Prof. Seyedali Mirjalili
Machine learning is an extremely hot area in Artificial Intelligence and Data Science. There is no doubt that Neural Networks are the most well–regarded and widely used machine learning techniques.
A lot of Data Scientists use Neural Networks without understanding their internal structure. However, understanding the internal structure and mechanism of such machine learning techniques will allow them to solve problems more efficiently. This also allows them to tune, tweak, and even design new Neural Networks for different projects.
This course is the easiest way to understand how Neural Networks work in detail. It also puts you ahead of a lot of data scientists. You will potentially have a higher chance of joining a small pool of well–paid data scientists.
Why learn Neural Networks as a Data Scientist?
Machine learning is getting popular in all industries every single month with the main purpose of improving revenue and decreasing costs. Neural Networks are extremely practical machine learning techniques in different projects. You can use them to automate and optimize the process of solving challenging tasks.
What does a data scientist need to learn about Neural Networks?
The first thing you need to learn is the mathematical models behind them. You cannot believe how easy and intuitive the mathematical models and equations are. This course starts with intuitive examples to take you through the most fundamental mathematical models of all Neural Networks. There is no equation in this course without an in–depth explanation and visual examples. If you hate math, then sit back, relax, and enjoy the videos to learn the math behind Neural Networks with minimum efforts.
Instructor Details
Courses : 2
Specification: Introduction to Artificial Neural Network and Deep Learning
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16 reviews for Introduction to Artificial Neural Network and Deep Learning
Add a review
| Price | $17.99 |
|---|---|
| Provider | |
| Duration | 7 hours |
| Year | 2020 |
| Level | All |
| Language | English |
| Certificate | Yes |
| Quizzes | No |
$17.99
Christian Malakani –
great course
Murat Ulu an –
I am happy with the course up to now.
Gajendra Gulgulia –
this course is exactly what it says in the title , i.e., an introductory course to NN and DL. However that being said, the course builds the idea with visual and intuitive feel of what a NN is and how it works rather than diving directly into the math and showing examples using ready made available library where everything is abstracted away. To learn more and gain more expertise in DL, it is necessary to take advanced courses with more math but this particular course builds a solid foundation (at least in my opinion) for more advanced course in DL PS : I tried other DL courses but this one is better for any one having no idea of what DL or NN is and wants to get a better insight before doing any advanced course.
Caupolican –
Great explanations
Farid Boussaha –
Good course. Clear speaker. Manages to pass his enthusiasm through each video. Notes: 2.8 coding a simple perception in java > I’d say misplaced video as some concepts were only explained later. In the course content, typos: 4.17 typo netwrok and section 6 typo netoworks
Ankit khare –
As always.. Excellent 🙂
Ali Safaa Sadiq –
Amazing Section Ever!
Marta Dylewska –
yes, it looks like it’s a good match
Renu Gupta –
Like the detailed view of each concept. Explained well
Anil Ghorpade –
Truly engaging materials that made concepts clear on AI. Thank you.
Steven Milhiser –
It would help if the instructor spoke clearer English.
Luisa Maria Ramos Sobrino –
all good
Ryan Savino –
Theory concepts were good but the mathematics were a little too in depth for me as a beginner.
Lalit Balfad –
NICE
Joao Madeira –
More focus on the multli layer network with different examples, use other functions, and direct java implementation.
Debraj Ghosh –
Volume is very low for all videos. I like the way of explaining. I am new to MLT.