The course titled, “Deep Learning Prerequisites: Linear Regression in Python” aims to enlighten the audience on the basics and the pre-requisites that are needed for learning some of the hottest technologies of the 20th century like Machine Learning, Artificial Intelligence, Data Science, etc. The machine learning has many concepts like Neural Networks that enforces Deep Learning methodologies that forms the base for Computer Vision/Machine Vision. There are many reasons why one should prefer learning this course and the one can be encouraged to take up this Deep Learning Prerequisites: Linear Regression in Python course to aim for better salary packages and to lay a strong foundation to learn the concepts of Deep Learning.
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Udemy sets forth an example of how a startup that could provide high-quality content at the best possible prices and become one of the forerunners even being a relatively newer one. The students can reach their teachers any time for the clarification of doubts that they can learn better. The doubt clearing only enhances the thirst for knowledge. The enthusiastic teacher’s add-on to a plus factor. The use of graphics in the content is a great idea as it makes learning interesting. The simple language that has been used here is an advantage for global students
Before diving into the learning of pre-requisites of Deep Learning, one should know what Deep Learning means and why it is important to learn. Machine learning is nothing but a set of algorithms that are used to read and learn the data to make future predictions from the data. Machine Learning is the subset of Artificial Intelligence. The machine can learn and predict and act according to base on the history of data that has been fed in. Support Vector Machines and Artificial Neural Networks concepts are used to make the machine learn by itself. The Artificial Neural Network is the base for Deep Learning.
Now one might think of why when there is already Artificial Neural Network, what is the need for Deep Learning? Artificial Neural Network comprises of three stages namely the input, the hidden layer and the output. This holds good only when the input that has been fed into is of simple nature and lesser in size that one hidden layer is more than enough to process the input, when the input is of a more complex nature, and there are multiple inputs, then there has to be number of hidden layers or deep layers that can process the input and provide the necessary output. This gave rise to the importance of learning Deep Learning methodologies.
The process of registering for this course is very easy if the person already has an account on Udemy.com. The enroll button has to be clicked to attend this Deep Learning Prerequisites: Linear Regression in Python course.
Once a person registers for this course, the candidate will receive two emails. One mail will be the one that thanks the candidate for registering for the course and the second one will be from the instructor himself that confirms if the candidate is already equipped with the pre-requisites or not.
If the candidate is not very well equipped with the pre-requisites, then the instructor would advise him to focus on getting the basics right so that the candidate can understand the course better and otherwise the course would not make much sense to the viewer.
The list of pre-requisites and a brief description of why the pre-requisites play a vital role in the learning of this course is given below.
Deep learning is nothing but a genre of Machine learning algorithms.
Linear algebra is the branch of mathematics concerning vector spaces and linear mappings between such spaces. It includes the study of lines, planes, and subspaces but is also concerned with properties common to all vector spaces.
Why do we study Linear Algebra?
One has to understand the design and analysis of algorithms to learn the concepts of probability in computing. The probability concepts are very important these days in the field of computer science as machine learning, Data Science, and Data mining. Anything that is connected to the research and analysis of data needs knowledge of probability.
Basics of Python Coding:
Many languages are used in Data Analytics; Python is one language that is very efficient. Thus, it is very important to read and understand the concepts in Python.
Why should one take up this course?
These are some of the reasons why one should take up this course.
Deep Learning is projected to be the base for Visual Computing. Computer Vision is one area that has a huge market in the upcoming years.
A market that has a value of about 6 billion dollars has the following areas of importance like Media, Surveillance, Automotive, graphics, etc.
A market that has a value of about 3.5 billion dollars in Medical Image Analysis
Any language can be mastered only when one is strong with the pre-requisites, this course serves that very purpose in a very short span of time.
If you’re interested in knowing more about the Numpy Stack in Python, then continue reading as followed:
What are the topics that are covered in this course?
The prerequisites that are important for Deep Learning, Machine Learning and Data Science are discussed in the course. The instructor has developed this course after receiving lots of requests from the people not being able to understand some of the codes that are written for Machine Learning, Data Science, and Deep Learning concepts. Numpy stack knowledge is needed to convert concepts into code.
The core area of Numpy Stack is Numpy Array. This array is not similar to that arrays that are used in C, C++, Java. One can know that matrices are the basis of the methods of feeding data into the process. It is not as similar to the input statements that are used in C, C++ or Java. This is because the inputs are unlabeled and there is junk of a lot randomness in quantity. This forces the inputs to be accepted in the form of vectors.
Thus, this Numpy array is more or less like a mathematical operation. Many operations like the addition, subtraction, inversion, determinant of a matrix can be performed effortlessly using this Numpy stack. This is easier to work on and efficient than using the lists feature in Python.
When the Numpy Stack can work on the operations like additions, subtraction, etc., more of statistical operations are performed using Scipy. The various concepts that one would have learned in their under graduation in subjects like Digital Signal and Digital Image Processing, the statistical Hypothesis testing, the mathematical concepts like Convolution theorems, Fourier Transforms, etc., are used as tools in Scipy. Thus, any complex operation is performed by Scipy.
To make the learning of Pandas easier, if one knows how Data loading is done in SQL and one is familiar with the concept of a data frame in R, then it becomes very easy for one to learn Pandas. As the concepts are used in these two areas are being used in Pandas.
The plotting is very necessary to view the data. This is done by knowing how MatPlotlib works. The images are shown using the various plotting methodologies like histogram, line plot and scatter plot.
If one does not equip himself with the working of these tools, it becomes very difficult to study further on how things or the concepts are written as codes as several functions have their libraries and the percussive in these stacks and tools. Thus, before one learns on how to write codes from concepts one should know where the ideas have their base in and how things are being perceived.
Salient Features of this course
The following are some of the salient features of this course.
Top-Rated Deep Learning, Machine Learning & Data Science Courses Recommended here:
Who is the target audience?
This Deep Learning Prerequisites: Linear Regression in Python course is for those who want to learn deep learning. The deep learning is projected to be one of the top growing fields at present. This makes the course a valuable one. This course is not for those who are alien to computer science and mathematics concepts.
This course would require one to have good knowledge about the basics of programming languages that are used widely in the field of data analytics. This course is mostly enrolled by the students and the ones who work in the industries and those who wish to have a growth in their career.
This course caters to students from various backgrounds of engineering and science as one can see. There are concepts from Mathematics, Image Processing, Theoretical Computer Science as well as other core areas of computing like Databases, etc..
This course helps one to gather all the knowledge that one has already learnt, and it infuses one the prerequisites that helps one to master some of the growing technologies that is projected to hold some of the best jobs in the 21st century. All these reasons are sufficient enough to prove why one should take up this course and gain a lot of insights and become successful.
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