Neural networks are systems that operate similarly to the human brain. Currently, neural networks are used daily – even without us realizing it. For example, when we use our mobile assistant to perform a search- such as Siri, Google, or Amazon Web- it is all neural-network driven. In computer games, neural networks are also utilized – such as how the system adjusts its players, maps applications, processes map images, and finds the shortest routes to reach the destination.

Neural networks can perform any of the following tasks:

Translate text

Control robots

Recognize speech

Read handwritten text

Identify faces

How does the Neural Network Function?

There are three different layers to neural networks:

Input layer: Picks up the input signals and passes them on to the subsequent layers.

Hidden layer: This layer does every feature extraction and calculation required. Often, we will find more than one hidden layer in a single neural network.

Output layer: This is the final layer and has the job of delivering the result

To better understand these layers and their workings, let’s take a real-life example of how traffic cameras capture the license plates of vehicles speeding on the road. Pixels, which make up the picture, are fed into the input layer in arrays. Each neuron has activation, or an assigned number, ranging from 0 to 1. For the white pixel, it is 1, and for the black, it is 0. Next, the input layer passes these inputs to the hidden layer. All the interconnections have weights assigned to them randomly, which then is multiplied by the input signal. Lastly, a bias is added to all these weights.

The weighted addition of the inputs is then converted to the information of the activation function- this function decides which nodes should fire for the feature extraction.

Here we will take a look at the different types of activation functions that are there:

Threshold function: This function is deployed when we don’t want to worry about mid-level uncertainty.

Sigmoid function: The sigmoid function is useful when the model is required to predict probability.

Hyperbolic Tangent function: This function is very similar to the sigmoid function; however, it ranges from -1 to 1.

ReLU (rectified linear unit) function: This rectified linear unit (ReLU) function works in such a manner that it gives the function value, however, if the value is over 1, then it will just be one, and similarly, if the value is less than 0, it will be just 0. This ReLU function is the most commonly used among all.

Now that we know all the activation function types, let’s explore more neural networks. So finally, the model, after having applied a suitable application function to the output layer, will predict what the outcome is. The error rate in the output is minimized through the adjustment of weights, and the error itself is back-propagated through the network. The weights are constantly adjusted until they perfectly fit all the training models that it has been put in. Furthermore, the output is contrasted with the original result, and numerous iterations are done to attain maximum accuracy.

Neural Networks and their Types

There are mainly six different types of neural networks, as discussed below:

Feed-forward neural network: This is the example we just explored where data travels in a uni-directional manner- from input to output. It is also the simplest form of artificial neural network.

Radial basis function neural network: Here, the data point is classified based on distance from any central point. We often want to group things and contract a centre point when we don’t have any other training data. This network groups similar data points and has its application in power restoration systems.

Kohonen Self-organizing Neural Network: Discrete maps that are comprised of neurons have inputs that are vectors of random input. Applications of this network include recognizing patterns in data such as medical analysis.

Recurrent Neural Network: Here, the hidden layer saves the output such that it becomes part of the new output and can be used for future predictions.

Convolution Neural Network: Here, the input features are filtered and taken in batches, therefore, allowing the network to remember an image.

Modular Neural Network: This model consists of multiple neural networks which function together to get an output.

Keras is an efficient software that allows users to create artificial neural networks with the Python programming language. Many data scientists use Python because it is essential for the Keras software. If you are new to Python and data science, you can opt for a Data Science and Analytics course or data analyst training to learn how to use Python more efficiently.

Aspirants in this Data Science and Analytics domain can benefit significantly by enrolling in the Post-graduate Program in the Data science and analytics course of IMARTICUS Learning.

How To Learn Python Online and Set Up a Local Programming Environment

Python is one of the best programming languages that you will come across in the world. It is not as cumbersome as Java, but it is not very easy either. However, there is one thing for sure, learning Python can be highly advantageous for your career. Once you learn Python online, you can build programmes and automate several of your daily tasks.

Also, knowing the intricacies of programming could open up new opportunities for you. With this training, you could opt for opportunities in data science, data analytics, machine learning, and artificial intelligence domains. All in all, Python can offer you a chance to do whatever you want to.

How to set up a local programming environment for Python?

If you are using this programming language every day, then it is essential to set up your Python local development environment correctly.

