#best python libraries for machine learning

Introduction

Data science is one of the fastest-growing fields in the technology sector. Industries such as finance, healthcare, e-commerce, logistics, and IT rely heavily on data to make strategic business decisions. Because of this demand, many students and working professionals are exploring data science courses to build analytical and technical skills.

For learners in Thane, choosing the right program involves understanding the course syllabus, learning format, career opportunities, and practical exposure. This guide explains what a modern data science course includes and how to select the right training program.

What Is a Data Science Course?

A data science course in Thane teaches students how to process large volumes of data and extract meaningful insights. The training usually combines programming, statistics, and data visualization.

Students typically learn how to:

Collect and clean raw data

Analyze patterns and trends

Build predictive models

Present insights using dashboards and reports

These skills help organizations make better decisions and improve operational efficiency.

Syllabus and Tools Learned in a Data Science Course

A strong syllabus is one of the most important factors when selecting a data science program. Industry-oriented courses generally cover the following areas.

Python Programming

Python is the most widely used programming language in data science. Students usually work with popular libraries such as:

NumPy

Pandas

Matplotlib

Scikit-learn

These tools are used for processing large datasets and performing statistical analysis.

Statistical and Data Analysis

A solid understanding of statistics is essential for interpreting data. Common topics include:

Probability

Regression analysis

Hypothesis testing

These concepts help analysts identify patterns and make predictions from data.

Data Visualization

Visualization tools convert complex datasets into clear and understandable insights. Students often learn tools such as:

Power BI

Tableau

These tools help create dashboards and visual reports for business decision-making.

Machine Learning Basics

Many data science courses introduce machine learning algorithms that help build predictive models for tasks such as forecasting, automation, and pattern detection.

Data Science Course Fees and Duration in India

In India, the cost of a data science course varies depending on the learning mode and training level.

Typical course durations include:

3–6 months for beginner or intermediate programs

6–12 months for advanced or comprehensive courses

Course fees depend on several factors:

Curriculum coverage

Trainer experience

Project exposure

Career support

Instead of considering cost alone, learners should also evaluate the quality of training and practical learning opportunities.

Online vs Offline Data Science Course: Which Is Better?

Both online and classroom learning offer unique advantages.

Online Data Science Courses

Online courses are flexible and convenient for students who cannot attend physical classes.

Benefits include:

Flexible learning schedule

Access to recorded sessions

Self-paced study options

Offline Classroom Courses

Classroom training provides direct interaction with instructors and classmates.

Advantages include:

Live problem-solving sessions

Instant doubt clarification

Hands-on lab practice

Many institutes now offer hybrid learning models that combine online flexibility with classroom interaction.

Placement-Focused Data Science Courses

Placement-oriented programs focus on job readiness and practical learning. Students build industry-relevant skills such as:

Data analysis using Python and SQL

Data visualization and reporting

Applying machine learning models to real problems

Presenting insights through dashboards and reports

Practical projects and portfolio development are important for preparing students for interviews.

Best Data Science Courses for Beginners

Beginners should choose courses that start with basic concepts and gradually move toward advanced topics.

A beginner-friendly course usually includes:

Python fundamentals

Data cleaning and preprocessing

Data visualization tools

Introduction to machine learning

Hands-on projects are essential because they help learners apply theoretical knowledge to real-world scenarios.

Data Science Courses for Working Professionals

Working professionals often prefer online or weekend programs that allow them to balance learning with their jobs.

Professional certification programs typically focus on:

Real-world business case studies

Advanced data analysis tools

Portfolio development

Career transition guidance

These programs help professionals upgrade their skills and move into analytics or data science roles.

Choosing the Right Data Science Training Institute

Selecting the right training institute requires evaluating several important factors:

Industry-relevant curriculum

Practical project experience

Experienced trainers

Transparent course structure

Career guidance and counseling

For learners considering data science training in Thane, institutes such as Quastech IT Training and Placement Institute are often mentioned for their focus on practical IT training and structured learning programs.

Career Guide After Completing a Data Science Course

After completing a professional data science program, learners can explore roles such as:

Data Analyst

Junior Data Scientist

Reporting Analyst

Business Analyst

With experience, professionals can advance to roles such as:

Machine Learning Engineer

Analytics Consultant

Data Science Lead

Final Thoughts

A best data science course in Thane can open the door to many career opportunities in today’s digital economy. However, selecting the right training program is essential.

Learners should focus on curriculum quality, practical exposure, and industry relevance, rather than only the popularity of the course.

