Course to learn Python, ML/AI, Computer Vision

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  • 100% Online

    Online Live Interactive + Recorded Sessions

  • Whatsapp Group For Discussions
  • Price

    till 31st May

    ₹999 4999
  • Duration5 weeks
  • Projects12+
  • Batch starts31st May
  • Refund

    Within 7 days

    100%

Learn Python, ML/AI and ComputerVision from scratch and build 12+ projects with us in 5 weeks

The course aims to groom the students as a good programmer who can learn new things quickly and add new skills to the arsenal.

the course consists of a number of different projects that the students need to solve. The problems are aimed to make you better at Python, Machine Learning, Artificial intelligence, Computer Vision.

Week 1

  • Python Foundation
    • Syntax

    • Variables

    • Data Types

    • Arithmetic Operators

    • Conditional Statements - if-else, if-elif-else

    • Comparison Operators

    • Logical Operators

    • Loops - While, For

    • Break and Continue Statements

    • Solve problems on Leetcode

  • Virtual Environments
    • How to work on multiple projects

    • Creating a requirements file for projects

    • Understanding program versions

  • Working with git
    • What is git and why do we need it?

    • Installing git in your system

    • Setup repository on Github

    • Push your code to Github

    • Common git commands and practice

Week 2

Week 3

  • Machine Learning - Introduction
    • What is Machine Learning?

    • Why use it?

    • Labeled Data and Unlabeled Data

    • Types of ML - Supervised Learning, Unsupervised Learning, Reinforcement Learning

    • Common Tasks in ML - Regression, Classification, and Clustering

  • Exploring ML
    • Important functions in ML - Target, Hypothesis, Loss, Cost, and Objective functions

    • Gradient Descent

    • Linear Regression - Implementation without using scikit-learn

    • Underfitting and Overfitting

    • KNN Classifier - Implementation without using scikit-learn

    • Confusion Matrix - Accuracy, Sensitivity (Recall), Specificity, Precision, etc

    • Using scikit-learn to use different ML algorithms

Week 4

  • Computer Vision
    • What is it?

    • What is an image?

    • Understanding image coordinate system

    • Grayscale and Color images

    • Introduction to OpenCV

    • Playing with image pixels

    • Convolution Operation

    • Blurring Image

    • Edge Extraction

    • Binary Threshold

    • Contours

    • Understanding video and playing around it with OpenCV

Introduction to Python, Machine Learning, Artificial Intelligence, Computer Vision

Learn Python, ML/AI and ComputerVision from scratch and build 12+ projects with us in 5 weeks

The course aims to groom the students as a good programmer who can learn new things quickly and add new skills to the arsenal.

The course consists of a number of different projects that the students need to solve. The problems are aimed to make you better at Python, Machine Learning, Artificial intelligence, Computer Vision. A Whatsapp group consisting of students and the Guides/TAs is available to help you all the time.

Certification

You will get certificate on successful completion of the course.

What does this course provide?

Note: The course is not limited to a set of things to be learned from. The whole idea of this course is to enhance the problem-solving skills of the student by providing practical tips and methods.

Directly
  • A Whatsapp group of the students along with the Guides/TAs.
  • Open source content (blogs, videos, articles, etc.) curated to help you learn and understand the basics of Python, ML, AI, Computer Vision.
  • A document (unique for each project ) which will help you understand how to solve the problem in a technical way, without spoilers!
  • Practical hands-on projects
  • Guidance on showcasing your projects on LinkedIn, Github, Blogs, etc.
  • A Guide/TA to explain complex information and clear your doubts.
  • Practical datasets, which are a mix of publicly available datasets and custom created ones for the course.
Indirectly
  • A better GitHub profile.
  • Good theoretical as well as practical knowledge of data science
  • Write blogs that are actually helpful to others.v
  • Peer-based learning.
  • Learn to read code, error messages, and fix them.
  • Learn to read the documentation of many different (generally open-source) software and use them.
  • Find out the challenges in real-world projects and try to fix them as much as you can.
  • Practical tips to approach and solve the problems.

Prerequisites

Basics of coding

You should know the basics of programming. Practice with with algorithms would be helpful.

Lapotp and internet (required)

that's it

What does this course not provide?

Note: This is not a traditional course. There are no live coding sessions. Instead of teachers, we have guides who will help you solve your problems and explain complex things when required. 1. Live coding sessions. 2. Deep mathematical background.

course

Vinay Khonragade

Deep Learning Research | Executive Director @codevectorlabs

Course Rating

4.1 average based on 4 reviews.
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3 Reviews

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James Anderson
Excellent

Very well built theme, couldn't be happier with it. Can't wait for future updates to see what else they add in.

course
Sarah Taylor
Video Quality!

Was really easy to implement and they quickly answer my additional questions!

course
David Warner
Perfect Coding!

Stunning design, very dedicated crew who welcome new ideas suggested by customers, nice support.

course
King Kong
Perfect Video!

Stunning design, very dedicated crew who welcome new ideas suggested by customers, nice support.