Python is a perfect choice for beginner to make your focus on in order to jump into the field of machine learning and data science. It is a minimalistic and intuitive language with a full-featured library line (also called frameworks) which significantly reduces the time required to get your first results.Start Learning with us ACTE Python With Machine Learning Classroom and Online Training Course.
A background in data analysis is perfect for transitioning or getting into machine learning as a career. Lean Python, then, dive into ML libraries- Scikit-learn & Tensor Flow are pretty popular in the field. Machine learning, like any other industry, possesses its own unique needs and goals.
Is a career in Machine Learning lucrative or not? If this question is in your mind then rethink, because PwC report says that 31% of the executives are worried about the inability to meet the demand for AI skills over the next 5 years. In this article I will put forth this topic ‘Machine Learning Career and Future Scope’.
Even as a fresher, you can get a job in Python With Machine Learning domain.Learning Enough Python to Land a Job. If you want a job programming in Python, prepare to do a lot of work beforehand. The language is easy to pick up, but you need to do more than just learn the basics; to get a job, you need to have a strong understanding of some pretty complex processes.
We are happy and proud to say that we have strong relationship with over 700+ small, mid-sized and MNCs. Many of these companies have openings for Python With Machine Learning. Moreover, we have a very active placement cell that provides 100% placement assistance to our students. The cell also contributes by training students in mock interviews and discussions even after the course completion.
For beginners, I recommend installing the Anaconda Python platform.
It is free and comes with Python and libraries needed for machine learning, such as scikit-learn, pandas, and much more.
You can also easily install deep learning libraries with Anaconda such as TensorFlow and Keras.
- To get started with Machine Learning you must be familiar with the following concepts
- Statistics
- Linear Algebra
- Calculus
- Probability
- Programming Languages
Machine learning projects don't end with just coding,there are lot more steps to achieve results like Visualizing the data, applying suitable ML algorithm, fine tuning the model, preprocessing and creating pipelines. So,yes coding and other skills are also required.
If you have almost no technical knowledge but want to get started with machine learning, it's important to master some of the basics, like linear algebra, probability, and python programming.
Our course ware is designed to give a hands-on approach to the students in Python With Machine Learning. The course is made up of theoretical classes that teach the basics of each module followed by high-intensity practical sessions reflecting the current challenges and needs of the industry that will demand the students’ time and commitment.
It's definitely worth You can learn Python on weekends if you know C++ or Java but if you really want to be able to develop application using Python then you will have to plunge into it and I can bet once you do that you will never want to come out of that.
When it comes the matter of being worth then...I would say...If you learn Java for 3 months if you are a jobless then it's not sure whether you might get a job or not but if you know Python and give your 3 months to learn its framework or related stuffs (for which 3 months is enough) then I can guarantee that you wont be jobless ever.
If you are new, learning and mastering Python can take you anywhere from 6 months to 1 or even 2 years. You can start as an intern first to get your feet wet into production level code, understand the concerned issues and their fixes/work around.
- Data Science. This is the single, biggest reason why many programmers are learning Python in 2018.
- Machine Learning.
- Web Development.
- Simplicity.
- Huge Community.
- Libraries and Frameworks.
- Automation.
- Multipurpose.
Reasons for Python in machine learning’s popularity
The main reason that python has managed to grab the attention of programmers is that it is full of features that are taking it to a new level. It is an extremely simple and easy language when it comes to read and write. As a result, the programmer can easily code without worrying about any confusion. If anything, Google, being one of the biggest search engines depends on Python to code and work on. To make it clear, listed below are a few features of python that make it unique in itself.
1. Open Source
Yes, the best part about Python is that it is an open-source language that makes it highly popular and available among others. On top of that, the codes being open source can be used by anyone publicly on the net. It is also easy to work on the code or even modify it as per the requirement.
2. Supportive and Rich community
If you are a person who is connected with coding then you might know that not every language will support the system. It is the major concern when it comes to code which makes python reliable. There is some language that makes it difficult for programmers to document the whole project which brings us back to one of the major issues. The building up of a project can be extremely daunting when it comes to another programming language.
However, when we go for Python then there are no such issues that might halt the process. The best part is that python is ruling for so many years that make it even easier to get tutorial. In addition to this, there are several guides and documents present online and offline that makes it easy for new programmers to deal with it.
3. Cross-platform language
Another of the best feature of python is that it is extremely diverse when it comes to the operating system. In simple words, python can be used for any type of operating system such as Linus, Windows, Ubuntu, etc. Hence, one can easily run off a software without worrying about system support. It can be interpreted in the language with the help of a portable feature that makes it beneficial to use. In brief, write code on the Mac platform and run it smoothly on Windows as well. There is no need to write a code in multiple languages with it.
- Data Scientist
- Develops Engineer
- Python Developer
- Senior Software Engineer
- Software Developer
- Software Engineer
Future Technologies Counting On Python with machine learning
Generally, we have seen that python programming language is extensively used for web development, application development, system administration, developing games, etc.
But do you know there are some future technologies that are relying on python? As a matter of fact, Python has become the core language as far as the success of these technologies is concerned. Let’s dive into the technologies which use python as a core element for research, production, and further developments.
(1) Artificial Intelligence (AI)
Python programming language is undoubtedly dominating the other languages when future technologies like Artificial Intelligence(AI) comes into play.
There are plenty of python frameworks, libraries, and tools that are specifically developed to direct Artificial Intelligence to reduce human efforts with increased accuracy and efficiency for various development purposes.
It is only the Artificial Intelligence that has made it possible to develop speech recognition systems, autonomous cars, interpreting data like images, videos etc.
We have shown below some of the python libraries and tools used in various Artificial Intelligence branches.
- Machine Learning- PyML, PyBrain, sci-kit-learn, MDP Toolkit, GraphLab Create, MIPS etc.
- General AI- pyDatalog, AIMA, EasyAI, SimpleAI, etc.
- Neural Networks- PyAnn, pyrenn, ffnet, neurolab etc.
- Natural Language & Text Processing- Quepy, NLTK, gensim
(2) Big Data
The future scope of python programming language can also be predicted by the way it has helped big data technology to grow. Python has been successfully contributing in analyzing a large number of data sets across computer clusters through its high-performance toolkits and libraries.
Let’s have a look at the python libraries and toolkits used for Data analysis and handling other big data issues.
- Pandas
- Scikit-Learn
- NumPy
- SciPy
- GraphLab Create
- IPython
- Bokeh
- Agate
- PySpark
- Desk
(3) Networking
Networking is another field in which python has a brighter scope in the future. Python programming language is used to read, write and configure routers and switches and perform other networking automation tasks in a cost-effective and secure manner.
For these purposes, there are many libraries and tools that are built on the top of the python language. Here we have listed some of these python libraries and tools especially used by network engineers for network automation.
- Ansible
- Netmiko
- NAPALM(Network Automation and Programmability Abstraction Layer with Multivendor Support)
- Pyeapi
- Junos PyEZ
- PySNMP
- Paramiko SSH