Hey everyone! You've hit that critical point in every project: picking the backend. By 2026, the game is all about performance, microservices, and AI integration.

The debate always boils down to these three heavy hitters. I'm not picking a winner, just giving you the facts to make your own call for your next big build.

The Backend Lineup in 2026

1. Laravel (PHP)

The Vibe: The elegant, batteries-included framework. Laravel uses PHP, which powers most of the web, and it has an incredibly clear syntax and short learning curve.

Best For: Traditional web apps, e-commerce, content management systems (CMS), and stable enterprise platforms. It’s reliable and structured, making rapid application development super efficient.

The Hosting Reality: Laravel is often the easiest to host across the board, including basic shared hosting environments, making it a budget-friendly way to keep your initial BigCloudy web hosting costs low.

2. Python (Django/Flask)

The Vibe: The easy-to-read powerhouse. Python’s syntax is considered beginner-friendly, and its frameworks, like Django and Flask are highly scalable and secure.

Best For: Data science, machine learning, and AI-driven applications, where its powerful libraries are unmatched. It’s also great for data-heavy platforms and APIs.

The Hosting Reality: Python requires more dedicated resources than PHP to handle the computational loads for those AI models. For serious development, you'll generally want a strong BigCloudy VPS or cloud hosting solution for the best performance.

3. Node.js (Express/NestJS)

The Vibe: The speed demon and real-time champ. Because it's JavaScript on the server, you can use one language for the entire stack. Its event-driven, non-blocking I/O model is its key advantage.

Best For: Real-time chat apps, live streaming, microservices, and systems that need to handle thousands of concurrent connections (high concurrency). It's excellent for high-speed transaction processing.

The Hosting Reality: Node.js, while fast, is complex and requires specialized configurations. You'll need specialized Node.js hosting or cloud hosting plans that offer full root access for full control and optimization.

The Takeaway

There is no single "best" winner; it depends entirely on your project's needs.

If your project is about speed and concurrency, Node.js is your champion.

If your project relies heavily on data science and AI, Python is non-negotiable.

If you need stability and rapid delivery on an established CMS, Laravel is proven.

Python has already made a significant impact in the tech world, and its trajectory for the future is even more promising. From its simplicity and versatility to its widespread use in cutting-edge technologies, Python is expected to continue thriving in the coming years. Considering the kind support of Python Course in Chennai Whatever your level of experience or reason for switching from another programming language, learning Python gets much more fun.

Let's explore why Python will remain at the forefront of software development and what trends and applications will contribute to its ongoing dominance.

1. Artificial Intelligence and Machine Learning

Python is already the go-to language for AI and machine learning, and its role in these fields is set to expand further. With powerful libraries such as TensorFlow, PyTorch, and Scikit-learn, Python simplifies the development of machine learning models and artificial intelligence applications. As more industries integrate AI for automation, personalization, and predictive analytics, Python will remain a core language for developing intelligent systems.

2. Data Science and Big Data

Data science is one of the most significant areas where Python has excelled. Libraries like Pandas, NumPy, and Matplotlib make data manipulation and visualization simple and efficient. As companies and organizations continue to generate and analyze vast amounts of data, Python’s ability to process, clean, and visualize big data will only become more critical. Additionally, Python’s compatibility with big data platforms like Hadoop and Apache Spark ensures that it will remain a major player in data-driven decision-making.

3. Web Development

Python’s role in web development is growing thanks to frameworks like Django and Flask, which provide robust, scalable, and secure solutions for building web applications. With the increasing demand for interactive websites and APIs, Python is well-positioned to continue serving as a top language for backend development. Its integration with cloud computing platforms will also fuel its growth in building modern web applications that scale efficiently.

4. Automation and Scripting

Automation is another area where Python excels. Developers use Python to automate tasks ranging from system administration to testing and deployment. With the rise of DevOps practices and the growing demand for workflow automation, Python’s role in streamlining repetitive processes will continue to grow. Businesses across industries will rely on Python to boost productivity, reduce errors, and optimize performance. With the aid of Best Online Training & Placement Programs, which offer comprehensive training and job placement support to anyone looking to develop their talents, it’s easier to learn this tool and advance your career.

5. Cybersecurity and Ethical Hacking

With cyber threats becoming increasingly sophisticated, cybersecurity is a critical concern for businesses worldwide. Python is widely used for penetration testing, vulnerability scanning, and threat detection due to its simplicity and effectiveness. Libraries like Scapy and PyCrypto make Python an excellent choice for ethical hacking and security professionals. As the need for robust cybersecurity measures increases, Python’s role in safeguarding digital assets will continue to thrive.

