Udemy Python courses are overpriced, Get Python courses for $5 with Certificate

Learning a language like python should not cost you a fortune, it should be affordable and it should not cost your $12.99 to get high quality courses.

In this article I will list out python courses that you can get for $5 which will give you a Certificate once you're done. I tell you what learning platform to get more of them.

If you are excited and ready to learn and become a certified Python developer let us dive in.

Python courses

These Python courses are from Eduonix e-learning Platform. On the Platform you have access to thousands of courses on Programming for a discounted price of $5 (60% off).

You also have access to their numerous E-Degrees and Certifications.

To visit their page, click on any of the buttons below.

Below are some of the python courses:

Python tutorial from Zero to Hero: + Machine Learning

data science, machine learning, python, artificial intelligence, math, statistics

Instructor: Shyngys

Lecture: 7 lectures

Time: 4.25 hours

Language: English

Price: $5

About this Course

Q&A

Will this course give you core python skills?

Yes, it will. There is a range of exciting opportunities for Python developers. All of them require a solid understanding of Python, and that’s what you will learn in this course.

Will the course teach me data science, machine learning, and artificial intelligence?

Yes, of course!

Why should you take this course?

Basically, because the course offers on-demand skills and gives you a wide range of opportunities.

Is any practice?

a ton of practice.

What if I have questions?

Feel free to ask me! I will satisfy your every question.

You literally can’t lose.

SKILLS YOU WILL GAIN

Data Science logic
the Power of A.I

WHAT YOU WILL LEARN

Have a solid understanding of the Python programming language fundamentals!
Various Branches of Python: Machine Learning, Artificial Intelligence, Data Science, etc...!
Learn to Write CLEAN code
Make your CV/resume attractive

Learn more

Time Series Analysis and Forecasting using Python

Learn about time series analysis & forecasting models in Python |Time Data Visualization|AR|MA|ARIMA|Regression| ANN

Instructor: Start Tech Academy

Lectures: 95 lectures

Time: 13 hours

Language: English

Price: $5

About this Course

You're looking for a complete course on Time Series Forecasting to drive business decisions involving production schedules, inventory management, manpower planning, and many other parts of the business., right?

You've found the right Time Series Analysis and Forecasting course. This course teaches you everything you need to know about different forecasting models and how to implement these models in Python.

After completing this course you will be able to:

Implement time series forecasting models such as AutoRegression, Moving Average, ARIMA, SARIMA etc.

Implement multivariate forecasting models based on Linear regression and Neural Networks.

Confidently practice, discuss and understand different Forecasting models used by organizations

SKILLS YOU WILL GAIN

Use Pandas DataFrames to manipulate Time Series data and make statistical computations.
Learn about Auto regression and Moving average Models
Understand the business scenarios where Time Series Analysis is applicable

WHAT YOU WILL LEARN

Get a solid understanding of Time Series Analysis and Forecasting
Building 5 different Time Series Forecasting Models in Python
Learn about ARIMA and SARIMA models for forecasting

Learn more

Python Ethical Hacking : Build tools for ethical hacking

Learn hacking with Python by building your own tools

Instructor: Frahaan Hussain

Lectures: 121 lectures

Time: 10 hours

Language: English

Price: $5

About this Course

Python is one of the most used programming languages in the world and its significance can't be ignored. Python has gained immense popularity recently owing to its performance in various fields like machine learning, data science, data analytics and cyber security. This course is designed in python to make ethical hacking easier for students since python is one of the most easy programming languages to learn. The concepts used in this course are fairly simple and anyone with some knowledge of computer science can try this course.

In this course we will learn following topics:

Introduction to basics of computer networks

What is hacking

How to stay anonymous

Learn how to track IP location

Create your own undetectable malware

Execute system commands on the victim machine using backdoor

Download and upload files to victim machine

How to create a Trojan

How to steal wifi passwords stored on the PC

How to intercept, manipulate and craft network packets

How to perform man in the middle attack

Crack password protected zipped files

Build your own undetectable key logger

Create a botnet with a command and control center

How to protect yourself online

SKILLS YOU WILL GAIN

Create your own undetectable malware
Create a botnet with a command and control center
Execute system commands on the victim machine using backdoor

WHAT YOU WILL LEARN

Introduction to basics of computer networks
How to stay anonymous
How to perform man in the middle attack
Build your own undetectable key logger

Learn more

Object Oriented Programming in Python - Aided with Diagrams

Concept Building, Syntax and Examples of Object Oriented Programming (OOP) in Python including Inheritance

