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Python tutorials from basics to deep learning (with code)

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بواسطة Learn-with-us

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تحديث 2026/04

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list_alt محتويات المسار:

schedule 34h 11m من التعلم
smart_display 100 درس مُصور
smartphone مشاهدة عبر كافة الأجهزة

format_list_bulleted محتوى المسار التعليمي

lock 01 - Why do you need to learn programming?

lock 02 - What is programming?

lock 03 - What is command prompt?

lock 04 - What is a digital image?

lock 05 - What is Python?

lock 06 - Python basics - IDE & operators

lock 07 - Python basics - logical operators and basic math

lock 08 - A warning about round off errors in Python

lock 09 - if else elif statements in Python

lock 10 - lists tuples and dictionaries

lock 11 - numpy arrays

lock 12 - Python Recap of lists and numpy arrays

lock 13 - for and while Loops in Python

lock 14 - Python Functions

lock 15 - Python Classes

lock 16 - Understanding digital images for Python processing

lock 17 - Reading images in Python

lock 18 - Image processing using pillow in Python

lock 19 - image processing using scipy in Python

lock 20 - Introduction to image processing using scikit-image in Python

lock 21 - Scratch assay analysis with just 5 lines code in Python

lock 22 - Denoising microscope images in Python

lock 23 - Histogram based image segmentation in Python

lock 24 - Random Walker segmentation in Python

lock 25 - Reading Images, Splitting Channels, Resizing using openCV in Python

lock 26 - Denoising and edge detection using opencv in Python

lock 27 - CLAHE and Thresholding using opencv in Python

lock 28 - Thresholding and morphological operations using openCV in Python

lock 29 - Key points, detectors and descriptors in openCV

lock 30 - Image registration using homography in openCV

lock 31 - A few ways to read and write csv files in Python

lock 32 - Grain size analysis in Python using a microscope image

lock 33 - Grain size analysis in Python using watershed

lock 34 - Grain size analysis in Python using watershed - multiple images

lock 35 - Cell Nuclei analysis in Python using watershed segmentation

lock 36 - Introduction to Pandas - Data reading and handling

lock 37 - Introduction to Pandas - Data Manipulation

lock 38 - Introduction to Pandas - Data Sorting

lock 39 - Introduction to Pandas - Grouping Data

lock 40 - Introduction to Pandas - Dealing with missing (null) data

lock 41 - Introduction to Pandas - Plotting

lock 42 - Introduction to Seaborn Plotting in Python

lock 43 - What is machine learning anyway?

lock 44 - What is linear regression?

lock 45 - Linear regression using Sci-Kit Learn in Python

lock 46 - Splitting data into training and testing sets for machine learning

lock 47 - Multiple Linear Regression with SciKit-Learn in Python

lock 48 - What is logistic regression?

lock 49 - Logistic Regression using scikit-learn in Python

lock 50 - What is k-means clustering and how to code it in Python?

lock 51 - Image Segmentation using K-means

lock 52 - What is GMM and how to use it for Image segmentation?

lock 53 - How to pick optimal number of parameters for your unsupervised machine learning model?

lock 54 - Unsupervised and supervised machine learning - a reminder

lock 55 - How to read proprietary microscope images into Python

lock 56 - What are features in machine learning?

lock 57 - How to generate features in Python for machine learning?

lock 58 - What are Gabor filters?

lock 59 - What is Random Forest classifier?

lock 60 - How to use Random Forest in Python?

lock 61 - How to create Gabor feature banks for machine learning

lock 62 - Image Segmentation using traditional machine learning - The plan

lock 63 - Image Segmentation using traditional machine learning Part1 - FeatureExtraction

lock 64 - Image Segmentation using traditional machine learning - Part2 Training RF

lock 65 - Image Segmentation using traditional machine learning - Part3 Feature Ranking

lock 66 - Image Segmentation using traditional machine learning - Part4 Pickling Model

lock 67 - Image Segmentation using traditional machine learning - Part5 Segmenting Images

lock 67b - Feature based image segmentation using traditional machine learning. (Multi-training images)

lock 68 - Quick introduction to Support Vector Machines (SVM)

lock 68b - SVM vs. Random Forest for image segmentation

lock 70 - An overview of deep learning and neural networks

lock 71 - Malarial cell classification using CNN

lock 73 - Image Segmentation using U-Net - Part1 (What is U-net?)

lock 74 - Image Segmentation using U-Net - Part 2 (Defining U-Net in Python using Keras)

lock 75 - Image Segmentation using U-Net - Part 3 (What are trainable parameters?)

lock 76 - Image Segmentation using U-Net - Part 4 (Model fitting, checkpoints, and callbacks)

lock 77 - Image Segmentation using U-Net - Part 5 (Understanding the data)

lock 78 - Image Segmentation using U-Net - Part 6 (Running the code and understanding results)

lock 79 - What is Docker?

lock 80 - What is Dockerfile, Docker Image, and Docker Container

lock 81 - Testing Docker on Windows and introduction to basic commands

lock 82 - Dockerizing your python application

lock 83 - Running your Docker in the cloud

lock 84 - How to build a Docker (module) with your code and run it on APEER?

lock 85a - What are Autoencoders and what are they used for?

lock 85b - An introduction to autoencoders - in Python

lock 86 - Applications of Autoencoders - Denoising using MNIST dataset

lock 87 - Applications of Autoencoders - Denoising using custom images

lock 88 - Applications of Autoencoders - Anomaly Detection

lock 89 - Applications of Autoencoders - Domain Adaptation

lock 90 - Application of Autoencoders - Image colorization

lock 91 - Introduction to transfer learning

lock 92 - Autoencoders using transfer learning - Image colorization

lock 93 - Do not waste your time with deep learning (updated)

lock 94 - Denoising MRI images (also CT & microscopy images)

lock 95 - What is digital image filtering and image convolution?

lock 96 - What is Gaussian Denoising Filter?

lock 97 - What is median denoising filter?

lock 98 - What is bilateral denoising filter?

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description حول المسار (Description)

Welcome to my Python coding channel! Here, I'll teach you everything from the very basics to advanced topics in machine learning and deep learning. I'll focus a lot on image processing and other relevant topics.

How to cite my work?
YouTube video:
The general format for citing a YouTube video in APA (American Psychological Association) style is:
Author’s Last Name, First Initial. (Year, Month Day Published). Title of video [Video]. YouTube. URL

So, here is an example:
Bhattiprolu, S. (2023, August 23). 330 - Fine tuning Detectron2 for instance segmentation using custom data [Video]. YouTube. https://youtu.be/cEgF0YknpZw

GitHub code:
Author’s Last Name, First Initial. (Year). Title of Repository. GitHub. URL
Example:
Bhattiprolu, S. (2023). python_for_microscopists. GitHub. https://github.com/bnsreenu/python_for_microscopists/blob/master/330_Detectron2_Instance_3D_EM_Platelet.ipynb

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list_alt محتويات المسار:

schedule 34h 11m من التعلم
smart_display 100 درس مُصور
smartphone مشاهدة عبر كافة الأجهزة