You need to configure Python environments on your local servers (personal computers). For that, you need to choose one environment that everyone else uses to work on a project. Otherwise, you might be unable to compile and execute the Python programs you have built. Unless everybody does not have the same environment, they cannot collaborate and build a project together. If these steps are not precisely followed, then there might be a discrepancy in the collaborative work. Then there will be difficulty in switching between working on multiple projects seamlessly.

Below are the steps you need to follow while installing the local programming environment.

1. Download the Python version you want from https://python.org. After visiting the Python homepage, click on the type of installer you want, 32-bit or 64-bit. 
2. After that, you need to run the .exe file of the installer. Within a few minutes, Python and the Python environment will be installed on your local server. 
3. Up next, you need to start the command prompt and check whether Python has been installed or not. The path where the files have been downloaded will be shown in the installer. 
4. To set up the local environment, you need to tinker around and download PyPI. Notably, PyPI is also called Python Package Index. This package index helps the programmer to utilise multiple options in choosing open-source Python packages. 
5. Now you will need to learn the way to manage your local development environment when working on individual, team, and commercial projects. For that, you will need to use pip or the Python Package Installer. Pip will help you in managing your Python packages that are inside virtual environments. 
6. Finally, the last step is that you need to download an IDE (integrated development environment), where you can build a program in Python and run the code. So, you can learn how to use an IDE before you go ahead and download it. You can use simple IDEs like Notepad and Notepad++ for Python. But, we would recommend you use something like PyCharm, which is quite advantageous as it helps to point out the bugs in your code and is, therefore, a big aid in improving your workflow. But, it has a slow execution speed. All the coders and programmers use Visual Studio Code, which can be used to program in C++, Java, JavaScript and Python. It has all the features of an advanced IDE and helps set up the Python environment easily. You can also work efficiently in this IDE and use keyboard controls for various functions.

These were some of the steps that will come in handy while setting up a local programming environment. Now, we will head over to the next step, where we will see where and how to learn Python online.

How to learn Python online?

Learning Python online is not difficult these days as there are multiple online resources where you can teach yourself Python in the easiest way possible. You can use tutorial websites to teach yourself Python. Or, you could also take up various video courses that are available on online coaching websites.

But, it would be better if you learn from somebody who is from the industry. Faculty who are already trained in teaching understand the right pedagogy to teach programming and the junctions to test the person, which will help to solidify the concepts. Therefore, Imarticus Learning’s PG in Data Analytics is worth consideration because it teaches about all the complex topics in the easiest way possible and builds a good foundation for Python. This post-graduate program in data analytics and machine learning shapes you for professional life with live projects and industry experience.

Hopefully, you have got an idea of the process of learning Python online and which analytics and machine learning course can help you in this process. So, enrol for a course of choice and start setting up your local programming environment to practise and excel at this subject.  

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The Anatomy Of A Data Analytics Course With Placement

Even a beginner can learn python online: Here’s how

When you’re just starting out in the world of coding, the syntax of other languages might be incredibly intimidating. Python, on the other hand, with its simple setup and readability, encourages you to think like a programmer and saves you time in constructing a large syntactic format. Amazing as it is, typing and compiling code in this language is like a breeze because of its straightforward syntax. If you want to learn python online, you must know that it has never been easier than now.

How should you start to learn Python?

Learn the Syntax

Identifiers and Keywords in Python

• Identifiers: Classes, functions, variables and other programmatic constructs are all given unique names, known as identifiers. It is useful for setting apart distinct things in the world. The names of Python objects such as variables, functions, classes and modules are called identifiers. Any character from a to z, or the underscore (_), may serve as the first character of an identifier, which may then be followed by a combination of letters, numerals, and/or additional underscores (0 to 9). Names in Python cannot include any punctuation, including @, $, or %.
• Keywords: Python’s equivalent of words that are reserved are called ‘keywords’. In other words, we can’t use a keyword in any way that would make it an identifier (such as a variable or function name). They serve as the foundation for the Python language’s grammar and organisation. Python is case-sensitive when it comes to using keywords. In Python 3.7, you’ll find a total of 33 different keywords. This figure is subject to minor changes over time.

Indentations in Python

The spaces at the start of a line of code are called indentation. Python places an emphasis on proper indentation, which is mostly superficial in other languages. Python code blocks are denoted by indentation. Hence there is no need to use ‘{’ and ‘}’ to define blocks of code.

