In this article, you will learn the basics of the Applied Machine Learning Online Course and what brings it close to the economic needs of the modern world.
We have been a witness to rampant innovations and development in the field of Applied AI. But, it’s time to go deeper into the subsets of AI applications. Starting with machine level intelligence and applying their test and train modules to our daily tasks can save a lot of time, effort, and monies. The subspecialty within the AI realm that deals with the study and development of Machine Learning software for specific activities is called Applied Machine Learning.
Popular Applied ML to begin with
There are many ML concepts that you might learn in your total duration of Applied AI and ML course, but these popular algorithms are best, to begin with:
1 – Naïve Bayes
2 – Support Vector Machine / SVM Classifier
3 – KNN Algorithms / k-Nearest Neighbour (in Python and R)
4 – Decision Trees
5 – Logistic / Linear Regression
There’s No ‘Perfect’ in Applied Machine Learning
AI ML engineers and business intelligence analysts constantly tag each other out to build and deploy the most perfect AI ML suite that can solve all their problems in one go. But, unfortunately, this hypothetical ML platform is yet to arrive. The very context of developing and deploying the most perfect Applied Machine Learning is what keeps the community of AI engineers and data science analysts together.
The sooner you learn and understand that there is no best machine learning training or data science platform, the better your chances of working with these tools and applications in the long run. Eventually, your project might take you close to the ‘hypothetically perfect’ machine learning test and build a tool kit.
Applied ML is based on powerful attributes
One of the most engrossing applications of Applied ML is the way it is transforming the entire cycle of cancer tissue detection and prevention. Thousands of professionals certified from the best Applied Machine Learning Online course work on breast cancer research programs in the US and UK, diagnosing billions of data points evaluating the attributes that have caused the disease, and the other attributes that lead to recurrence or relapse of the cancer formation within 5 years.
Data science experts work on breast cancer data sets based on the commonly known application in Machine Learning, called the “Binary Classification problem.” It’s a simple “yes or No / On or Off, or simply “Choice versus Fact” situation that helps ML engineers to design and develop Applied ML algorithms based on two class labeling system.
Applied ML courses train professionals in getting their projects approved as a leading benchmark in ‘Classification Accuracy”, meaning 100% correct accuracy in predictions made by the ML tool.
The combination of Binary Classification with other ML concepts can further refine your overall efficiency, including working with multi-label, multi-class and classless objects in the data pool.