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Designed for Students and Professionals

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Live interactive classes by Industry Experts

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On-the-spot Doubt Resolution

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In-depth Hand-on Practicals

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One-to-One Mentorship

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Mock Interview Preparation

Guided by experts, Adapted for you

We call this Assisted Learning.

At 10xAI, we aren’t just teaching AI, we are creating experts. Which is why, we offer live interactive classes by our industry experts for an authentic learning experience.
 
In addition, you get life-time access to our online study library, interview guidance, 24-hour query resolution and much more.
 
 
With personalised batches, tier-based training content and one-to-one counselling, we ensure you are assisted at every step of the way.

One-to-One Mentorship

Assessment and Counselling. Guided course and batch selection.

Authentic Learning Experience

Interactive Classes with recordings to refer later.

Course Completion Certificate

Get a verified certificate post successful completion of courses.

COMPREHENSIVE COURSES IN
INDUSTRY-READY MODULES

The AI industry is ever-evolving. Our Course modules have been specially curated by industry-experts to equip you with the latest skills and expertise to join the fastest growing industry. Take a look at our ML/AI Courses –

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.
Download our brochure to view more details about the courses, fee structure and more.

be part of the industry, even before entering the industry

At 10xAI, we believe in application-based learning. Get access to 25+ AI & ML practical projects for a hands-on learning experience. With our experts assisting you at every step of the way, you’ll be able to build an impressive project portfolio that will showcase your expertise to prospective employers.

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.

Weekends

SAT – SUN

9 AM – 8 PM

WEEKDAYS

MON – FRI

9 AM – 8 PM

HYBRID

Online Theory +

Hands-on Classes

REGISTER AND GET 15% off on all COURSES

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

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.

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.

Machine Learning is a Subject full of Abstraction and very fast-moving research space. Our courses cover all the needed concepts, Frameworks, Data variety, Programming assignments. The best possible timelines will be –
  • 1-2 Month – python.
  • 2-3 Months – Machine/Learning and Deep learning each.
  • 1-2 Months Refresher and consolidation.
So, if you are starting from ground-0 and want to truly learn the subject, then have a mindset of 6-7 months of 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.

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.

  • 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.

Our Brochure covers all the details of the fee structure, please download the brochure.

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.

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.

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

Absolutely! Here are the samples from classes available in the following folder.

⇒    Course preview

So, go ahead and check it out.