Deep Learning l Data Science Python Course

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What are Data Science and Deep learning?

To extract value from data, data science combines multiple domains such as statistics, scientific methodologies, artificial intelligence (AI), and data analysis. Data scientists are individuals that use a variety of talents to analyze data acquired from the web, cellphones, consumers, sensors, and other sources in order to generate actionable insights. Data science is a branch of study that tries to extract meaning and insights from data using a scientific method.

Deep learning refers to a class of machine learning techniques that employ numerous layers to extract higher-level features from raw data. Lower layers in image processing, for example, may recognize edges, whereas higher layers may identify human-relevant notions like numerals, letters, or faces.

A new way to solve problems that were previously thought impossible. Deep Learning is something similar to Artificial Neural Networks (ANN). ANNs are inspired by biological neuron cells, and they have been successfully applied in computer vision, speech recognition, and language translation. However, unlike their counterparts, Deep Learning algorithms do not use human brains or the same parts of the brain that neurons use. Instead, they use deep neural networks that mimic those brains’ abilities.

In some ways, AI has already made an impact on society. Automation is improving at an incredible rate. As we get closer to robots and self-driving vehicles, Deep Learning promises to make the lives of people easier and safer; creating jobs that previously did require humans to perform, allowing them to stay competitive in the workplace. In addition, it also benefits consumers and businesses alike, who can find new ways to meet customer demands through smarter targeting, higher quality products, and the ability to improve revenue streams and increase customer experience. Let us now consider how this approach could benefit companies from an environmental perspective, too.

Both data science and Deep learning are popular buzzwords these days. These two names are frequently used interchangeably, however, they are not synonymous

What is Python?

Python is an open-source programming language that may be used on Linux, Mac OS X, and Windows. This programming language is considered an elementary language, especially for novices, and can handle any data science or data analysis assignment.

Python is a high-level programming language that is known for having a syntax that is virtually natural language-like. It's used for a variety of things, including:

Data analysis

Web application development


More about Deep learning:

The majority of modern deep learning models are based on artificial neural networks, specifically convolutional neural networks (CNNs), though they can also include propositional formulas or latent variables organized layer-wise in deep generative models like deep belief networks and deep Boltzmann machines.

Each level of deep learning learns to turn the data it receives into a little more abstract and composite representation. The raw input in an image recognition application could be a matrix of pixels; the first representational layer could abstract the pixels and encode edges; the second layer could compose and encode edge arrangements; the third layer could encode a nose and eyes, and the fourth layer could recognize that the image contains a face.

Benefits of using Python in Deep learning and Data sciences:

Huge Library- The extensive and gigantic library and other useful functions make the developers and programmers keep using python in data sciences, Machine learning, and all other fields. Development and implementation of ML are always difficult without the presence of a vast library of tools and functions. Python has a separate toolset built only for Machine learning and Data Science.

Ease and Simplicity- The first and most important thing which hits the mind of a developer when he hears about Python is simplicity. Python is very easy to understand and has short syntax and readable code which is useful for Data Science and Machine learning. The main aim of machine learning is to reduce human effort and python is also playing an important role in fulfilling by reducing the effort in coding. This simplicity is the best reason that most developers choose python for machine learning.

Platform Independence- Python is platform-independent and allows programmers and developers to use the same code, developed on one device, to use this same code on any other platform. This feature brings a lot of fame to Python. As, Python supports all popular platforms including Windows, Mac, and Linux operating systems.

Purpose of this course

This is an excellent course for those who are new to Deep Learning and Data Science and wish to learn everything about it. Thank you to the lecturers for clearly and simply explaining everything. This course was created by expert Data Scientists to help you master difficult theories, algorithms, and code libraries in a straightforward manner.

Advantages of taking this course on Brainmeasures:


The specialists are highly qualified, and this course includes some excellent professors who present you with real-world knowledge based on their own experiences.

The cost of each course is also reasonable for students.

You can take this Deep learning and Data science Python course from Brainmeasures' many courses if you wish to start your job as a novice after you graduate. The classes are tailored specifically for recent grads.

Brainmeasures does not provide any mini-courses. Instead, you learn and practice everything in great depth. This institution would be ideal for you as a working professional. Big companies will hire you and you can start your professional career after learning courses on Brainmeasures.

Benefits of Deep Learning and Data Science:

Data science and Deep learning each have their own set of benefits and will allow you to learn a great deal. Given the aforementioned advantages of boot camps, it's safe to say that they're something you should always strive for. Just a word of caution: you should not completely rely on boot camps; instead, you should make every effort to develop a desirable resume and advance your career. Experts agree that we are still in the early stages of AI development and that it hasn't yet reached its full potential. However, due in large part to the so-called deep learning revolution, technology has recently become both powerful and economical enough to enable widespread adoption. That's why now is an excellent time to examine the various ways it may be utilized and the outcomes it's currently giving to various firms, many of which would not have been conceivable even a few years ago.

Improving communication

Fraud prevention

Preventing infrastructure failures

Interpreting IoT data in real-time

Pattern Recognition

The benefits that data science and Deep learning may offer to firms in numerous sectors are demonstrated in the examples above. Furthermore, we are only now beginning to make use of AI's capabilities. Companies are continually discovering new advantages of these technologies, boosting services, and expanding their company value. Data science and Deep learning solutions should be considered by any company that deals with data processing and automation and wants to expand its operations.

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Course Syllabus

Getting Started 11 lectures 17 mins
Introduction Preview 01:42
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