Python Data Types

• Numerical data types: Python can handle simple numbers, complex numbers and floating-point numbers. int, float and complex are the Python classes that define them.
• Sequence data types: With Python, lists are one of the most flexible ways to interact with several data items simultaneously.

Python’s tuple data structure may be thought of as a list. While we may modify list items after they have been assigned, tuple elements cannot be modified after the assignment.

• String data type: In computing, a string refers to a sequence of characters. A Python string is a set of Unicode characters. Unicode was created so that every character from every language may be encoded consistently and reliably.
• Mapping data type: In Python 3.7 and beyond, a dictionary is an organised list of elements. A dictionary stores information as a key/value pair for each entry. This is because dictionaries are designed to provide results after the key has been determined.

Flow Control in Python

In programming languages, decision-making statements determine the course of programme execution. The ‘if’, ‘elif’ and ‘else’ statements are used to make decisions in Python.

Loops in Python

In certain cases, you may want to run the same piece of code several times. Languages’ varied control structures enable more complex execution routes. Python ‘while’ and ‘for’ loops make it easy to manage looping situations.

Functions in Python

A function is a collection of connected instructions that may be used again and again to accomplish a certain task. Your programme will benefit from the increased modularity and increased code reuse made possible by functions. It is common knowledge that in addition to Python’s numerous predefined functions like print(), you may also write your own. User-defined functions are the common term for such functions.

Create Projects

Python is not always easy to learn. Reading a book might help you understand the material, but putting it into practice can be difficult. Worse, you might spend a lot of time studying syntax just to get bored or lose interest. Making Python projects improves your odds of success. That way, you are able to put your skills to the test in a realistic setting.

Conclusion

Getting started with Python is simple, and after you’ve mastered the language, you’ll be well-positioned to pursue a rewarding career in the expanding field of data science and analytics. Better still, you’ll be in high demand as a professional since new uses for AI mean a higher need for Python programmers. 

If you are a person who is intrigued by the domain of data science and data analytics, you should definitely consider the best data analytics certification course brought to you by Imarticus. The Post Graduate Program in Data Analytics & Machine Learning is an extremely comprehensive course to learn data analytics. So enrol right now and do not miss this opportunity to be an asset to the corporate.

What you’ve not considered before: Learn python online

The world is evolving every day and we are moving towards a tech-savvy world, where almost everything is digital. Even nowadays, numerous software and programming languages are used to conduct various tasks. Among them, one of the popular choices of programming language is python. This language is widely accepted and practiced to carry out important works like data analysis.

Data analytics and the python language share a relationship that is interesting to know. Below is some information that will expand your knowledge of python and its relation to data analytics. 

What is Python?

To briefly explain, python is a programming language used for various purposes like web development, system scripting, software development, and even in mathematics. There are multiple applications of this programming language as well. Starting from rapid prototyping for software development to connect database systems, it is also used on servers to create web applications. Python can be used just as a scripting language or can be compiled to byte code for building gigantic applications. 

One of its interesting uses lies in mathematics where the python language can handle big data and solve complex mathematical problems. 

When it comes to applying python, the different platforms available are Windows, Linux, Mac, Pi, Raspberry, and many more. 

With every passing day, the use and popularity of python are increasing and becoming a huge part of our future. Thus, it will be a good call to learn python online and gain the skill that is in demand.

Facts about Python

Here are some other facts that make python distinct from others and an easy language to learn and apply; 

• Python has a similar structure to that of the English language which makes it easier for an individual to learn it. 
• This structure also helps programmers to write programs in fewer lines compared to any other programming languages. 
• Interestingly, python runs on an interpreter system that allows the coder to run all his/her codes as soon as written. This signifies that prototyping can be really quick using python language. 
• We can easily integrate the python language with C, C++, CORBA, JAVA, and ActiveX. 
• To understand in simplest terms, a sizable collection of libraries in python is completely dedicated and appropriately used for valuable analytics and to solve complex calculations. 

Why should you learn Python Online?

We already know about the popularity of python and its universal appeal. This is one reason to learn python online from the best platform to gain good command over a language that applies from data science to app development. 

Even if you compare python with any other programming language, it is one of the most versatile, making it the popular choice of people.

Among the various reasons behind learning python, one evident reason is its simpler approach. Comparatively, it has a simpler syntax and high-readability factor, making it beginner friendly. Another quality of this language is its flexibility across other programming languages. For example, you can integrate it with JAVA and .Net, and perform cross-language operations as well. 