Instructor: Frahaan Hussain

Lectures: 6 lectures

Time: 0.43 hours

Language: English

Price: $5

About this Course

This course includes;

-Class,

-Objects,

-Inheritance (Multi-level and Multi-layers of Inheritance)

-Overriding the functionality of Parent Class

-Method Resolution Order

-Operator Overloading

with concepts, diagrams, syntax and examples and

Some of the Common Operator Overloading Special Functions in Python

# Operator Expression Internally

# Addition p1 + p2 p1.__add__(p2)

# Subtraction p1 - p2 p1.__sub__(p2)

# Multiplication p1 * p2 p1.__mul__(p2)

# Power p1 ** p2 p1.__pow__(p2)

# Division p1 / p2 p1.__truediv__(p2)

SKILLS YOU WILL GAIN

Python
Object Oriented Programming
Jupyter Notebook

WHAT YOU WILL LEARN

Concept Building of Objects Operator Overloading
Classes, Inheritance
Operator Overloading

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Computer Vision: Python OCR & Object Detection Quick Starter

Quick Starter for Optical Character Recognition, Image Recognition Object Detection and Object Recognition using Python

Instructor: Abhilash Nelson

Lecture: 44 lectures

Time: 4.25 hours

Language: English

Price: $5

About this Course

Hi There!

Welcome to my new course 'Optical Character Recognition and Object Recognition Quick Start with Python'. This is the third course from my Computer Vision series.

Image Recognition, Object Detection, Object Recognition and also Optical Character Recognition are among the most used applications of Computer Vision.

Using these techniques, the computer will be able to recognize and classify either the whole image, or multiple objects inside a single image predicting the class of the objects with the percentage accuracy score. Using OCR, it can also recognize and convert text in the images to machine readable format like text or a document.

Object Detection and Object Recognition is widely used in many simple applications and also complex ones like self driving cars.

This course will be a quick starter for people who want to dive into Optical Character Recognition, Image Recognition and Object Detection using Python without having to deal with all the complexities and mathematics associated with the typical Deep Learning process.

Let's now see the list of interesting topics that are included in this course.

At first we will have an introductory theory session about Optical Character Recognition technology.

After that, we are ready to proceed with preparing our computer for python coding by downloading and installing the anaconda package and will check and see if everything is installed fine.

Most of you may not be coming from a python based programming background. The next few sessions and examples will help you get the basic python programming skill to proceed with the sessions included in this course. The topics include Python assignment, flow-control, functions and data structures.

Then we will install the dependencies and libraries that we require to do the Optical Character Recognition. We are using the Tesseract Library to do the OCR. At first we will install the Library and then its python bindings. We will also install OpenCV, which is the Open Source Computer Vision library in Python.

We also will install the Pillow library, which is the Python Image Library. Then we will have an introduction to the steps involved in the Optical Character Recognition and later will proceed with coding and implementing the OCR program. We will use a few example images to do a Character Recognition testing and will verify the results.

Then we will have an introduction to Convolutional Neural Networks , which we will be using to do the Image Recognition. Here we will be classifying a full image based on the single primary object in it.

We will then proceed with installing the Keras Library which we will be using to do the Image recognition. We will be using the built in , pre-trained Models that are included in Keras. The base code in python is also provided in the Keras documentation.

At first We will be using the popular pre-trained model architecture called the VGGNet. we will have an introductory session about the architecture of VGGNet. Then we will proceed with using the pre-trained VGGNet 16 Model included in keras to do Image Recognition and classification. We will try with a few sample images to check the predictions. Then will move on to a deeper VGGNet 19 Model included in keras to do Image Recognition and classification.

Then we will try the ResNet pre-trained model included with the Keras library. We will include the model in the code and then we will try with a few sample images to check the predictions.

And after that we will try the Inception pre-trained model. We will also include the model in the code and then we will try with a few sample images to check the predictions. Then will go ahead with the Xception pre-trained model. Here also, we will include the model in the code and then we will try with a few sample images.

And those were Image Recognition pre-trained models, which can only label and classify a complete image based on the primary object in it. Now we will proceed with Object Recognition in which we can detect and label multiple objects in a single image.

First we will have an introduction to MobileNet-SSD Pre-trained Model, which is a single shot detector that is capable of detecting multiple objects in a scene. We will also be having a quick discussion about the dataset that is used to train this model.