6. Internet of Things (IoT)

Python’s compatibility with microcontrollers and embedded systems makes it a strong contender in the growing field of IoT. Frameworks like MicroPython and CircuitPython enable developers to build IoT applications efficiently, whether for home automation, smart cities, or industrial systems. As the number of connected devices continues to rise, Python will remain a dominant language for creating scalable and reliable IoT solutions.

7. Cloud Computing and Serverless Architectures

The rise of cloud computing and serverless architectures has created new opportunities for Python. Cloud platforms like AWS, Google Cloud, and Microsoft Azure all support Python, allowing developers to build scalable and cost-efficient applications. With its flexibility and integration capabilities, Python is perfectly suited for developing cloud-based applications, serverless functions, and microservices.

8. Gaming and Virtual Reality

Python has long been used in game development, with libraries such as Pygame offering simple tools to create 2D games. However, as gaming and virtual reality (VR) technologies evolve, Python’s role in developing immersive experiences will grow. The language’s ease of use and integration with game engines will make it a popular choice for building gaming platforms, VR applications, and simulations.

9. Expanding Job Market

As Python’s applications continue to grow, so does the demand for Python developers. From startups to tech giants like Google, Facebook, and Amazon, companies across industries are seeking professionals who are proficient in Python. The increasing adoption of Python in various fields, including data science, AI, cybersecurity, and cloud computing, ensures a thriving job market for Python developers in the future.

10. Constant Evolution and Community Support

Python’s open-source nature means that it’s constantly evolving with new libraries, frameworks, and features. Its vibrant community of developers contributes to its growth and ensures that Python stays relevant to emerging trends and technologies. Whether it’s a new tool for AI or a breakthrough in web development, Python’s community is always working to improve the language and make it more efficient for developers.

Conclusion

"you shouldn't be too worried about warnings. You should be very worried about errors, but warnings are usually there to be ignored"

Student: "could you please reiterate your conclusion for that last graph?"   Prof: "sure! the conclusion is, this graph is a mess"

"this is the library that you will love and curse time after time again and again"

"the larger the sample, the more even the distribution, OR the prettier the distribution. the same thing should be applied to people but often it is not because humanity sucks"

Prof: "We had a stdent in the first year who's now driving a really fancy car all because we taught her R"    Student: "A really fancy car?"   Prof: "REALLY fancy. That's my way of saying now she's rich. You think she'd give me the fancy car for teaching her but no. You're welcome, Rebecca"

"we're never really sure when something is an outlier, that's usually a very ad-hoc decision"

"its been three years since i last used chalk on a blackboard because of covid. now i finally feel like i'm living again, using chalk without care, using chalk with, if i may be so bold, utter abandon"

"There's new types of statistics now. You might have heard of bayesian statistics. That's very popular right now, and very hipster"

Prof 1: "Let's see what the dimensions of the smallest book in this dataset is. And it's... zero?"   *prof 2 nods*   Prof 1: "how?!"   *prof 2 stops nodding*   Prof 2: "... i think there's a problem with our dataset"   Prof 1: "yeah, so do I, but you were nodding VERY confidently so I was worried I was going insane"

"so if you want to read more about this article yourself, i've included the link to this lovely helpful website called "callingbullshit.org". it truly is one of a kind"

“probability was invented by gamblers, and i am very good at probability. make of that, what you will"

Student: "Why is it called the mode?"   Prof: "What do you mean?"   Student: "Why is the mode called the mode"   *silence, then professor gives a long drawn-out sigh*   Prof: "the term mode originates with Karl Pearson in 1895. Pearson uses the term mode interchangeably with maximum-ordinate. in one of his research logs, he says I have found it convenient to use the term mode for the abscissa corresponding to the ordinate of maximum frequency-”   *fifteen minutes of explination later*   Prof: “... every single year someone asks that question, so i just learned off the wikipedia answer to it”

"the mean and median may seem rather arbitary and, in many ways, they are because it's difficult to say where they came from mathemtically speaking, but a mean in particular has a very instinctual meaning... especially in gambling. A LOT of statistics comes from gambling”

"this formula looks complicated and i know i promised we wouldn't do any meth- MATH, any MATH"

"You see how these python notebooks are full of little jokes? humor is very important to maintain sanity in coding"

PPT Slide: "Do NOT use pie charts. If you use pie charts, then I will fail you. Thanks in advance."   Prof 1: "... okay then. I actually didn't write this, is this is a pet peeves of yours, Prof 2?"   Prof 2: "No. No no no. This isn’t a pet peeve, THIS is based on scientific facts"   Prof 1: "What type of science tells you to never use pie charts?"   Prof 2: "My science. They're the comic sans of statistics!"

