merging of datasets

Compared to Numpy, Pandas is fast, and it is one of the few libraries that can work with DateTime independently without getting help from external libraries. This tool works on all essential aspects of ML and data analysis.

Conclusion

Data scientists require software that makes their work easy – they already have complex equations and complicated algorithms in their minds! Every data scientist has specific requirements and priorities while working on Python to develop ML algorithms.

There are many Python libraries for machine learning algorithms. Each offers different advantages and disadvantages. It is up to the developer to choose the desired tool according to their own needs.

Deep learning and machine learning are becoming easier to understand, and ML tools make programming tasks more manageable, efficient, and timely.

Source: kdnuggets