Later we will be implementing the MobileNet-SSD Pre-trained Model in our code and will get the predictions and bounding box coordinates for every object detected. We will draw the bounding box around the objects in the image and write the label along with the confidence value.

Then we will go ahead with object detection from a live video. We will be streaming the real-time live video from the computer's webcam and will try to detect objects from it. We will draw rectangles around each object detected in the live video along with the label and confidence.

In the next session, we will go ahead with object detection from a pre-saved video. We will be streaming the saved video from our folder and will try to detect objects from it. We will draw rectangles around each object detected along with the label and confidence.

Later we will be going ahead with the Mask-RCNN Pre-trained Model. In the previous model, we were only able to get a bounding box around the object, but in Mask-RCNN, we can get both the box coordinates as well as the mask over the exact shape of the object detected. We will have an introduction about this model and its details.

Later we will be implementing the Mask-RCNN Pre-trained Model in our code and as the first step we will get the predictions and bounding box coordinates for every object detected. We will draw the bounding box around the objects in the image and write the label along with the confidence value.

Later we will be getting the mask returned for each object predicted. We will process that data and use it to draw translucent multi coloured masks over each and every object detected and write the label along with the confidence value.

Then we will go ahead with object detection from a live video using Mask-RCNN. We will be streaming the real-time live video from the computer's webcam and will try to detect objects from it. We will draw the mask over the perimeter of each object detected in the live video along with the label and confidence.

And like we did for our previous model, we will go ahead with object detection from a pre-saved video using Mask-RCNN. We will be streaming the saved video from our folder and will try to detect objects from it. We will draw coloured masks for objects detected along with the label and confidence.

The Mask-R CNN is very accurate with a vast class list but will be very slow in processing images using low power CPU based computers. MobileNet-SSD is fast but less accurate and low in number of classes. We need a perfect blend of speed and accuracy which will take us to Object Detection and Recognition using YOLO pre-trained model. we will have an overview about the yolo model in the next session and then we will implement yolo object detection from a single image.

And using that as the base, we will try the yolo model for object detection from a real time webcam video and we will check the performance. Later we will use it for object recognition from the pre-saved video file.

To further improve the speed of frames processed, we will use the model called Tiny YOLO which is a lightweight version of the actual yolo model. We will use tiny yolo at first for the pre-saved video and will analyse the accuracy as well as speed and then we will try the same for a real-time video from a webcam and see the difference in performance compared to actual yolo.

That's all about the topics which are currently included in this quick course. The code, images and libraries used in this course has been uploaded and shared in a folder. I will include the link to download them in the last session or the resource section of this course. You are free to use the code in your projects with no questions asked.

Also after completing this course, you will be provided with a course completion certificate which will add value to your portfolio.

So that's all for now, see you soon in the classroom. Happy learning and have a great time.

SKILLS YOU WILL GAIN

Learn Optical Character Recognition
Basic Understanding of Image Recognition using Keras
Object Recognition using MobileNet SSD

WHAT YOU WILL LEARN

Object Detection
Image Recognition
Object Recognition
Deep Learning

Learn more

The Complete Python Hacking Course: Beginner to Advanced!

Learn ethical hacking, penetration testing and network security while working on Python coding projects!

Instructor: Joseph Delgadillo

Lectures: 89 lectures

Time: 17 hours

Language: English

Price: $10

About this Course

If you would like to master ethical hacking, you are going to LOVE our bestselling Python course! Learn ethical hacking and penetration testing while working on practical Python coding projects. We will cover the following topics in this course:

Introduction and setup

Port and vulnerability scanning

SSH and FTP attacks

Password cracking

Sniffers, flooders and spoofers

DNS spoofing

Network analysis

Coding a multi-functioning reverse shell

Keyloggers

Command and control center

Website penetration testing

This course was designed for students interested in intermediate to advanced level ethical hacking tutorials, however it is still taught in a step-by-step, beginner friendly method. English subtitles are available and all lectures are downloadable for offline viewing. 1 on 1 assistance with the coding projects is available within the discussion forum.

Still not sold? Check out these great reviews!

"It's already fun wow. I enjoy learning ethical hacking and python"

"Great even for non-programmers!"

"Great Course"

Thank you for taking the time to read this and we hope to see you in the course!

SKILLS YOU WILL GAIN

Ethical Hacking
Network Security

WHAT YOU WILL LEARN

Python Hacking
Python Programming

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Python For Data Analysis and Data Science: Zero To Mastery With Pandas

Learn how to use Python for Data Science, Machine Learning & Data Analysis, Learn Hand's on Pandas and NumPy With 100+ Exercises and 4 Real Life Projects !