"if you know nothing about machine learning then this will be a life changing course for you, but everything with me is life changing- NOPE. i heard it as soon as i said it, please don’t report me"

"the line here is... well, it's doing its best, but its not really suceeding, is it? ... much like half of this class"

Part 1 | Part 2 | Part 3 | Part 4 | Part 5 | Part 6 | Part 7  | Part 8

Learning to code can be a rewarding and empowering journey. Here are some steps to help you get started:

Define Your Purpose:

Understand why you want to learn to code. Whether it's for a career change, personal projects, or just for fun, having a clear goal will guide your learning path.

Machine learning technology has truly transformed multiple industries and continues to hold enormous potential for future development. If you're considering incorporating machine learning into your business or are simply eager to learn more about this transformative field, seeking advice from experts or enrolling in specialized courses is a wise step. For instance, the ACTE Institute offers comprehensive machine learning training programs that equip you with the knowledge and skills necessary for success in this rapidly evolving industry. Recognizing the potential of machine learning can unlock numerous avenues for data analysis, automation, and informed decision-making.

Now, let me share my successful journey in machine learning, which I believe can benefit everyone. These 10 steps have proven to be incredibly effective in helping me become a proficient machine learning practitioner:

Step 1: Understand the Basics

Develop a strong grasp of fundamental mathematics, particularly linear algebra, calculus, and statistics.

Learn a programming language like Python, which is widely used in machine learning and provides a variety of useful libraries.

Step 2: Learn Machine Learning Concepts

Enroll in online courses from reputable platforms like Coursera, edX, and Udemy. Notably, the ACTE machine learning course is a stellar choice, offering comprehensive education, job placement, and certification.

Supplement your learning with authoritative books such as "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron and "Pattern Recognition and Machine Learning" by Christopher Bishop.

Step 3: Hands-On Practice:

Dive into real-world projects using both simple and complex datasets. Practical experience is invaluable for gaining proficiency.

Participate in machine learning competitions on platforms like Kaggle to challenge yourself and learn from peers.

Step 4: Explore Advanced Topics

Delve into deep learning, a critical subset of machine learning that focuses on neural networks. Online resources like the Deep Learning Specialisation on Coursera are incredibly informative.

For those intrigued by language-related applications, explore Natural Language Processing (NLP) using resources like the "Natural Language Processing with Python" book by Steven Bird and Ewan Klein.

Step 5: Learn from the Community

Engage with online communities such as Reddit's r/Machine Learning and Stack Overflow. Participate in discussions, seek answers to queries, and absorb insights from others' experiences.

Follow machine learning blogs and podcasts to stay updated on the latest advancements, case studies, and best practices.

Step 6: Implement Advanced Projects

Challenge yourself with intricate projects that stretch your skills. This might involve tasks like image recognition, building recommendation systems, or even crafting your own AI-powered application.

Step 7: Stay updated

Stay current by reading research papers from renowned conferences like NeurIPS, ICML, and CVPR to stay on top of cutting-edge techniques.

Consider advanced online courses that delve into specialized topics such as reinforcement learning and generative adversarial networks (GANs).

Step 8: Build a Portfolio

Showcase your completed projects on GitHub to demonstrate your expertise to potential employers or collaborators.

Step 9: Network and Explore Career Opportunities

Attend conferences, workshops, and meetups to network with industry professionals and stay connected with the latest trends.

Explore job opportunities in data science and machine learning, leveraging your portfolio and projects to stand out during interviews.

Python, often regarded as one of the most beginner-friendly programming languages, offers an excellent entry point for those looking to embark on a coding journey. Whether you aspire to become a Python pro or simply want to add a valuable skill to your repertoire, the path to Python proficiency is well-paved. In this blog, we’ll outline a comprehensive strategy to learn Python from scratch to pro, and we’ll also touch upon how ACTE Institute can accelerate your journey with its job placement services.

1. Start with the basics:

Every journey begins with a single step. Familiarise yourself with Python’s fundamental concepts, including variables, data types, and basic operations. Online platforms like Codecademy, Coursera, and edX offer introductory Python courses for beginners.