There is no other language that can compete with python when it comes to the numerous useful libraries. The python language comes with an assortment of libraries like NumPy, Matplotlib, StatsModels, Pandas, and many more. Because of the availability of this vast collection of libraries, python has more uses and its functions have multiplied significantly over the years. 

Adding to the extensive list of qualities of python, its application in the field of data science is commendable. There is a huge role of the python language in data analytics as it is easy to learn and understand. 

Python and Data Analytics

Till now, we know little about the application of python in complex mathematics. This function of python language enables it to be a useful resource in data analytics. All the above-mentioned qualities of python make it the best choice for data analytics. Apart from those features, it is also well-known for its graphics and visualisation. As it is quite easy to comprehend visual information, python becomes the preferred language because of its plethora of different visualisation options. 

Besides this, there are multiple built-in analytics tools in python to process complex data. These tools can penetrate patterns, provide insightful information, as well as correlate those in extensive sets. 

To summarize this relationship, we can say that the language’s ability to analyse huge data sets in no time and carry out repetitive tasks makes it the right choice for data analytics. Thus, it is worth it to learn from a data analytics course with placement that can provide you with knowledge and experience, all at the same time.

Conclusion

It is widely known that data analytics is a huge field of operation that deals with complex data to derive valuable information from it. And to work out these enormous tasks, python is internationally recognized. It assists in managing data with its versatile nature, and multi-features.

So, to be an asset for a company in the future, it is an excellent choice to learn data analytics which includes python as well. When you learn data analytics, you will understand how valuable it is in this field. 

Lastly, to learn data analytics it is advisable to opt for a postgraduate program in data analytics & machine learning to gain the appropriate knowledge of analysis combined with the skills of python. 

Learn Python online: Turn python scripts into machine learning tools

In 2022, you will be able to learn Python online and turn your scripts into powerful machine learning tools. This is an exciting time for Python developers, as the language is becoming more popular than ever for data science and machine learning applications. This guide will teach you everything you need to know about Python for data analysis and machine learning. So what are you waiting for? Start Learning Python today!

Python is a programming language with many features, including an intuitive syntax and robust data structures. It’s no wonder that Python is one of the most popular languages today.

But what makes Python so unique? And why should you learn it?

Here are five reasons to start learning Python in 2022:

• Python is easy to understand.
• Python is a universal language that you can use for many different purposes.
• A python is a powerful tool for data science and machine learning.
• Python is growing in popularity, so learning Python will make you more marketable.
• Python is fun! Python is the best language for beginners and experienced programmers alike. 

So why not start learning Python today? You might get surprised at how quickly you can pick it up.

How to turn your python scripts into machine learning tools?

A python is a powerful tool for automating tasks, and it has many libraries that allow for easy integration with machine learning tools. Internal tools for every significant machine learning project are inevitably buggy and unmaintainable. 

Fortunately, there is a better way by using Python’s Machine Learning Server; you can turn your python scripts into machine learning tools that are scalable and easy to use. 

#1: Embrace Python scripting: The best way to learn is by doing

Streamlit applications are simply scripts that run from top to bottom. There is no hidden state. You may factor your code using function invocations. You may create Streamlit apps if you know how to write Python scripts.

#2: Treat widgets as variables. There are no callbacks in Streamlit! Every contact repeats the script from beginning to end.

#3: Reuse data and computation. What if you need to store a lot of data or do complicated calculations? The key is to reuse data safely across runs. Streamlit provides a cache primitive that behaves like a persistent, immutable-by-default data store that allows Streamlit apps to reuse knowledge securely.

#4. Let Streamlit do the heavy lifting. There’s no need to fiddle with HTML, CSS, or JavaScript. Instead, focus on your app’s logic and let Streamlit take care of the rest.

Discover data analytics course in India with Imarticus Learning

This data analytics course with a placement guarantee teaches students how to use Data Science in the real world and create sophisticated models that produce essential business insights and projections.

Course Benefits for Learners:

• Students must understand data analytics and machine learning fundamentals and the most popular data science tools and strategies.

• Learners obtain a machine learning online course by participating in 25 real-world projects and case studies led by corporate partners.

• Data science and analytics programs are among the most sought-after talents in today’s marketplace, so recent graduates and early career professionals should think about enrolling in data science and analytics courses.