Instructor: PRUTHVIRAJA L

Lecture: 103 lectures

Time: 15.5 hours

Language: English

Price: $10

About this Course

Hello, dear learning aspirants, welcome to “Python For Data Analysis and Data Science: Zero To Mastery With Pandas ”. We love programming. Python is one of the most popular programming languages in today is technical world. Python offers both object oriented and structured programming features. Hence, we are interested in data analysis with Pandas in this course.

This course is for those who are ready to take their data analysis skill to the next higher level with Python data analysis toolkit, i.e. "Pandas".

This tutorial is designed for beginners and intermediates but that does not mean that we will not talk about the advanced stuff as well. Our approach to teaching in this tutorial is simple and straightforward, no complications are included to make us bored Or lose concentration.

In this tutorial, I will be covering all the basic things you will need to know about the Pandas to become a data analyst or data scientist.

We are adopting a hands on approach to learn things easily and comfortably. You will enjoy learning as well as the exercises to practice along with the real life projects (The projects included are the part of large size research oriented industry projects).

I think it is a wonderful platform and I got a wonderful opportunity to share and gain my technical knowledge with the learning aspirants and data science enthusiasts.

We will also provide you with a course completion certificate once you are done with all the sessions and it will add great value to your career.

Who is this course for?

Data Analysis Beginner
Business and Analyst
Students and Other Professionals
Beginner Python developers Curious to learn about Data Science
Aspiring data scientists who want to add Python to their tool arsenal
Any curious learner who wants to update their knowledge in Business Analysis
AI and ML aspirants to upgrade their knowledge in Data Preprocessing before applying the machine learning algorithms to their projects

What should you know before starting this course?

No Prior Knowledge or Experience Needed, Only a Passion to Learn !

Basic knowledge of data types (Basic Maths, integers, floating point numbers, Logic Conditions) etc, but not necessary

Basic Or intermediate experience with Microsoft Excel, but not necessary.

What you will learn?

You will become a specialist in the following things while learning via this course

“Data Analysis With Pandas”.

You will be able to analyze a large file

Build a Solid Foundation in Data Analysis with Python

After completing the course you will have professional experience on;

Pandas Data Structures: Series, DataFrame and Index Objects

Essential Functionalities

Data Handling

Data Pre processing

Data Wrangling

Data Grouping

Data Aggregation

Pivoting

Working With Hierarchical Indexing

Converting Data Types

Time Series Analysis

Advanced Pandas Features and much more with hands on exercises and practice works.

What will students achieve or be able to do after taking your course?

How to code with Pandas toolkit

Learn hundreds of methods and attributes across numerous pandas objects

Manipulate data quickly and efficiently

Create dataframes with pandas and Recognize analytical approaches to data

Solid Foundation in Data Analysis with Python

I think it is a wonderful platform and I got a wonderful opportunity to share and gain my technical knowledge with the learning aspirants and data science enthusiasts.

I wish you all good luck with the course and happy learning. Let's dive into the course and engage with the knowledge.

SKILLS YOU WILL GAIN

Python
Data Science
Data Analysis
Data Munging and Data Cleaning

WHAT YOU WILL LEARN

Data Science
Data Analysis
Python
How to use Pandas

Learn more

Computer Vision: Face Recognition Quick Starter in Python

Quickly Build Python Deep Learning based Face Detection, Recognition, Emotion , Gender and Age Classification Systems

Instructor: Abhilash Nelson

Lecture: 42 lectures

Time: 3.41 hours

Language: English

Price: $5

About this Course

Hi There!

Welcome to my new course 'Face Recognition with Deep Learning using Python'. This is the second course from my Computer Vision series.

Face Detection and Face Recognition are the most used applications of Computer Vision. Using these techniques, the computer will be able to extract one or more faces in an image or video and then compare it with the existing data to identify the people in that image.

Face Detection and Face Recognition is widely used by governments and organizations for surveillance and policing. We are also making use of it daily in many applications like face unlocking of cell phones etc.

This course will be a quick starter for people who want to dive deep into face recognition using Python without having to deal with all the complexities and mathematics associated with the typical Deep Learning process.

We will be using a python library called face-recognition which uses simple classes and methods to get the face recognition implemented with ease. We are also using OpenCV, Dlib and Pillow for python as supporting libraries.

