Buy AI & Machine Learning for Professional Deal at Eduonix. This Is BEST AI and Machine Learning Course for Professionals Users in 2020. With This AI and Machine Learning for Professionals Course on Eduonix Learning Solutions You Will To Become Technologians in 2020.
Why This AI and Machine Learning for Professionals Deals
Be an advance Artificial Intelligence practitioner with our unique course bundle which covers courses for students at all stages of learning. Right from the basics such as mathematical foundations and algorithmic foundations, to more complex concepts like NLP.
AI and Machine Learning for Professionals Course Curriculum
Course 1: Machine Learning For Absolute Beginners
Machine learning is changing the way we design and use our technology. It has slowly spread it’s reach through our devices, from self-driving cars to even automated chatbots. To define machine learning in the simplest terms, it is basically the ability to equip computers to think for themselves based on the scenarios that occur. This is the basis of artificial intelligence.
With machine learning as the future of technology, getting your hands on this type of development is crucial. However, it isn’t easy. Machine learning is a complex concept that uses algorithms to design coding that will help the computers decipher a lot of data. This is why we have designed this course. If you are interested in mastering machine learning using a simple and easy approach, then this course is for you!
The only requisites for this course includes having a strong mathematical background and familiarity with Python coding principles. Everything else is provided within this course. Starting at the very beginning, this course will help you breakdown machine learning into simple and easy to understand concepts.
The course covers a variety of different machine learning concepts such as supervised learning, unsupervised learning, reinforced learning and even neural networks. But that’s not all. In addition to understanding the theory behind machine learning, you will then actually use these concepts and implement them into actual projects to see how they work in action!
The course also comes with quizzes at the end of each section to help solidify your understanding for the subject. It will also help you valuate your learning of the subject.
At the end of this interactive and hands-on course, you will have everything you need to actually get started with understanding machine learning algorithms and even start writing your own algorithms that you can use for your own projects.
Let’s take a look at everything you will find in this course.
What you will learn in this course:
- What is machine learning
- Breakdown of important concepts required in machine learning
- Different types of machine learning
- Detailed analysis of the different types of machine learning such as unsupervised learning, supervised learning, reinforcement learning, neural networks, and so on
- How to integrate the algorithms in actual Python Projects
- Quizzes at the end of each unit to help evaluate your learning
- If you want and easy introduction into machine learning, then this course has got you covered. It has been designed to help newbies understand exactly what is machine learning and how to get started with machine learning.
So, what are you waiting for? Enroll now into this course and we’ll see you on the other side!
Course 2: Machine Learning In The Cloud With Azure Machine Learning
In this course, we will discuss Azure Machine Learning in detail. You will learn what features it provides and how it is used. We will explore how to process some real-world datasets and find some patterns in that dataset.
- Do you know what it takes to build sophisticated machine learning models in the cloud?
- How to expose these models in the form of web services?
- Do you know how you can share your machine learning models with non-technical knowledge workers and hand them the power of data analysis?
These are some of the fundamental problems data scientists and engineers struggle with on a daily basis.
This course teaches you how to design, deploy, configure and manage your machine learning models with Azure Machine Learning. The course will start with an introduction to the Azure ML toolset and features provided by it and then dive deeper into building some machine learning models based on some real-world problems
If you are serious about building scalable, flexible and powerful machine learning models in the cloud, then this course is for you.
These data science skills are in great demand, but theres no easy way to acquire this knowledge. Rather than rely on hit and trial method, this course will provide you with all the information you need to get started with your machine learning projects.
Course 3: Mathematical Foundation For Machine Learning and AI
The integration of Artificial Intelligence is growing and multiple sectors are now looking to build technologies that include AI. With self-driving cars, smart robots, to even your coffee machines, AI has become a prominent technology that cannot be overlooked.
Writing algorithms for AI and Machine Learning is difficult and requires extensive programming and mathematical knowledge. While these algorithms have the potential to solve a number of difficult problems that are currently plaguing the world, designing these algorithms to solve these problems requires intricate mathematical skills and experience.
In this course, we have tried to help you cover exactly that. The course delves deep into the world of mathematics and algorithms to help you get started understanding these complex concepts. The course will help you learn the mathematical background you need to start working on building algorithms and networks for your next machine learning and AI projects.
The course has been designed to help breakdown these mathematical concepts and ideas by dividing the syllabus into three main sections which include:
Linear Algebra – Throughout the field of Machine Learning, linear algebra notation is used to describe the parameters and structure of different machine learning algorithms. This makes linear algebra a necessity to understand how neural networks are put together and how they are operating.
Multivariate Calculus – This is used to supplement the learning part of machine learning. It is what is used to learn from examples, update the parameters of different models and improve the performance.
Probability Theory – The theories are used to make assumptions about the underlying data when we are designing these deep learning or AI algorithms. It is important for us to understand the key probability distributions, and we will cover it in depth in this course.
What you will learn in this course:
- Scalars, Vectors, Matrices, Tensors
- Matrix Norms
- Special Matrices and Vectors
- Eigenvalues and Eigenvectors
- Differential Operators
- Convex Optimization
- Elements of Probability
- Random Variables
- Variance and Expectation
- Special Random Variables
Thats not all. The course also comes with projects and quizzes to help solidify your knowledge of the mathematical concepts. At the end of this course, you will have the mathematical foundation necessary to understand and analyze deep neural networks, the most common artificial intelligence algorithm.
So, what are you waiting for? Get your hands on one of the most comprehensive mathematical foundation course and start building your own AI and ML algorithms!
Course 4: Applied Machine Learning For Healthcare
Machine learning is changing the way how businesses and industries uses data, whether it be self driven cars, automating Chatbots or stock predictors Machine learning is everywhere. Healthcare is one of the most important industry which has embraced machine learning and it is already delivering results.
To characterize machine learning in the least complex terms, it is fundamentally the ability to equip the computers to think for themselves depending upon the situations that occurs based on the training data. Considering, machine learning and the bright future it has, we have designed this hands on course for you.
The value of machine learning in healthcare is its capacity to process huge datasets beyond the scope of human capability, and afterward change the analysis of that data into clinical insights that guide physicians in planning and providing care, ultimately leading to better outcomes, bring down expenses, and increased patient satisfaction. This is the reason we have outlined this introductory course of Applied Machine Learning in healthcare only for you.
This course covers five python programming projects, that will explore medically related data sets by solving the critical issues using state of the art machine learning techniques.
This is the five project series, lets take a look at everything you will find in this course:-
- Breast cancer detection project
- Diabetes onset prediction
- DNA classification projects
- Heart disease prediction
- Autism screening
The big part of this projects will be data pre processing. This course will cover handling the type of data i.e. typical to the health care field and also look at some cool machine learning techniques such as Neural Networks and other Supervised learning techniques such as other simple linear classifiers.
We will be then comparing the contrast with the results and see what works the best with the data that we have used. So, What are you waiting for just join us on this amazing course and start learning the cutting edge machine learning application.
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