2. Learn Control Structures:

Master Python’s control structures, such as loops and conditional statements. These are essential for writing functional code. Sites like HackerRank and LeetCode provide coding challenges to practice your skills.

3. Dive into Functions:

Understand the significance of functions in Python. Learn how to define your functions, pass arguments, and return values. Functions are the building blocks of Python programmes.

4. Explore Data Structures:

Delve into Python’s versatile data structures, including lists, dictionaries, tuples, and sets. Learn their usage and when to apply them in real-world scenarios.

5. Object-Oriented Programming (OOP):

Python is an object-oriented language. Learn OOP principles like classes and objects. Understand encapsulation, inheritance, and polymorphism.

6. Modules and Libraries:

Python’s strength lies in its extensive libraries and modules. Explore popular libraries like NumPy, Pandas, and Matplotlib for data manipulation and visualisation.

7. Web Development with Django or Flask:

If web development interests you, pick up a web framework like Django or Flask. These frameworks simplify building web applications using Python.

8. Dive into Data Science:

Python is a dominant language in the field of data science. Learn how to use libraries like SciPy and Scikit-Learn for data analysis and machine learning.

9. Real-World Projects:

Apply your knowledge by working on real-world projects. Create a portfolio showcasing your Python skills. Platforms like GitHub allow you to share your projects with potential employers.

10. Continuous learning:

Python is a dynamic language, with new features and libraries regularly introduced. Stay updated with the latest developments by following Python communities, blogs, and podcasts.

The ACTE Institute offers a structured Python training programme that covers the entire spectrum of Python learning. Here’s how they can accelerate your journey:

Comprehensive Curriculum: ACTE’s Python course includes hands-on exercises, assignments, and real-world projects. You’ll gain practical experience and a deep understanding of Python’s applications.

Experienced Instructors: Learn from certified Python experts with years of industry experience. Their guidance ensures you receive industry-relevant insights.

Job Placement Services: One of ACTE’s standout features is its job placement assistance. They have a network of recruiting clients, making it easier for you to land a Python-related job.

Flexibility: ACTE offers both online and offline Python courses, allowing you to choose the mode that suits your schedule.

The journey from Python novice to pro involves continuous learning and practical application. ACTE Institute can be your trusted partner in this journey, providing not only comprehensive Python training but also valuable job placement services. Whether you aspire to be a Python developer, data scientist, or web developer, mastering Python opens doors to diverse career opportunities. So, take that first step, start your Python journey, and let ACTE Institute guide you towards Python proficiency and a rewarding career.

I hope I answered your question successfully. If not, feel free to mention it in the comments area. I believe I still have much to learn.

If you feel that my response has been helpful, make sure to Follow me on Tumblr and give it an upvote to encourage me to upload more content about Python.

Python has rapidly become the go-to language in the data science space and is among the first things recruiters search for in a data scientist’s skill set, there’s no doubt about it. It has consistently ranked top in global data science surveys and its widespread popularity only keeps on increasing!

But what makes Python so special for data scientists?

Just like our human body consists of multiple organs for multiple tasks and a heart to keep them running, similarly, the core Python provides us with the easy easy-to-code, object-oriented, high-level language (the heart). We have different libraries for each type of job like Math, Data Mining, Data Exploration, and visualization(the organs). learn with these resources Best Python Books

It is of utmost importance that we master each and every library, these are the core libraries and these won’t be changed overnight. The AI and ML BlackBelt+ program help you master these 13 libraries along with many more.

That’s not all, you’ll get personalized mentorship sessions in which your expert mentor will customize the learning path according to your career needs.

Let us learn about the Top 13 Python libraries for data science that you must master!

Before starting out, I have a bonus resource for you! Python is a diverse language and it is hard to remember each and every line of syntax so here’s the link to the Python cheatsheet to help you out-

Math

NumPy

NumPy is one of the most essential Python Libraries for scientific computing and it is used heavily for the applications of Machine Learning and Deep Learning. NumPy stands for NUMerical PYthon. Machine learning algorithms are computationally complex and require multidimensional array operations. NumPy provides support for large multidimensional array objects and various tools to work with them.

Various other libraries which we are going to discuss further like Pandas, Matplotlib and Scikit-learn are built on top of this amazing library! I have just the right resource for you to get started with NumPy –

SciPy

SciPy (Scientific Python) is the go-to library when it comes to scientific computing used heavily in the fields of mathematics, science, and engineering. It is equivalent to using Matlab which is a paid tool.