Let's now see the list of interesting topics that are included in this course.

At first we will have an introductory theory session about Face Detection and Face Recognition technology.

After that, we are ready to proceed with preparing our computer for python coding by downloading and installing the anaconda package. Then we will install the rest of dependencies and libraries that we require including the dlib, face-recognition, opencv etc and will try a small program to see if everything is installed fine.

Then we will have an introduction to the basics and working of face detectors which will detect human faces from a given media. We will try the python code to detect the faces from a given image and will extract the faces as separate images.

Then we will go ahead with face detection from a video. We will be streaming the real-time live video from the computer's webcam and will try to detect faces from it. We will draw rectangles around each face detected in the live video.

In the next session, we will customize the face detection program to blur the detected faces dynamically from the webcam video stream.

After that we will try facial expression recognition using pre-trained deep learning model and will identify the facial emotions from the real-time webcam video as well as static images

And then we will try Age and Gender Prediction using pre-trained deep learning model and will identify the Age and Gender from the real-time webcam video as well as static images

After face detection, we will have an introduction to the basics and working of face recognition which will identify the faces already detected.

In the next session, We will try the python code to identify the names of people and their faces from a given image and will draw a rectangle around the face with their names on it.

Then, like we did in face detection we will go ahead with face recognition from a video. We will be streaming the real-time live video from the computer's webcam and will try to identify and name the faces in it. We will draw rectangles around each face detected and beneath their names in the live video.

Most times during coding, along with the face matching decision, we may need to know how much matching the face is. For that we will get a parameter called face distance which is the magnitude of matching of two faces. We will later convert this face distance value to face matching percentage using simple mathematics.

In the coming two sessions, we will learn how to tweak the face landmark points used for face detection. We will draw lines joining these face landmark points so that we can visualize the points in the face which the computer is used for evaluation.

Taking the landmark points customization to the next level, we will use the landmark points to create a custom face make-up for the face image.

Also after completing this course, you will be provided with a course completion certificate which will add value to your portfolio.

So that's all for now, see you soon in the classroom. Happy learning and have a great time.

SKILLS YOU WILL GAIN

Face Detection from Images, Face Detection from Realtime Videos, Face Recognition from Images, Face Recognition from Realtime Videos
Face Distance, Face Landmarks Manipulation, Face Makeup

WHAT YOU WILL LEARN

Face Distance, Face Landmarks Manipulation, Face Makeup
Face Detection from Images, Face Detection from Realtime Videos, Face Recognition from Images, Face Recognition from Realtime Videos

Learn more

Computer vision: OpenCV Fundamentals using Python

Start your Deep Learning Computer Vision Endeavor with Strong OpenCV Basics in Python

Instructor: Abhilash Nelson

Lectures: 41 lectures

Time: 4.25 hours

Language: English

Price: $5

About this Course

Hi There!

Welcome to my new course OpenCV Fundamentals using Python. This is the first course from my Computer Vision series.

Let us see what are the interesting topics included in this course. First, we will have an overview of computer vision and the amazing OpenCV, the open-source computer vision library.

After that, we are ready to proceed with preparing our computer for installing OpenCV and later will proceed with installing OpenCV itself. Then we will try a one liner code to check if everything is working fine.

When I said this course is for complete beginners, I really mean it. Because even-if you are coming from a non-python background, the next few sessions and examples will help you get the basic python programming skill to proceed with the rest of the sessions. The topics include Python assignment, flow-control, functions and data structures.

Now we are all set to proceed with python computer vision exercises. But before that, we need to learn the theory of how a digital image is organized. Concept of pixels, color and grayscale channels, color codes, etc.

Then we will write our first OpenCV program in which we will simply load and display an image from our computer and we will write a grayscale version of this image back to our computer itself.

As you already know the basic building block of a digital image is pixels, we will use the power of OpenCV to manipulate the individual pixels of an image and modify it.

After that we will try the geometric transformations which include scaling or resizing the image, then translating or place shifting the image, flipping or changing sides, rotating the image by fixing an axis, and cropping the image to extract the region of interest.

In the coming two sessions, we will try the basic arithmetic and logical operations between two images. We will try to do the addition operation and subtraction operation between two images. We will also try the AND, OR, XOR and NOT binary bitwise operations for two images and will check the results obtained.

Later we will go ahead with Image masking, which is a technique of covering the unwanted areas of an image and display only the region of interest.

