INSAID ML Advanced course that you absolutely cannot miss!

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In our previous articles, we discussed the break up of machine learning courses in our GCD program. At INSAID, we offer three terms in Machine Learning: Machine Learning Foundation, Machine Learning Intermediate and Machine Learning Advanced. Read on to explore our Machine Learning Advanced course.

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 Advanced 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 Intermediate course. In this term, the GCD course covers Machine Learning at an advanced level. 

Machine learning is still at a tiny fledgling in terms of the field’s evolution. While not a lot of people are aware of the best potential of the domain, the buffs have started pursuing this hot subject. The course at INSAID aims at delivering lectures on level comprehensive and engaging to novices. 

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 guide

Machine Learning Advanced

The name says it all, doesn’t it?

Machine Learning Advanced is the last rung on the ladder preceded by Machine Learning Foundation and Machine Learning Intermediate. At this stage, we expect our students to be well-versed with the vision and attitude of a data scientist and the skill set of a competent machine learning expert.

The Machine Learning Advanced course is designed being mindful of the curriculum covered before and the grasping potential of our students. While the Machine Learning Foundation focused on supervised learning and Machine Learning Intermediate focused on unsupervised learning, Machine Learning Advanced focuses on semi-supervised learning. 

Let us recap this a bit, shall we? 

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.

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.

machine learning example

The next approach that is significant to our Machine Learning Advanced course is semi-supervised learning.

Semi-supervised learning is when huge unlabeled data-sets are combined with some labeled data-sets for exploration. The machine then uses the labeled data as a reference to identify patterns and relationships in the heavy and unlabeled data set.

In our zoo analogy, now consider that the child is accompanied by his slightly elder sibling who has selective knowledge about the animal kingdom. While he does not recognize all animals found in the wild, he helps his little brother out with some a few names. 

Based on the animals identified by his older brother, the child tries to fit all the zoo animals into the categories he learned from his brother. This is a case of semi-supervised learning.

Semi-supervised learning comes into play when you don’t have enough labeled data at your disposal and you still need results for your largely unlabelled data-set. Semi-supervised learning makes the most of the situation deriving inferences with whatever limited data it has.

Let’s discuss what you stand to learn from our prized Machine Learning Advanced course!

1. Apriori algorithms

Apriori gets its name from ‘prior’ as it depends on the prior relationship of elements to determine changes.

Many complementary goods are associated together. For example, shampoo and conditioner at a supermarket would be associated together as people would buy the two as part of a package.

Apriori algorithms are used to determine such associations. These algorithms help basket complementary products and associated data elements to drive business decisions.

2. Recommender Systems

Recommender systems are used extensively by many leading companies across industries. 

Google, YouTube, Amazon, Netflix, Spotify and countless retail applications are using Recommender systems to gain and build a user base

Recommender systems keep track of different customer preferences and usage patterns. They group customers together based on their preferences and suggest similar recommendations based on each others’ liked content.

machine learning apps

3. Linear Discriminant Analysis

Let’s say you have collected data for people across 4 cities- A, B, C and D with the different features being height, weight, individual income and family income.

Some of these features can overlap. We can use LDA to get rid of the duplicity. LDA generates a set of new features which minimize overlapping and we get better separation of classes. LDA generates clearer boundaries around clusters or classes such that they are as separated as possible.  

LDA is another algorithm that targets dimensionality reduction like PCA as discussed previously in the Machine Learning Intermediate course.

4. Anomaly Detection

Anomaly detection is used colossally in many industries and is gaining rapid popularity.

Anomaly detection helps in detecting misfit data or data that does not comply with the entire data-set. Credit card fraud and spam emails are just some applications of this approach.

Different algorithms covered here are density-based, cluster-based and support vector-based anomaly detection techniques.

5. Ensemble Learning

Ensemble learning combines a number of different models to generate a model that carries the cohesive efficiency and productivity of all models combined. 

Ensemble learning is used extensively across a variety of areas like malware detection, face recognition, fraud detection and a range of computer and cybersecurity areas.

6. Stacking

Stacking is a technique to assemble models together as part of ensemble learning. Stacking combines various models such that the resultant model rates higher the base models increasing the overall efficiency while disregarding the ones that reduce it. 

Stacking is most helpful when the underlying models are as different from each other as can be.

7. Optimization

Optimization, simply put, is a technique to optimize inputs to generate desired or best possible outputs. 

This can be achieved by a number of different ways of altering the inputs. Maximizing or minimizing outputs is a common application of optimization problems. 

Determining the size and design of airplane wings to minimize weight, maximize the load-bearing capacity of a bridge and picking most suited stocks to maximize returns on investment are some ways optimization helps us the most.

8. Neural Network

Neural networks used in machine learning are called Artificial Neural Networks. Neural networks are a set of algorithms, structured like the neuron network in the brain, which are responsible to identify patterns. 

Neural networks learn from data rather than explicit instructions from human programmers. It is this feature that makes them autonomous and robust when it comes to decision making and problem-solving.

Structure of an artificial neural network

These are the different modules covered in our Machine Learning Advanced course. The course is aimed at making its students world-class Machine Learning experts and it delivers on this objective quite spectacularly with the help of India’s top Faculty and an extremely helpful style of learning.

To help cement whatever our students have learned, we at INSAID, offer them a relentless series of quizzes, projects, case studies and pre and post reads.

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

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

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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