Convolutional Neural Networks in Python: CNN Computer Vision

Python for Computer Vision & Image Recognition - Deep Learning Convolutional Neural Network (CNN) - Keras & TensorFlow 2

4.52 (1116 reviews)
Data Science
7.5 hours
Aug 2022
last update
regular price

What you will learn

Get a solid understanding of Convolutional Neural Networks (CNN) and Deep Learning

Build an end-to-end Image recognition project in Python

Learn usage of Keras and Tensorflow libraries

Use Artificial Neural Networks (ANN) to make predictions

Use Pandas DataFrames to manipulate data and make statistical computations.


You're looking for a complete Convolutional Neural Network (CNN) course that teaches you everything you need to create a Image Recognition model in Python, right?

You've found the right Convolutional Neural Networks course!

After completing this course you will be able to:

  • Identify the Image Recognition problems which can be solved using CNN Models.

  • Create CNN models in Python using Keras and Tensorflow libraries and analyze their results.

  • Confidently practice, discuss and understand Deep Learning concepts

  • Have a clear understanding of Advanced Image Recognition models such as LeNet, GoogleNet, VGG16 etc.

How this course will help you?

A Verifiable Certificate of Completion is presented to all students who undertake this Convolutional Neural networks course.

If you are an Analyst or an ML scientist, or a student who wants to learn and apply Deep learning in Real world image recognition problems, this course will give you a solid base for that by teaching you some of the most advanced concepts of Deep Learning and their implementation in Python without getting too Mathematical.

Why should you choose this course?

This course covers all the steps that one should take to create an image recognition model using Convolutional Neural Networks.

Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.

What makes us qualified to teach you?

The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep learning techniques and we have used our experience to include the practical aspects of data analysis in this course

We are also the creators of some of the most popular online courses - with over 300,000 enrollments and thousands of 5-star reviews like these ones:

This is very good, i love the fact the all explanation given can be understood by a layman - Joshua

Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy

Our Promise

Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.

Download Practice files, take Practice test, and complete Assignments

With each lecture, there are class notes attached for you to follow along. You can also take practice test to check your understanding of concepts. There is a final practical assignment for you to practically implement your learning.

What is covered in this course?

This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems.

Below are the course contents of this course on ANN:

  • Part 1 (Section 2)- Python basics

    This part gets you started with Python.

    This part will help you set up the python and Jupyter environment on your system and it'll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.

  • Part 2 (Section 3-6) - ANN Theoretical Concepts

    This part will give you a solid understanding of concepts involved in Neural Networks.

    In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.

  • Part 3 (Section 7-11) - Creating ANN model in Python

    In this part you will learn how to create ANN models in Python.

    We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. Lastly we learn how to save and restore models.

    We also understand the importance of libraries such as Keras and TensorFlow in this part.

  • Part 4 (Section 12) - CNN Theoretical Concepts

    In this part you will learn about convolutional and pooling layers which are the building blocks of CNN models.

    In this section, we will start with the basic theory of convolutional layer, stride, filters and feature maps. We also explain how gray-scale images are different from colored images. Lastly we discuss pooling layer which bring computational efficiency in our model.

  • Part 5 (Section 13-14) - Creating CNN model in Python
    In this part you will learn how to create CNN models in Python.

    We will take the same problem of recognizing fashion objects and apply CNN model to it. We will compare the performance of our CNN model with our ANN model and notice that the accuracy increases by 9-10% when we use CNN. However, this is not the end of it. We can further improve accuracy by using certain techniques which we explore in the next part.

  • Part 6 (Section 15-18) - End-to-End Image Recognition project in Python
    In this section we build a complete image recognition project on colored images.

    We take a Kaggle image recognition competition and build CNN model to solve it. With a simple model we achieve nearly 70% accuracy on test set. Then we learn concepts like Data Augmentation and Transfer Learning which help us improve accuracy level from 70% to nearly 97% (as good as the winners of that competition).

By the end of this course, your confidence in creating a Convolutional Neural Network model in Python will soar. You'll have a thorough understanding of how to use CNN to create predictive models and solve image recognition problems.

Go ahead and click the enroll button, and I'll see you in lesson 1!


Start-Tech Academy


Below are some popular FAQs of students who want to start their Deep learning journey-

Why use Python for Deep Learning?

Understanding Python is one of the valuable skills needed for a career in Deep Learning.

Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:

    In 2016, it overtook R on Kaggle, the premier platform for data science competitions.

    In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools.

    In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.

Deep Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well.

What is the difference between Data Mining, Machine Learning, and Deep Learning?

Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions.

Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.