And after that, we will try Image Smoothing techniques. At first we will use our own filter to do a custom smoothing of image and later built in filters using algorithms like Gaussian Smoothing, average smoothing, Median and finally the bilateral smoothing.

Then we will see an advanced technique called thresholding which is very useful in preprocessing and preparing the image for computer vision algorithms. We will do exercises to demonstrate simple thresholding, Otsu thresholding, and adaptive thresholding.

Then we will check an interesting image color intensity plotting technique called the histograms. We will plot a histogram and will learn how we can analyze the histogram to predict the nature of the image.

By using this histogram and adjusting the values based on it, we can enhance the contrast of dull looking images. We will explore the technique called histogram equalization.

Image pyramids are different sized images generated and stacked one on top of others. We will explore how we can use OpenCV methods to generate image pyramids.

For us humans, it's an easy task to find an object in a scene and find the edges of it. For computers, it's not that easy. We will explore the OpenCV functions which enable us to find the edges using the Canny edge detection.

As we know from a computer, an image is just a collection of numbers. To find the edges, gradients or the pattern of intensity change of colors should be found out. We will use the gradient detection function of OpenCV to do that.

Then finally we will draw contours along the different objects in an image with the help of the above mentioned techniques and try to count the number of objects available in the scene.

That's all about the basics. The code and the images used in this course have been uploaded and shared in a folder. I will include the link to download them in the last session or the resource section of this course. You are free to use the code in your projects with no questions asked.

So that's all for now, see you soon in the classroom. Happy learning and have a great time.

SKILLS YOU WILL GAIN

OpenCV Image Manipulation Fundamentals using Python.
Python basics

WHAT YOU WILL LEARN

Computer Vision
OpenCV
Python

Learn more

Computer Vision & Deep Learning in Python: Novice to Expert

For all those who are interested in becoming experts in Deep Learning and Computer Vision using Python

Instructor: Abhilash Nelson

Lectures: 106 lectures

Time: 14 hours

Language: English

Price: $10

About this Course

Hello and welcome to my new course Computer Vision & Deep Learning in Python: From Novice to Expert

Making a computer classify an image using Deep Learning and Neural Networks is comparatively easier than it was before. Using all these ready made packages and libraries will be a few lines of code that will make the process feel like a piece of cake.

It is just like driving a big fancy car with an automatic transmission. You just only have to know how to use the basic controls to drive it. But, if you are a true engineer, you will also be fascinated by the internal working of the engine. At an expert level, you should be able to build your own version of that car from the scratch using the available basic components. Even though the performance may not match the commercial production line version, the experience knowledge you gain from it cannot be explained in words.

And only because of this we have our course divided into exactly two halves. In the first half, we will learn the working concepts of image recognition using computer vision and deep learning and will try to implement the simple versions of popular algorithms and techniques using plain python code. In the next half, we will use the popular packages and libraries to implement more complex deep learning image classification models.

Here is a quick list of sessions that are included in this course.

The first three sessions will be theory sessions in which we will have an overview of the concepts of deep learning and neural networks. We will also discuss the basics about a digital image and its composition

Then we will prepare your computer by installing and configuring Anaconda, the free and open source Python data science platform and the other dependencies to proceed with our exercises.

If you are new to python programming, do not worry. The next four sessions will be covering the basics of python program with simple examples.

And here comes the aforementioned first half with our own custom code and libraries.

In the coming two theory sessions we will be covering the basics of image classification and the list of datasets that we are planning to cover in this course.

Then we will do a step by step custom implementation of the k nearest neighbors (KNN) algorithm. It is a simple, easy to implement supervised machine learning algorithm that can be used to solve both nonlinear classification and regression problems. We will use our own created classes and methods without using any external library. The theory sessions involve learning the KNN basics. Then we will go ahead with downloading the dataset, loading, preprocessing and splitting the data. We will try to train the program and will do an image classification among the three sets of animals. Dogs, cats and pandas prediction using our custom KNN implementation.

Now we will proceed with Linear Classification. Starting with the Concept and Theory, we will proceed further with building our own scoring function and also implementing it using plain python code. Later we will discuss the loss function concepts and also the performance optimization concepts and the terminology associated with it.

Then we will start with the most important optimization algorithm for deep learning which is the Gradient Descent. We will have separate elaborate sessions where we will learn the concept and also implementation using the custom code for Gradient Descent. Later we will proceed with the more advanced Stochastic Gradient Descent with its concepts in the first sessions, later with implementing it using the custom class and methods we created.

