Machine Learning: Your Complete Guide
Today’s Artificial Intelligence has surpassed the blockchain and quantum computing hype. This is due to the huge amount of computing resources available to the common man. Developers can take advantage of this in creating machine learning models and re-train the existing ones for better and more efficient performance and results.
What is machine learning?
Machine learning is a field under Artificial Intelligence that is dedicated to the algorithms capable of learning from data. It can be applied in many fields like business analytics, health informatics, financial forecasting, and self-driving cars.
In 2022, the demand for machine learning courses grew widely. According to a report published by the World Economic Forum, in the future, machine learning is expected to be one of the world’s most in-demand skills.
Machine learning has a large width and needs skills across several domains. The skills that are required for a machine learning program are –
- Probability Theories
- Optimization techniques
Machine Learning – Implementation
For the development of machine learning applications, you have to decide on the platform, the IDE, and the language for development. There are many choices available and most of these will meet your requirements as all of them provide an implementation of AI algorithms.
If you are developing an algorithm on your own, you need to keep the following things in your mind –
- Language – This is about you being efficient in a language that is supported by machine learning development.
- The IDE – This depends on your familiarity with the existing IDEs and your comfort with them.
- Development platform – There are some platforms available for development and deployment and most of these are free to use. In some cases, you may need to pay some license fee beyond using it for a certain time.
Some languages that you can choose from are –
IDEs that support machine learning development are –
- R Studio
- iPython/Jupyter Notebook
- Google –Colab
Platforms that can be used for machine learning applications are –
- Microsoft Azure
- Google Cloud
There are many other options for platforms, IDEs, and languages apart from the ones in the list.
There are many courses through which you can gain knowledge relating to machine learning.
- Machine learning (Stanford University)
This should be the first pick for anyone interested in machine learning. This is a seminal machine learning course available on Coursera taught by Andrew. It is one of those courses that boosted the popularization of massive open online courses (MOOCS).
The course starts with laying down the mathematical foundations of machine learning. A review of linear algebra and univariate linear regression is taught before moving to multivariate and logistic regression.
The course jumps from topic to topic every week and covers a wide variety of techniques and tools relating to machine learning. These topics include deep learning, support vector machines, principal component analysis, etc.
The course also touches on practical aspects like designing and leveraging large-scale machine learning projects.
By the end of the course, the learner has a broad understanding of machine learning.
One thing to note in this course is that it uses Octave rather than Python as its language.
This is an 11-week course and each week about 6 hours of study has to be done. Concepts are made clear through a mix of video lectures and readings. Every week one auto-graded quiz has to be solved.
- Machine Learning Foundation: A Case Study Approach
This is the second-best course after the above-mentioned course. Many courses approach the subject from an abstract perspective and spend a lot of time laying down mathematical foundations and relegating the tangible aspect of the subject to examples and exercises. But this course is the opposite of that.
As the name suggests, the course approaches the subject through case studies with a well-defined context and objective. This does not mean that the course overlooks theoretical knowledge, it is just that it approaches the subject more pragmatically.
This course starts by explaining what machine learning is, its applications, and making a case study.
The course also covers Python fundamentals as well as the rudiments of tools like Jupyter Notebooks.
The course covers different case studies, with each one illustrating a particular facet of machine learning. In these case studies, you use regression, classification to evaluate sentiments in user reviews, clustering for grouping of related articles, deep learning for identification of objects in images, and so on.
This course is best for you if you are someone who finds it easier to learn through examples.
The course is 6 weeks long and each week includes 3 hours of work. Concepts are taught through a mix of short videos and readings. Two exercises have to be done every week that take about an hour to be completed.
- Machine Learning For All
A course offered by the University of London on Coursera, this course aims to make machine learning available and accessible to a wider audience. It does not require any advanced mathematical knowledge or the use of programming languages or machine learning libraries like Python and TensorFlow.
The course starts with an explanation of artificial intelligence and machine learning and how both of them are connected. The course discusses real-world applications and explains data representation, how to set up the project, and the opportunities and ethical considerations of machine learning.
This course is broken down into 4 weeks and each week, approximately 6 hours of study have to be done. The course is a mix of video lectures and readings. Usually, an hour-long auto-graded quiz has to be taken as an assignment, and sometimes, additional practices also have to be done.
To sum up, machine learning is a technique used to train machines to perform activities similar to a human brain, just faster and better than an average human being. Machine learning is going to be the most-demanded course and its popularity in the future is going to go up as artificial intelligence is making continuous growth.