Convolutional Neural Networks in Python: CNN Computer Vision - Screenshot_01Convolutional Neural Networks in Python: CNN Computer Vision - Screenshot_02Convolutional Neural Networks in Python: CNN Computer Vision - Screenshot_03Convolutional Neural Networks in Python: CNN Computer Vision - Screenshot_04



Course resources

Setting up Python and Jupyter Notebook

Installing Python and Anaconda
Opening Jupyter Notebook
Introduction to Jupyter
Arithmetic operators in Python: Python Basics
Strings in Python: Python Basics
Lists, Tuples and Directories: Python Basics
Working with Numpy Library of Python
Working with Pandas Library of Python
Working with Seaborn Library of Python

Single Cells - Perceptron and Sigmoid Neuron

Activation Functions
Python - Creating Perceptron model

Neural Networks - Stacking cells to create network

Basic Terminologies
Gradient Descent
Back Propagation

Important concepts: Common Interview questions

Some Important Concepts

Standard Model Parameters


Tensorflow and Keras

Keras and Tensorflow
Installing Tensorflow and Keras

Python - Dataset for classification problem

Dataset for classification
Normalization and Test-Train split

Python - Building and training the Model

Different ways to create ANN using Keras
Building the Neural Network using Keras
Compiling and Training the Neural Network model
Evaluating performance and Predicting using Keras

Saving and Restoring Models

Saving - Restoring Models and Using Callbacks

Hyperparameter Tuning

Hyperparameter Tuning

CNN - Basics

CNN Introduction
Filters and Feature maps

Creating CNN model in Python

CNN model in Python - Preprocessing
CNN model in Python - structure and Compile
CNN model in Python - Training and results

Analyzing impact of Pooling layer

Comparison - Pooling vs Without Pooling in Python

Project : Creating CNN model from scratch

Project - Introduction
Data for the project
Project - Data Preprocessing in Python
Project - Training CNN model in Python
Project in Python - model results

Project : Data Augmentation for avoiding overfitting

Project - Data Augmentation Preprocessing
Project - Data Augmentation Training and Results

Transfer Learning : Basics

Transfer Learning

Transfer Learning in Python

Project - Transfer Learning - VGG16


June 29, 2022
its a good course which gives basic overview of CNN, which helps us to learn topics of computer vision.
June 23, 2022
It's an interesting application of Convolutional Neural Networks (CNN). It includes how to get a better model's accuracy.
June 15, 2022
I think you should explain syntax more deeply and maybe show us the difference between multi-layer, multi-neurons networks and theses smaller ones that we create. Generally, I would be very pleased with more network structure experiments.
February 13, 2022
Amazing Course learned a lot, but kindly see all video 2 times to understand more and do practice with it as well.
February 4, 2022
Thorough introduction to ANN and CNN. Perfect combination of theory and implementation and very clear instructions. I highly recommend this course to anyone who is interested in learning more about ANN and CNN using Tensorflow and Keras.
October 26, 2021
Excellently constructed! Well-balanced among logical learning flow and key points and details. Good coverage and careful illustrated and demonstrated for most topic and points
October 17, 2021
Clearly explained, and with a lot of practical examples. Teaching in this way allows the student to easily recognize the techniques applied and then try their own projects. A great starting point.
August 30, 2021
Great course! Very clear! Easy to follow! State of the art content. One pedagogical remark. Forget about the Perceptron, do all the examples in Tensorflow/Keras, even the simplest example. Build up the model, layer by layer, this will force student to type more and debug better. For example start a model with Input and Output layer. Push it as far as possible, then add another layer, etc. That will help create better intuitions about the final model.
August 23, 2021
The course has the most genuine explanation of CNN and how it works especially when it comes to the fundamentals that no one has ever explained to me this way. Thank you so much for the rewarding effort.
August 15, 2021
in the final project the implementation was not shown .just the accuracy scores were shown.i was expecting to see the working of the project.
June 15, 2021
Good Basic course on ANN, and CNN. This include introduction to transfer learning as well. Overall good course. However, codes have not provided. You have to code yourself
May 19, 2021
The theory explanations and then the practical implementation of the same made the concepts really clear.
May 19, 2021
really great :) Excellent . Actually I have done my project in different way by using Pandas library using Scikit learn. In that I used my own csv file. But here in tensorflow you are importing already existing dataset. Can you please help me to process my own csv file in tensorflow?
April 16, 2021
It's no non-sense course. It gets you quickly on your feet on one of the complex topics in Data Science.
April 15, 2021
So far, your presentation in theory and practice of ANN and CNN is the best explanation for me. Thank you sir.



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