We will then look at regularization techniques that can also be used for enhancing the performance and also will implement it with our custom code.

In the coming sessions, we will have Perceptron, which is a fundamental unit of the neural network which takes weighted inputs, processes it and is capable of performing binary classifications. We will discuss the working of the Perceptron Model. We will implement it using Python and also we will try to do some basic prediction exercises using the perceptron we created.

In deep learning, backpropagation is a widely used algorithm in training feed forward neural networks for supervised learning. We will then have a discussion about the mechanism of backward propagation of errors. Then to implement this concept, we will create our own classes and later implementation projects for a simple binary calculation dataset and also the MNIST optical character recognition dataset.

And with all the knowledge from the pain of making custom implementations. We can now proceed with the second half of deep learning implementation using the libraries and packages that are used for developing commercial Computer Vision Deep Learning programs

We will be using Keras which is an open-source neural network library written in Python. It is capable of running on top of TensorFlow, Theano and also other languages for creating deep learning applications

At first we will build a simple Neural Network implementation with Keras using the MNIST Optical Character Recognition Dataset. We will train and evaluate this neural network to obtain the accuracy and loss it got during the process.

In deep learning and Computer Vision, a convolutional neural network is a class of deep neural networks, most commonly applied to analyzing visual imagery. At first, we will have a discussion about the steps and layers in a convolutional neural network. Then we will proceed with creating classes and methods for a custom implementation of a Convolutional neural network using the Keras Library which features different filters that we can use for images.

Then we will have a quick discussion about the CNN Design Best Practices and then will go ahead with ShallowNet. The basic and simple CNN architecture. We will create the common class for implementing ShallowNet and later will train and evaluate the ShallowNet model using the popular Animals as well as CIFAR 10 image datasets. Then we will see how we can serialize or save the trained model and then later load it and use it. Even though a very shallow network, we will try to do a prediction for an image we give using ShallowNet for both the Animals and CIFAR 10 dataset

After that, we will try famous CNN architecture called LeNet for handwritten and machine printed character recognition. LeNet also, will create the common class and later will train, evaluate and save the LeNet model using the MNIST dataset. Later we will try to do a prediction for a handwritten digit image.

Then comes the mighty VGGNet architecture. We will create the common class and later will train, evaluate and save the VGGNet model using the CIFAR 10 dataset. After hours of training, later we will try to do a prediction for photos of a few common real life objects falling in the CIFAR 10 categories.

While training deep networks, it is helpful to reduce the learning rate as the number of training epochs increases. We will learn a technique called Learning Rate Scheduling in our next session and implement it in our python code.

Since we are spending hours training a model, if we do not checkpoint our training models at the end of a job, there is a great chance that we will have lost all of our hard earned results. We will see how we can efficiently do that in the coming sessions.

Enough with training using our little computer. Let us go ahead with popular Deep learning models already pre trained for us which are included in Keras library. They are trained on Imagenet data which is a collection of image data containing 1000 categories of images.

The first pre-trained model that we are dealing with is the VGGNet 16, we will download the already trained model and then do the prediction. Later will go a bit deeper with VGGNet 19 pre trained model and will do the image classification prediction.

The next pre trained model that we are using is the ResNet, which can utilize a technique called skip connections, or shortcuts to jump over some layers. We will do the image classification prediction with this network too.

Finally, we will get the Inception and Xception models. Which are convolutional neural networks trained on more than a million images from the ImageNet database. They learn by using Depth Wise Separable Convolutions. We will download the weights and do the image classification prediction with this network too.

Overall, this course will be the perfect recipe for custom and ready made components that you can use for your career in Computer Vision using Deep Learning.

All the example code and sample images with the dataset can be downloaded from the link included in the last session or resource section of this course.

We will also provide you with a course completion certificate once you are done with all the sessions and it will add great value to your career.

So best wishes and happy learning. See you soon in the classroom.

Bibliography & Reference Credits:

* CS231M Stanford University, CS231N Stanford University

* pyimagesearch blog by Dr. Adrian Rosebrock, Ph.D.

* Andrej Karpathy. CS231n: Convolutional Neural Networks for Visual Recognition.

