What is INSAID ML foundation course that you have been missing on?

In a world where Data Science education is spreading like wildfire, there is a lot of confusion and chaos among working professionals on how to structure their learning experience.

At INSAID, we offer our students a comprehensive framework to align their needs and objectives from learning data science with the course curriculum as we structure it.  

A screaming majority of INSAID students are working professionals and as such do not have time to devote to a full time, rigorous course. We, at INSAID, realize the importance of a packed schedule and a tight work week and therefore our focus is to deliver world-class education to our students in a given time frame.

One such beautifully structured course is our Global Certificate in Data Science course. It is a 6 month comprehensive program crafted for working professionals to transform them into industry leaders. The course is spread over 6 terms with every term lasting for a month apace.  

The GCD program has three terms of machine learning- Machine Learning Foundation, Machine Learning Intermediate and Machine Learning Advanced.

Machine learning course

In this feature, we will discuss about our Machine Learning Foundation course, how it will help you, what we offer and how is this course the standout program in the country.

Machine Learning Foundation

Machine Learning Foundation starts in our third term of the GCD program. The term is spread through a month and introduces students to the basics of machine learning. Let’s see what this course entails:

Introduction

Everyone is relatively new to machine learning. The course at INSAID aims at delivering lectures at a similar level. 

As we have discussed, machine learning is the ability of a machine to perform tasks, learn from experience, learn from data and improve its performance over time. 

This is accomplished through innumerable statistical models and algorithms. It is imperative to understand which algorithm befits your problem at hand. 

machine learning course

You can compare the process to a doctor’s appointment. If a doctor was to receive a patient complaining of repeated headaches, he would not immediately arrange for a range of full body tests without a vision or plan in mind. The doctor would infer some information from the patient’s current condition and symptoms which would form part of his prognosis.

After initial prognosis, he would order a few tests and inspections to detail on a complete diagnosis. This is a structured approach taken by best utilizing all the information available at every step of the way.

This is similar to selecting the right algorithm. You need to understand your initial data as available in the rawest nature, lay down objectives for your problems and conclude on the nature of results you expect.

The algorithms, in turn, are covered under the umbrellas of supervised, unsupervised and reinforcement learning.

Consider taking a walk in a zoo. All animals that you come across are classified in your brain differently based on previous knowledge and awareness of the animal characteristics, features and behaviors.

Learning AI

This is supervised learning. In this case, your brain acts as a supervisor, relating everything you’ve seen to previously acquired knowledge and finally assigning an animal to a certain class. Simply put, supervised learning needs a supervisor.

Now imagine a child is walking in that zoo with you. This is the child’s first visit to a zoo and he has no previous knowledge of the animal kingdom. Now if you were to not help the child, the child would be totally unsupervised when learning about these animals.

If this child was to make sense of what he saw at the zoo that day, he’ll probably describe the animals as being big or small, having wings or not, having stripes or a plain coat, having a long neck or a long trunk and the possibilities go on.

The child would consider classifying animals with most commonality and assign them to different groups. In this case, the child is completely unsupervised. This is a case of unsupervised learning. 

You can read more about supervised and supervised machine learning algorithms here.

The third way is reinforcement learning. Learning is reinforced through a sequence. The input data goes through a series of commands and at every step gains some reward or incurs a fine based on the result of its predecessor. The aim is for the algorithm to learn through experience and improve on itself loosely based on a carrot or stick approach.

In the same example, now consider the child and you are working as a team. For every animal that the child recognizes correctly, you give him a bar of chocolate and for every animal that he incorrectly identifies, you correct him while taking away half the bar from him.

Let us see some of the algorithms that are covered in our Machine learning foundation course and what they actually are:

1. Linear Regression

Linear regression algorithm is widely used for predicting values for data-sets assumed to have a linear relationship. 

The algorithm works with an independent variable (X), a dependent variable (Y) and testing the values of the dependent variable for different input values of the independent variable.

A simple linear regression equation looks like this:

Y = ????0 + ????1(X)

where Y = dependent variable; X = independent variable; ????0 = intercept; ????1 = coefficient of X

2. Logistic Regression

Logistic regression is much similar to linear regression such that it explains the relationship between dependent and independent variables. This is done by estimating probabilities using a logistic or sigmoid function.

Logistic regression is extensively used in classification problems. It works best to classify outputs into two categories- male/female, yes/no and such.

3. Decision Trees

Decision trees are used extensively for classification. 

A decision tree is exactly how it sounds like. The tree has nodes and branches which represent every stage at which a decision is taken. 

Decision trees use a combination of regression and classification models. These models, at every node, render an output that forms the basis for subsequent questions. The answers at each stage lead to further questions in the series. 

Such decisions and questions continue until we reach a terminal node after which any subsequent questions are impossible.

4. Random Forest

We discussed how one decision tree works. Now we’ll talk about how we can use multiple decision trees to create a forest, and in turn, a more accurate output.

What would you do if you had to buy a new phone?  You’ll probably ask your friends for advice. Friend A might ask you what kind of phone camera you’re looking for, how much you post on social media and suggest you a model. Friend B might ask you how many apps you’d want, your storage needs and suggest you another option. Every friend would suggest options with some personal bias.

You can make a decision tree for each friend’s suggestion, and put together a forest of decision trees. Similarly, as a machine learning algorithm, random forests use conditions and rules to predict outcomes. It then calculates the votes for every predicted target and generates the highest voted as the final prediction.

These are some of the algorithms explained in the Machine Learning Foundation course. 

machine learning algorithms

We have shared an overview of these algorithms with you. Our course at INSAID offers a series of projects and case studies to help you imbibe the workings of these algorithms so that you can not only use them optimally in the future but can also determine which algorithm works best under which settings.

The Machine Learning Foundation course is structured to enhance our students’ productivity through an engaging series of lessons and other value-adds including:

Quiz: We believe a healthy spirit of learning is best encouraged through informative quizzes. 

Projects: Developing hands-on knowledge about machine learning basics is possible through constant application. GCD projects, especially those aimed towards machine learning, require students to be involved in real-world problems and simulating actual instances help our students fit theory into an application-driven world like a jigsaw puzzle.

Case- Studies: A closer view of the real-time problems and solutions help students broaden their horizons. Multiple use cases and case studies help students build a perspective on how what they are learning during classes is being used by people beyond the expanses of a classroom.

We believe in empowering students to perform well in the industry and ensure their success by enabling them to be critical about their work, understanding different approaches when solving a problem and evaluating applied models, algorithms and techniques to furnish the best possible solutions.

This ends our feature on Machine Learning Foundation. Do return to this space to discover more about our Machine Learning Intermediate and Machine Learning Advanced courses. 

With the help of world-class faculty, state-of-the-art resources, real-world case studies and an endless drive to deliver the best to our students, INSAID has drafted a niche curriculum for its students. 

You might want to check out about our courses and webinars on our main website page. Do visit our INSAID Learning Center page and write to our Admissions team if you need more assistance!

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  1. Hello Malvika,
    Amazing article. Your blog helped me to improve myself in many ways thanks for sharing this kind of wonderful informative blogs in live. I have bookmarked more article from this website. Such a nice blog you are providing !

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