Why is INSAID ML Intermediate course making waves!

Artificial intelligence brain robotic system vector

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 well-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.

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

Our previous article discussed the Machine Learning Foundation course. In the next term, the GCD course covers Machine Learning at an intermediate level. 

Machine Learning Intermediate

Our Machine Learning Intermediate course is designed on the lines of picking up from where we stopped at the Foundation course and building upon the content.

You remember how we introduced Supervised, Unsupervised and Reinforcement learning and their algorithms? Well, while Machine Learning Foundation deals with supervised algorithms, Machine Learning Intermediate touches base with selective algorithms part of unsupervised learning.

What is unsupervised learning?

Unsupervised learning is when a system makes decisions and solves problems based on no previous experience or learning of any sort.

If a child was given different shapes to sort through without knowing their properties and features, he might arrange them based on his understanding of differentiating features. 

This child has no previous context of shapes and a sense of recognition. He will classify probably making piles of rounder shapes together, shapes with distinct corners together and pointy shapes in a third pile. 

This is a case of unsupervised learning. Machine Learning Intermediate deals with algorithms under the unsupervised umbrella. Let’s see some of the algorithms discussed in our Machine Learning Intermediate course:

1. Principal Component Analysis

PCA is a dimensionality reduction algorithm used when a large number of redundant and duplicate features make a data set heavy and difficult to work with.

Getting rid of such duplicitous features without affecting the original data set is of utmost importance. 

PCA generates a new set of features for our data-set which are called principal components. The features are arranged in a descending order such that the feature with maximum variance to the original data is at the top.

Every other principal component is orthogonal to the previous one. It attempts to best capture the variance that is not captured by its predecessor.

2. K-nearest neighbor

Another algorithm working on unsupervised learning covered in our Machine Learning Intermediate course is KNN. KNN is a relatively assumption-free algorithm. 

K– nearest neighbor stores data-sets and finds similarities in new data-sets based on its repository. The algorithm finds and returns k number of cases from its repository that are closest to the new data-set. Here, the algorithm uses a distance function to identify closest neighbors.

3. Naive Bayes Classifier

This algorithm is based on Bayes theorem of conditional probability. It calculates the probability of an event given that another event has already occurred.

Naive Bayes makes a naive assumption that all features are independent of each other. And hence, the name! 

Assuming a given set of features in a data-set, the algorithm calculates the probability of the incoming data to belong to a particular output class.

4. K-means

K-means is a clustering algorithm. Think of it this way, as part of unsupervised learning, if you have no idea what you’re looking for in your data, the machine will start by grouping data into clusters of different kinds. Each cluster will have elements that are similar to each other.

K-means is used in clustering data points into k number of clusters based on some similarities within the clusters. 

We divide the data among these k clusters according to features. K-means is majorly used in clustering problems.

5. Support Vector Machines

Support Vector Machines divide data-sets into classes using the concept of hyper-planes. 

A hyper-plane is simply a subspace of one less dimension than an original n-dimensional space. For example, for a three-dimensional space, a hyperspace would be two dimensional.

In a data-set of two categories or features, a hyper-plane would be a line (two dimensional) and would divide the data-set into two classes.

How do you choose the right hyper-plane?

The most accurate hyper-plane is the data set on either side are at a maximum distance from the hyper-plane.  This distance is called margin and maxing it out is called maximizing the margin.

The data points closest to the margin are the most important. They are called support vectors and decide the orientation and position of the margin. Remember the higher the margin, the better is the probability of classifying new data.

6. Time Series

Time series forecasting is used when you have to interpret data that changes over time. 

Any data dependent on time such as weather conditions or traffic conditions can be explored using Time Series algorithms. 
Time series models get trained on historical data to understand what factors affect the data-set and predict future values based on their understanding.

These are the algorithms we cover in the Machine Learning Intermediate course. The term, lasting for an entire month covers unsupervised learning and related algorithms and forms a precursor to the Machine Learning Advanced course which focuses on semi-supervised learning.

Apart from the regular curriculum, engaging students in spirited competitions, motivational speeches and stimulating projects is what makes the INSAID learning experience exclusive and a stand-out.

These are some of the ways in which we ensure that the students’ drive towards the course fully blossoms:

Machine learning applications

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

Projects: Developing a 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 of 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.

Machine Learning Intermediate course has been curated by our team keeping in mind the industry landscape, the level of sophistication of the students trained on a foundation course and the desired skill set of a data scientist.

The course is delivered by INSAID’s World-class faculty that aims at maximizing the learning while minimizing one way interactions and blind reliance on theory. 

We hope you followed our term structure and understand our deliverables in each machine learning term as we discussed them. 

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

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