SciPy as the Documentation says is – “provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization.” It is built upon the NumPy library. Learn new skills with Best Python Programming Books

Data Mining

BeautifulSoup

BeautifulSoup is an amazing parsing library in Python that enables web scraping from HTML and XML documents.

BeautifulSoup automatically detects encodings and gracefully handles HTML documents even with special characters. We can navigate a parsed document and find what we need which makes it quick and painless to extract the data from the webpages. In this article, we will learn how to build web scrapers using Beautiful Soup in detail.

Scrapy

Scrapy is a Python framework for large scale web scraping. It gives you all the tools you need to efficiently extract data from websites, process them as you want, and store them in your preferred structure and format.

Data Exploration and Visualization

Pandas

From Data Exploration to visualization to analysis – Pandas is the almighty library you must master!

Pandas is an open-source package. It helps you to perform data analysis and data manipulation in Python language. Additionally, it provides us with fast and flexible data structures that make it easy to work with Relational and structured data. Learn advanced concepts with Best book to learn python

Matplotlib

Matplotlib is the most popular library for exploration and data visualization in the Python ecosystem. Every other library is built upon this library.

Matplotlib offers endless charts and customizations from histograms to scatterplots, matplotlib lays down an array of colors, themes, palettes, and other options to customize and personalize our plots. matplotlib is useful whether you’re performing data exploration for a machine learning project or building a report for stakeholders, it is surely the handiest library!

Plotly

Plotly is a free and open-source data visualization library. I personally love this library because of its high quality, publication-ready and interactive charts. Boxplot, heatmaps, bubble charts are a few examples of the types of available charts.

It is one of the finest data visualization tools available built on top of visualization library D3.js, HTML, and CSS. It is created using Python and the Django framework. So if you are looking to explore data or simply wanting to impress your stakeholders, plotly is the way to go!

Seaborn

Seaborn is a free and open-source data visualization library based on Matplotlib. Many data scientists prefer seaborn over matplotlib due to its high-level interface for drawing attractive and informative statistical graphics.

Seaborn provides easy functions that help you focus on the plot and now how to draw it. Seaborn is an essential library you must master. 

Machine Learning

Scikit Learn

Sklearn is the Swiss Army Knife of data science libraries. It is an indispensable tool in your data science armory that will carve a path through seemingly unassailable hurdles. In simple words, it is used for making machine learning models.

Scikit-learn is probably the most useful library for machine learning in Python. The sklearn library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering, and dimensionality reduction.

Sklearn is a compulsory Python library you need to master. Analytics Vidhya offers a free course on it. You can check out the resources here 

PyCaret

Tired of writing endless lines of code to build your machine learning model? PyCaret is the way to go!

PyCaret is an open-source, machine learning library in Python that helps you from data preparation to model deployment. It helps you save tons of time by being a low-code library.

It is an easy to use machine learning library that will help you perform end-to-end machine learning experiments, whether that’s imputing missing values, encoding categorical data, feature engineering, hyperparameter tuning, or building ensemble models. Here’s an excellent resource for you to learn PyCaret from scratch  Learn basick skills with  best python book for beginners

TensorFlow

Over the years, TensorFlow, developed by the Google Brain team has gained traction and become the cutting edge library when it comes to machine learning and deep learning. TensorFlow had its first public release back in 2015. At the time, the evolving deep learning landscape for developers & researchers was occupied by Caffe and Theano. In a short time, TensorFlow emerged as the most popular library for deep learning.

TensorFlow is an end-to-end machine learning library that includes tools, libraries, and resources for the research community to push the state of the art in deep learning and developers in the industry to build ML & DL powered applications.

Keras

Keras is a deep learning API written in Python, which runs on top of the machine learning platform TensorFlow. It was developed with a focus on enabling fast experimentation. According to Keras – “Being able to go from idea to result as fast as possible is key to doing good research.”

Keras is preferred over TensorFlow by many, due to its much better “user experience”, Keras was developed in Python and hence the ease of understanding by Python developers. It is simple to use and yet a very powerful library.

PyTorch

Many data science enthusiasts hail Pytorch as the best deep learning framework (that’s a debate for later on). It has helped accelerate the research that goes into deep learning models by making them computationally faster and less expensive.

PyTorch is a Python-based library that provides maximum flexibility and speed. Some of the features of Pytorch are as follows –

Production Ready

Distributed Training

Robust Ecosystem

Cloud support

End Notes

Python is a powerful yet simple language for all of your machine learning tasks.