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- Courses designed for Students and Professionals
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Designed for Students and Professionals

Live interactive classes by Industry Experts

On-the-spot Doubt Resolution

In-depth Hand-on Practicals

One-to-One Mentorship

Mock Interview Preparation
Guided by experts, Adapted for you
We call this Assisted Learning.

One-to-One Mentorship

Authentic Learning Experience

Course Completion Certificate
COMPREHENSIVE COURSES IN
INDUSTRY-READY MODULES
Python, Data Analysis and Machine Learning Courses
This course will teach Python 3 required for Machine Learning. This course is for people who wants to step-wise start their journey of Machine learning. Later they can learn Machine Learning.
Duration: 40 Hours / 8 Weeks
Key Concepts and Technology: Python-3 core, Data structures, Classes and function, Misc. modules e.g. http/file, Pandas and Numpy – Basic to advance, Google Colab.
Syllabus:
- Introduction and syntax
- Conditional flow and loops
- Functions(Simple), Lambda and higher order function
- Python core Data structures
- Classes and Misc. modules
- Introduction to Pandas, Numpy
- Advance operations with Numpy and Pandas
- 200+ practice exercises on learnt concepts
Course Pre-requisite: None
This course will teach algorithm-based machine learning in detail with hands-on projects. This course is for people with understanding of Python and wants to pursue career in Machine learning.
Duration: 60 Hours / 12 Weeks
Key Concepts and Technology: Supervised and unsupervised Machine learning, Python-3, SciKit Learn, Pandas/Numpy, MatplotLib, Seaborn , Google Colab.
Syllabus:
- Exploratory data analysis with Pandas, Matplotlib and Seaborn
- Learning Data preprocessing and Feature Engineering
- Machine learning internals and vocabulary
- Regression modelling
- Classification modelling
- Learning dimensions reduction techniques
- Unsupervised modelling – Clustering
- Ensemble modelling – Bagging, Boosting,
- Misc. – Class imbalance, Metrics/Evaluation, Regularization, Model comparison, Improvement strategies
- Basic of ML Model deployment with Flask API
- 15+ Hands-on projects and 50+ exercises on learnt concepts
Course Pre-requisite: Python
This course is intended to teach Python before Machine learning. Suitable for people who are not aware of Python
Duration: 100 Hours / 20 Weeks
Key Concepts and Technology: Supervised learning, unsupervised learning, Python-3, SciKit Learn, Pandas, MatplotLib.
Syllabus:
- Python for ML – Full course
- Machine Learning – Full course
Course Pre-requisite: None
This is a full fledged course for a data scientist. This course will cover Python, Statistical modelling/Test in detail, Data visualization and then modelling using Machine Learning.
Duration: 120 Hours / 22 Weeks
Key Concepts and Technology: Statistical modelling/Testing, Supervised learning, unsupervised learning, Time-Series data, Python, SciKit Learn, Pandas, MatplotLib, StatsModel, SciPy, Seaborn.
Syllabus:
- Python for ML – Full course
- Machine Learning – Full course
- Statistical Techniques
- Handling Time – Series data
Course Pre-requisite: None
Artificial Intelligence Courses
This course all about the Deep learning concepts in general. After this course you will be able to model and optimize Neural Network. Work on image and text data learning.
After this you may choose a path towards Computer vision Or Natural Languages processing as specialization.
Duration: 60 hours / 12 Weeks
Key Concepts and Technology: Neural network, CNN, RNN, Tensorflow with Keras, Hyper-parameter tuning, Google Colab.
Syllabus:
- Introduction to Deep Learning
- Introduction to Vectors, Tensors and Maths for DL
- Introduction to Neural Network
- Back-propagation and Gradient Descent
- Artificial Neural Network with Keras and Tensorflow, Tensorboard
- Challenges of a NN and Optimisation techniques
- Understanding images with Convolution Neural Networks
- Learning sequential data with Recurrent Neural Networks and LSTM
- Introduction to Generative Adversarial Networks
- Handling Time-series data with Deep Learning
- Hands-on projects and practice exercises
Course Pre-requisite: Basics of Machine Learning, Python
Courses that are coming soon
- Deployment and Maintenance in Machine Learning
- This course is intended to teach post modelling activities in Machine Learning Integration to Web and Mobile, Containerisation, Model Deployment, Feedback Loop, ML on GCP, ML on AWS and Best practices.
- Natural Language Processing (NLP)
This is a specialisation course in Natural Language Processing Problems. After finishing this course you will be able to solve every type of text data problems.
- Computer Vision
- This is a specialisation course in Computer vision. This course will enable you to apply all the latest work in CV area. This will directly place you to handle industry use cases.
be part of the industry, even before entering the industry
machine learning projects
Learn to build models using plane Python before using Library.
This will enable you with the knitty-gritty of the model
Learnt to Model Linear Regression, Polynomial Regression and L1/L2 Regularization
Learnt to Model Classification models e.g. Logistics Regression, DecisionTree, Support Vector Machine
Learn to handle high Dimension data with PCA on MNIST Digit dataset
Build a simple Recommendation system for user
Handle Imbalanced data with a Classifier predicting Cancer
Learn to catch Anomaly using Clustering techniques
Learn Statistical analysis (Hypothesis testing) on real-data (Data Science course only)
Learn to handle Time-series data on Sales dataset (Data Science course only)
Practice modeling on additional 15+ practical datasets to shapen ML skill
Code the learnt concept with Python with 50+ practice programming assignments
Build and Deploy an end to End project using Flask API and learnt the challenges of a Live Model
artificial intelligence projects
Learn to build models using plane Python before using Library.
This will enable you with the knitty-gritty of the model
Learn multi-class classification with a Neural network on handwritten digits(MNIST digits dataset)
Perform Regression analysis with Housing data using a Neural network
Learn Binary classification using Convolutional Neural network on Cats and Dog color images
Learn multi-class classification with Convolutional Neural Network on MNIST Fashion dataset