* AndrejKarpathy.LinearClassification

* Machine Learning is Fun Adam Geitgey

* Andrew Ng. Machine Learning

* Andrej Karpathy. Optimization

* Karen Simonyan and Andrew Zisserman. Very Deep Convolutional Networks for Large Scale Image Recognition

Intro Background Video Credits:

* Machine Learning: Living in the Age of AI

SKILLS YOU WILL GAIN

Computer Vision
Deep Learning
Machine Learning
Data Science

WHAT YOU WILL LEARN

Computer Vision
Deep Learning
Machine Learning
Data Science

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CNN for Computer Vision with Keras and TensorFlow in Python

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

Instructor: Start Tech Academy

Lectures: 50 lectures

Time: 7 hours

Language: English

Price: $5

About this Course

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

You have 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.

SKILLS YOU WILL GAIN

Image Recognition
CNN models in Python
Keras
Tensorflow

WHAT YOU WILL LEARN

Get a solid understanding of Convolutional Neural Networks (CNN) and Deep Learning
Learn usage of Keras and Tensorflow libraries
Use Pandas DataFrames to manipulate data and make statistical computations.

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Superb Python Course - Become Certified Python Developer

Learn Every Details to be Python Developer

Instructor: Paul Carlo Tordecilla

Lectures: 89 lectures

Time: 3 hours

Language: English

Price: $10

About this Course

Certified Python Developer

This is the best course to fulfill your dream to become Python Developer. I will teach you more hands-on to enhance your programming skills faster. Python is a powerful programming language that can be used in Web development and Desktop application.Python has standard libraries that offer all the things needed to build a complex application.

With this course you can be beginner to professional in just 4 weeks.

SKILLS YOU WILL GAIN

Python
Software development

WHAT YOU WILL LEARN

Create automatic task
Create your own game
Create Web application
Create Desktop application

Learn more

Django 2.2 Masterclass - Build & Deploy Web Application With Python & Django

Learn Django 2.2 - Build Real Web Application With Python, Django, GIT and Deploy on Heroku Server! | Backend on Python

Instructor: Shubham Sarda

Lectures: 77 lectures

Time: 6 hours

Language: English

Price: $10

About this Course

Welcome to Django A-Z: Learn Django 2 By Building & Deploying Project!

One course that will help you to start your Web Development Journey from Scratch Step-by-Step. This course touches each and every important concept for Django beginners with it's the latest version Django 2,

Throughout this course you will learn about:

Development Environment Set-up. (Libraries, Extensions, IDE's, Virtualenv)

Django Flow & File Structure

Django URLs, Views and Templates

Models and SQLite3 Database

Set-up Static files and Media files Structure. (CSS, JS, Images)

Work with Administration Panel

Work with Forms and Fields

Writing Function-Based Views

Messages and Notification.

Django Authentication System

Functionality for Registration, Login, and Logout

Integrating Crispy Form to improve Registration Functionality

Understanding Restrictions - Page, Header

Relationship Between Models (Task & User)

Understanding Foreign Key Concept - ManyToMany Relationship, OneToOne Relationship

Working with Django Security Updates

4 Important Pillars to Deploy (git, GitHub, Heroku, Heroku CLI)

Working with GitHub Repository

Understanding the working of requirements txt and .gitignore

Working with Django Environ (Django Environment Variable)

Push project from Local System to GitHub

Working with Django Heroku (STATIC_ROOT, WSGI, gunicorn)

Working with Heroku CLI

Handling WSGI with gunicorn

Hiding Secret Key, DEBUG, Allowed Host, Database Information

Working with Django Security and Database Updates

After completing this course you will be ready to work on beginner's projects as Intern, Fresher or Freelancer and you will also be able to implement everything yourself! Most importantly you will be ready to divide deep for big available scope with Django in the future.

Enroll now and I will make sure you learn best about Django 2!

SKILLS YOU WILL GAIN

Integrating Crispy Form to improve Registration Functionality
Learn about MVT (Model, View Template)
Implement CRUD Functionality. (Create, Read, Update and Delete)

WHAT YOU WILL LEARN

Learn about Django Apps, Templates, Models & Migrations.
Understanding Django Authentication System, Foreign Key Concept

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Conclusion

The courses you have here are some of the Python courses, you can get more from Eduonix website by going through one of the courses.

You do not have to spend so much to get a certificate for Python programming, with just $5 this is possible, so that the opportunity now before it course always.

Note that the reason why the courses are $5 is because Eduonix is giving 60% off on all their courses and once you buy any course now, you have access to it for life. Even when the price goes back to the normal ones you already have your course.

I hope you take this opportunity because I will also take it now.

This is all from me today, happy learning as you gain more Python programming skills.

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