Perceptron Live is a first of its kind, COMPLETELY LIVE course designed to provide you the perfect platform to start your journey in the most important technology of the future, Machine Learning. Machine learning is that field of study that helps machines to learn without being explicitly programmed. Giants such as Google, Microsoft, Apple, Amazon, Uber and even NASA, are heavily investing man and money on Machine Learning based research to make better products and services. From multipurpose cross-platform apps to high end software, from social media to stock trading, Machine Learning is very rapidly becoming the most important aspect of product development. We take pride in the fact that this will be the COUNTRY'S FIRST completely live course on Machine Learning, with classes and doubt sessions all being live. You will also be able to schedule live doubt classes as per your convenience.
Students willing to enrol are required to have a clear understanding of programing fundamentals in any one language, preferably Python, but not necessarily. The students will follow industry-standard programming practices to build intelligent systems, working on AI algorithms and data crunching

Implement Algorithms

Perceptron is an hands-on coding course. You will learn various supervised and unsupervised learning algorithms and will implement them in your code from scratch.

Work on LIVE Projects

This course covers a lot of basic & advanced projects like face-recognition, sentiment analysis, recommendation system, coversational engine, AI music generator and much more.

Deep Learning

We will cover the latest advanced in deep learning - a growing field in Machine Learning.Deep learning applications are being used in computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics

Advanced Topics

Many of the topics are advanced, up-to-date and require great patience and coding skills. Make sure you have good programming fundamentals and practice of Python before joining this course.

Course Contents

  • Introduction
    Introduction to ML and Mathematical Concepts

    1. Introduction to Machine Learning
    2. Python 2.7 overview
    3. Linear algebra
    4. Statistics and Probability
    5. Numpy, Scipy and Scientific computation with Python
  • Algorithms
    Simple Machine Learning Algorithms & Data Handling

    1. Nearest Neighbour search and K-means clustering
    2. Decision trees and Naive Bayes
    3. Data Scraping, Handling, Cleaning
    4. Random Forest Classifiers
  • Features
    Features and Dimensions

    1. Features and Importance
    2. Feature scaling
    3. The Curse of Dimensionality
    4. SVD and Principal Component Analysis
  • Diving Deep
    A Deep Dive into Machine Learning

    1. Regression Techniques
    2. Numerical Optimization
    3. Introduction to Neural Networks
  • Deep Learning
    Deep Learning

    1. Neural Architectures and Training
    2. Deep learning methods
    3. Convolutions and the GoogLe Net
    4. Dimensions revisited: The Auto-encoder
    5. Recurrent and Combined Architectures
  • Advanced
    Additional Material

    1. Support Vector Machines
    2. Introduction to Unsupervised and Reinforcement Learning
    3. Transfer Learning
  • Projects
    Following Projects will be Assigned

    1. Handwritten digit classification
    2. Face Recognition
    3. Image classification and Object detection
    4. Automated music generation
    5. Text/Poem generating bot
    6. Recommender systems
    7. Emotion/Sentiment Analysis

Come, fall in LOVE

Batches Starting This August!

Online code Submission & Evaluation
2 Hacakthons and Special Sessions on Competitive Coding



(20 Lectures)

Enrollment Starting Soon

Contact Us
Call(Toll-free): 1800-2744-504


(Drop a line at admissions@codingblocks.com if you have further queries)

  • Why do this course?

    Machine learning evolves from artificial intelligence and study of pattern recognition. Today, when excessively huge amounts of data are being dealt with everyday, rather every moment, pattern recognition is something that helps large corporations and websites work magnificently with the users. Artificial intelligence has become a favourite with the customers, esp. intelligent personal assistants like Apple's Siri, Microsoft Cortana, etc. We hope to extend our knowledge to the students so they are ready to tackle such problems in real-world.

  • I know competitive programming. should I do this course?

    Machine Learning requires extensive knowledge of mathematics, which you also gain while handling problems in competitive programming. This course shall equip with the right tools to handle huge amounts of data and derive meaningful conclusions from data crunching. Handling real-world problems is exactly what machine learning is all about, a skill very useful in development tasks. competitive programming equips you with critical thinking, but machine learning teaches you how to apply that skill and solve complex problems.

  • Will this help me in interviews?

    Machine Learning and Data science has been called as the ‘Hottest job of the 21st century’. If you learn this course well, you’ll be able to impress quite a lot of interviewers across various interviews.

  • I don’t pass all the prerequisite criteria. Should I enroll?

    Even if you don’t possess understanding of all the prerequisites, we shall help you cover every topic in detail and provide overview before diving deep into machine learning and data science. Python is a relatively easy language to learn, and you can pick up the basics very quickly. Therefore, you’ll have ample amount of time before the course to brush-up/learn the fundamentals.


Shubham Dokania

A graduate from DTU in Mathematics and Computing, Shubham is an avid Machine learning researcher, data scientist and web developer. Having already presented a paper at IEEE CISS 2017 at the John Hopkins University, he is now doing his research at IIIT Delhi, supervised by Dr. Ganesh Bagler. He has worked with various startups and won multiple hackathons including HackIndia and Code For India. Owing to his expertise and extensive research, he has been invited by various workshops and meets to deliver talks on Machine Learning and AI techniques, including PyData New Delhi.
He believes that the best way to learn is by doing and building. Working with the sciences of the future, Shubham aims to push forward the boundaries of what machines can achieve.


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