About this course
Python is an easy to learn, powerful programming language. You can use Python when your data analysis tasks need to be integrated with web apps or if statistics code needs to be incorporated into a production database.
Being a full-fledged programming language, Python is a great tool to implement algorithms for production use. There are several Python packages for basic data analysis and machine learning. In this course, you will learn about two popular packages in Python: NumPy and Pandas. These are the essential foundational packages that are required for basic data manipulation.
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Python for Machine Learning
- Intro to Numpy
- Joining NumPy Arrays
- Numpy Intersection & Difference
- Numpy Array Mathematics
- Saving and Loading Numpy Array
- Intro to Pandas
- Pandas Series Object
- Intro to Pandas Dataframe
- Pandas Functions
Get Python for Machine Learning course completion certificate from Great learning which you can share in the Certifications section of your LinkedIn profile, on printed resumes, CVs, or other documents.
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What is Python?
Python is an object-oriented programming language that is preferred by most of the developers. Professionals who are freshers in the field of programming, they start learning and practicing code with Python. Its simplicity, versatility, and the community support are the most important features for Python’s popularity. It has a wide range of libraries to further simplify coding in Python.
What is a Library in Python?
A library is a collection of pre-combined codes that are used to reduce the time required to code. Libraries eliminate the need for writing codes again and again from scratch by accessing pre-written codes that are used frequently. Similar to a physical library, a Python library is a collection of reusable resources having a root source. This makes the foundation of most of the open-source python libraries.
Why should you choose Python?
You should choose Python because it has become the most preferred programming language and enables machine learning applications. Python is swift as compared to other programming languages. Syntax is simpler and the pre-existing libraries eliminate the need for coding every logic from scratch.
Python is a swift compiler and since it is java-based, programmers will be able to extend its applications beyond analytical research, analytical modelling, and statistical modelling. Web applications that are created using Python can be integrated directly to the analytical models in the background.
Python could be easily integrated with other platforms and programming languages. With this common object oriented programming architecture wherein existing IT analysts, IT developers, and IT programmers can easily transition to the analytics domain.
As the structure of coding in Python is object-oriented programming architecture, it has excellent documentation support.
7 Reasons for Choosing Python
- Readable and Maintainable Code
- Multiple Programming Paradigms
- Compatible with Major Platforms and Systems
- Robust Standard Library
- Open Source Frameworks and Tools
- Simplified Software Development
- Test-Driven Development
Importance of Python for Machine Learning
Machine Learning applications are improving traditional processes across industries and solving some of their pressing problems efficiently. Enabling better personalisation, improved search functionality, and smarter recommendations and Python has been instrumental in all developments.
The characteristics of Python that make it an ideal programming language for machine learning are:
- Simplicity and Consistency
- Range of libraries and frameworks
- Platform Independence
- Visualisation Options
Simplicity and Consistency
Python provides a concise and reliable code that is easy to learn and implement. Machine learning models run on complex workflows and algorithms and Python’s simplicity enables the developers to write error free reliable codes. When working with Python, the effort and time spent on understanding and implementing the code reduces. Developers are able to focus their energies on solving ML problems instead of focusing on the technical nuances of the programming language.
Also, Python enables collaborative implementation . Multiple developers can together work on a single project at the same time. It is a general-purpose language, therefore, it performs complex ML tasks seamlessly and allows quick prototyping and product testing.
Range of Libraries and Frameworks
It is tricky to implement machine learning algorithms. But with a well structured and well tested environment, coming up with strong coding solutions is possible. Some of the libraries are:
- Scikit Learn: used to implement linear and logistic regression, classification, clustering, and more
- Tensorflow: for setting up and training artificial neural networks with massive datasets for deep learning applications
- Keras: allows fast calculations and prototyping
- Matplotlib: helps with visualisation using charts, 2Dplots, and histograms
- Numpy, Scipy, Pandas: any of the three could be used for high performance scientific computing and data analysis
- NLTK: helpful while working with computational linguistics and natural language processing and recognition
- Scikit-image: for image processing
- Pybrain: for unsupervised and reinforcement learning and neural networks
- Caffe: allows 60+ million images a day for deep learning applications
- StatsModels: for data exploration and statistical algorithms
Python is supported by all platforms including Linux, Windows, and macOS. Python codes are utilised to write standalone executable programs. Also, software built on python could be easily distributed and used on these platforms without the need of an interpreter.
There are organisations that use their own dedicated machines to train their ML models. For such organisations the fact that python is platform-independent makes it easier and cheaper to train the models.
Python gives programmers a choice between OOPs and scripting. It eliminates the need for recompiling source code so that the developers are able to implement changes with immediate results. Developers can also combine python with other programming languages to reach their desired goals.
Python provides an option of choosing programming styles as well. The programming styles are:
- Imperative Style
- Functional Style
- Object-oriented Style
- Procedural Style
Python has the least possibility of errors in comparison to other programming languages.
Python’s vast range of libraries provide better visualisation. For those who work in the domains of artificial intelligence, machine learning, or deep learning, it is important to represent data in human readable form. Hence, Python is very important for these professionals and developers.
Matplotlib is a Python library that allows data scientists to represent data in the form of histograms, plots, and charts for effective representation. Such visualisations provide better comprehension for the stakeholders who carry less or moderate knowledge of machine learning.
These are some of the reasons why Python is the perfect choice for machine learning as compared to other programming languages. Now let us see what is the best way to learn Python?
Best Way to Learn Python?
Python is very easy to learn and hence very popular. There are many python machine learning tutorials and machine learning with python courses available online. To start with, you can sign up for a python and machine learning course for beginners. Once you understand the basics and what all you need to learn in python and machine learning, you can sign up for an advanced professional course in these domains.
You should also work on machine learning projects in Python and building machine learning systems with Python. This will help you develop a better understanding of the subject. One would need around six to eight weeks to learn the basics of Python which include syntax, keywords, functions, classes, data types, coding basics, and exception handling.
Depending on the nature of work, one can develop the advanced skills sets specific to their role. Advanced skills include multithreading, socket programming, database programming, synchronisation techniques etc.
About the course
Sign up for the Python for Machine Learning free course on Great Learning Academy to kick start your career in this domain. The course has 5 hours of video content where we explain the basics of Python and its applications in machine learning. Along with a quiz, you can also test your learning once you complete the course.
The curriculum provides an introduction to machine learning with python. The video explains python machine learning by example. Upon completion, you will get a certification from Great Learning which you can share on your LinkedIn profile, on printed resumes and CV, or other documents.
Along with the basics, the course also covers libraries that are used in machine learning and their applications in ML algorithms. Enrol for this beginner level free python for machine learning course now.