Machine learning tools that data scientists must learn in 2023

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Machine Learning is an inseparable part of Data science these days. With the advancement in software development in AI and ML, several advanced and cutting-edge Machine Learning tools have been introduced in the market. 

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Due to popular demand, these tools have become easier to access, and every data scientist should avail of this wonderful opportunity. If you are a data scientist pursuing a PG in data analytics, it is even more important that you learn how to use these tools. Learning these tools will also be advantageous for your curricular studies, and you can upgrade your skills. 

Top 8 Machine Learning Tools Every Data Scientist Must Learn

And now, let me share about each of them in greater detail!

  1. Python

Python is a widely used programming language and a useful tool in data science to analyse data. It can also be repurposed for machine learning and deep learning. The syntax is also quite easy, and the programming language has rich libraries. 

The community is also very active and responds quickly in case you have an issue. There are also regular boot camps for Python in India. There are many resources where you can learn Python. You can take an online course or read a book to learn the tricks. 

  1. NumPy

It is an acronym for Numerical Python. It gives support for multi-dimensional arrays and matrices. This Python library uses C/C++ for its development. The biggest advantage of using NumPy is that it provides all the necessary mathematical support for ML. 

It also uses less memory and has a faster performance when compared to other libraries. It is truly an asset for Data Scientists who use machine learning for projects like random password generators, statistical analysis, calculators or video games.

  1. Pandas

It is a data analysis and manipulation library built over the NumPy package's architecture. It helps to handle tabular data. Pandas are quite flexible, as they can be used with other tools as well. You can effectively use it to build a recommendation system like Netflix or Python. You can also make prediction systems for stocks and neuroscience from this package.

  1. Scikit 

It is an open-source ML library for Python. It is built over the architecture of NumPy, SciPy and Matplotlib. Since it is accessible and reusable, it has great flexibility. It can be used for training and testing models using classification, clustering and regression algorithms. 

Apart from making prediction analysis, recommendations systems and automation, it can also be used to develop evaluation and matchmaking systems.

  1. Matplotlib

It helps in data visualisation and graphical plotting and is a wonderful OOPs tool that helps make embeddable plotting APIs. Since it can be used cross-platform, it can be integrated with third-party apps. It includes LaTex to develop neuroscience apps, stock price evaluation systems and also for game development.

  1. R Programming

R is another famous tool for Data Scientists who are also into machine learning. It is also a good choice of popular programming language highly regarded by statisticians or data scientists. It is also useful for a visual representation of data (with the help of ggplot2). 

R has many packages, making it a perfect scientific research tool. It finds many applications in the healthcare data sector and other fields that heavily use statistics. It is also best suited for data mining.

  1. TensorFlow

TensorFlow is quite a robust machine-learning tool developed by Google. As a data scientist, you can use it to build and train machine learning models in a short time. It provides a data automation platform. You can also train and monitor your models using TensorFlow. This tool is extremely useful but only when the hardware is decent enough.

  1. Hadoop 

Apache Hadoop is a collection of open-source software platforms that allows data scientists to use a whole computer network to solve Big Data and Computation problems. Hadoop provides a software framework that helps distribute the storage and improves big data processing using the MapReduce programming model. 

Now Hadoop is used to power many cloud storage apps and also powers search engines and social networks. Its community is so widespread that you have multiple resources to learn Hadoop online.

As an aspiring data scientist, it might be challenging to learn these separately. However, there is an option to learn all these tools at once. Imarticus Learning has a good data science course with placement, which covers all these important tools as a part of its curriculum. You will be taught by the best faculty and even be given a job placement after ending this course. What else could you ask for? 

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