But before we start, it should be clarified that AI is a very broad field of activity, consisting of many parts, such as machine learning, deep learning, natural language processing, etc. In general, AI resembles the ocean, and its components are seas, so it is simply unrealistic to study AI completely.
But enough of the chatter: let’s get down to business.
1. This is CS50 (Harvard University)
If you are not a programmer and programming is a completely unfamiliar field of knowledge for you (perhaps you even think that it is not for you at all), then this course is a great start.
The lectures of the course reveal the basics and the very essence of programming and computer science. Lectures are given by Professor David Malan, distinguished by dynamism and special energy. If you don’t have time to watch the whole course, watch at least the first lecture.
All of this was intended for perfect beginners. If you are familiar with programming in principle, you can skip this course. But it will be good enough for those who prepare for University. Dive into this course and leave all term paper tasks for https://eduboard.com/service/term-papers-writing-services.
2. Python Programming Course (FreeCodeCamp)
When it comes to programming in the field of AI and machine learning, most often you will hear about Python. This is one of the languages whose popularity is growing the fastest. In its ecosystem, you will find thousands of libraries tailored to work with AI, and this greatly facilitates the life of a developer in the long run. And the best thing about Python is that it’s a simple language.
The freeCodeCamp course is a 4.5-hour video that covers all the most necessary topics: Python installation, variables, strings, lists, tuples, functions, object-oriented programming concepts, and much more. By the way, this course is essentially a practical guide, i.e., you will not just watch lectures, but also write code yourself.
If you can program in Python, you can skip this course too.
3. AI For Everyone (Coursera)
Finally, we got to the AI itself. This course is purely theoretical. It is read by the most famous expert in the field of machine learning and AI — Professor Andrew Eun. By “the most famous” we mean that literally everyone who is interested in artificial intelligence has heard of Andrew Un.
This course is favorably distinguished by its brevity, conciseness, and interesting presentation, understandable for everyone — even those who know nothing about AI, and programming in general.
After completing this course, you will get excellent answers to some of the most common, but at the same time interesting questions:
- What is artificial intelligence and machine learning?
- What do AI companies do?
- What is machine learning capable of?
- How to choose a project in the field of machine learning and artificial intelligence?
- How is the work on such a project being built?
- Biases against AI.
- Work in the field of AI.
One of the most important questions that certainly comes to mind when studying machine learning and AI sounds like this:
“Should we know the inner workings of algorithms well, or can we study them superficially?”
For example: should I implement a neural network (one of the popular deep learning algorithms) from scratch or can I use one of the available platforms, say Tensorflow or Pytorch?
We advise you to study their inner workings before implementing algorithms using any external libraries, but the final decision, of course, is yours.
This course will be helpful for those who need dissertation help.
4. Machine Learning from Stanford University (Coursera)
This is one of the most popular machine learning courses, taught by Professor Andrew Eun. At the time of writing, the number of people enrolled in this course exceeded 3 million.
It is also definitely one of the most in-depth ML courses, it outlines the inner workings of algorithms and the mathematical calculations behind it all.
Octave/Matlab are used in writing algorithms in this course, but we advise you to write algorithms in Python since this is an industry standard.
5. Machine Learning Tutorial (code basics)
So, after you have tried to implement algorithms from scratch, you can proceed to the next course. It will also be very useful to you if you feel uncomfortable with close communication with mathematics in relation to machine learning.
This course is one of the best yet underrated courses on YouTube. It is distinguished by the simplicity of explanations. During the course, libraries such as numpy, pandas, matplotlib, and sklearn are used to implement and visualize various machine-learning algorithms. Thanks to these external libraries, you can easily implement all the necessary algorithms by writing just a few lines of code.
In addition to explaining standard machine learning algorithms such as linear regression, logistic regression, decision trees, random forest, and support vector machine, this course also covers other topics such as gradient descent, dummy variable, and unitary coding.
After you get used to the topic of machine learning, you can take on its subspecies — deep learning.
Deep learning algorithms underlie the recommendation and personalization systems of Netflix, Amazon, YouTube, and many other large corporations and startups.
6. Deep Learning Specialization (Coursera)
This is a comprehensive course on a separate specialization, namely, deep learning. It is read by the same Andrew Eun. The specialization consists of five separate courses:
- Neural Network and Deep Learning (Neural network and deep learning).
- Improving Deep Neural Networks: Hyper-parameter tuning, Regulation, and Optimization (Improvement of deep neural networks).
- Structuring Machine Learning Projects (Structuring machine learning projects).
- Convolutional Neural Network (Convolutional neural network).
- Sequence Models.
The material in these courses is thoroughly understood, and attention is paid to both the technical and mathematical sides of algorithms and approaches. Python is used for coding here.
Tensorflow and Pytorch are open-source deep-learning frameworks. They dominate the field of AI. Tensorflow is backed by Google, and Pytorch is backed by Facebook.
Tensorflow is the leader in terms of popularity and the number of downloads, but the AI research community is firmly holding on to Pytorch. In general, the choice of framework depends on you and on the platform you use. We advise you to try both and then decide which is best for you.
The bottom line
To be honest, it will take quite a lot of time to complete all the courses from our list, so everyone should independently analyze their needs and prioritize them accordingly.
We would like to end this article on a high note. Anyone can study AI. But besides, everyone should do it, because AI is the next industrial revolution. At the same time, it’s important to remember that not everyone can write a college paper on AI themes. Writing such an essay is highly complicated and requires in-depth knowledge of this field. Fortunately, you don’t have to struggle with your AI essay anymore – there are plenty of reliable college paper writing services such as collegepaper.net that are ready to take this burden off your shoulders.