Identify a Movie Genre based on poster images to learn Multi-label CNN
Learn to Model Regression with CNN on Hotel images and learn to predict Bounding Boxes
Learn to use the Encoder-decoder concept of Deep learning
Learn the building blocks of Natural Language Processing
Learn to handle Time-series data with Deep learning on Hourly Energy Consumption dataset
Practice modeling on additional 10+ practical datasets to shapen ML skill
Code the learnt concept with Python with 50+ practice programming assignments
Build and Deploy an end to End project and learn the challenges of a Live Model
Become an expert, ON your own time
AI may be complicated, learning is not. Our ML/AI courses offer Flexible Timing and Flexible Payment Options so that you can shape your learning at your convenience.
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9 AM – 8 PM
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MON – FRI
9 AM – 8 PM
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- Lifetime access to Course Material
- Course Completion Certificate
- Flexible Payment Options
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Register for a free demo class. Leave us with your details and we will contact you on your preferred date for a demo.
Frequently Asked Questions
I don’t know Python. Will I able to learn smoothly?
First thing first – without Python, you can’t scale into Machine Learning space.
On Learning, our trainers do take care of the current programming familiarity of the Student. So you will get a very smooth experience.
At the same time, you will need to devote sometime practice coding (~7-10 Hrs/Week) other than the Class. You will get 15×7 support on Discord for any issues/queries/doubts.
If I don’t learn any programming language. Will this affect my AI learning?
As we mentioned in our previous answer, to leverage the potential of AI/ML algorithms, learning a programming language will add a lot of options to your carer path.
There exist some GUI(Graphical User Interface) based tools that can help you to send instructions. But these tools have limited capabilities.
If you are ready to put the effort mentioned in the previous question, we will assure we learn Python easily and smoothly.
What is an optimistic time frame?
- 1-2 Month – python.
- 2-3 Months – Machine/Learning and Deep learning each.
- 1-2 Months Refresher and consolidation.
I am confused with these terminologies - AI Vs ML Vs Deep Learning.
AI is the umbrella under which ML, DS, and DL are placed, having a separate field of study. The definition of AI changes with time and advancement of research i.e. AI of 1990 is not the AI of 2020.
Al – It is the domain which attempts to achieve human-level intelligence by a machine.
ML – It is a subset technology of AI that enables machines to learn and adapt new data automatically from experiences without being previously programmed. This algorithm allows the machine to identify the data and build predictions around it like how humans do. Classical Statistics based algorithms are the core pillars of ML. Unfortunately, these algorithms didn’t scale well with Image data and very large text corpus.
DL – It is the latest evolution of ML, that uses neural networks to analyze information just like the similar structure found in the human brain. ML didn’t scale well with Image data and very large text corpus. With Deep Learning, we are able to analyze Image/text data too. This opens the path to analyze Image/Large text data on a large scale.
How important is Statistics, Mathematics for AI/Ml learning?
Mathematics – Linear Algebra and Differential Calculus is needed but in chunks. Our course will make sure you learn the needed concepts with the appropriate ML/DL topics without wasting too much time in the whole Calculus/Algebra domain.
Statistics – We need more of Statistics as compared to Mathematics. But it is fairly easy and needless to say, we will make sure you learn it with the ML and Data-Science course.
What are the must-have skills needed to learn AI/ML?
- You need to be spirited and perseverant.
- Must manage 7-10 Hours per week for brushing up.
- Maintain consistency in projects and assignments. Rest our courses will pave the way for building expertise in AI/ML.
Though these points sound easy, it demands a certain level of determination.
What is the course Fee structure?
Our Brochure covers all the details of the fee structure, please download the brochure.
When should I start Learning AI/ML?
The blunder you can make is to start in the final year when you don’t make it to the placements and then easily fall into the trap of 1-2 months of high promising alluring job oriented capsule courses.
As answered in some previous questions, you need a time-frame of 6-7 months for a decent level. Definitely, if you practice beyond that, it will add to the skill.
So, we recommend starting in the early years i.e. 1st/2nd and worse in the 3rd to give yourself an ample amount of time.
There are ample amount of free books/Courses available, why would I need a course?
Yes, it is true that ML/DL has a lot of freely available good courses/books.
But to it’s like scanning a lot of files to get the needed few files. Moreover, it is very difficult to identify the starting point e.g. with or without Python. Most of the good books/tutorials demand quality knowledge in Python. Deep Learning itself is quite difficult to comprehend. However, you may start doing shallow coding (by copying from blogs) without understanding the concepts.
So, our courses will save your time from searching all of the internets with our well-curated and optimized courses. If you want to dive deep, you will have all the perquisites of up to scratch books/Blogs to read attached to each Unit.
Plus, a 1-2-1 Live Instructor based learning provides you immersive learning experience with a well-defined set of Assignments, Practice datasets, and End projects to kick-start your Python learning.
Clearly understanding the tricky “WHYs” behind every concept which actually can take 100s of blogs if you try yourself.
Is this a video-based or Live Trainer based?
Our courses are 1-on-1 Live-with-trainer based classes that are conducted on google meet and with a digital Board( i.e. Microsoft Whiteboard) to give you a Classroom like an experience. You can check that with a Free Demo class anytime
Can we check some course/class sample?
Absolutely! Here are the samples from classes available in the following folder.
So, go ahead and check it out.