Installing framework for computer vision, image processing, data analytics and machine learning
Installing framework for computer vision, image processing, data analytics and machine learning | Machine learning 101 | Starter guide of ML for beginners
Time stamp
00:00 Start
1:50 Install python (Programming Language) v3.10
4:50 Install VS code (Code Editor)
8:15 Python extension in VS code
8:43 Make virtual environment
(command: python -m venv nameOfVirtualEnv)
13:07 Activate virtual environment
(command: Scripts\activate)
15:36 Install Tensorflow in virtual environment created above 2.10.1
(pip install tensorflow==2.10.1)
19:25 Check tensorflow version
(import tensorflow as tf
print(tf.version.Version))
22:15 Install other dependencies - seaborn, sci-kit learn, sci-kit image, opencv, sci-kit image, opencv, pillow, matplotlib, tensorflow-datasets
36:10 Interactive window (similar to notebook in vscode)
pip install seaborn
pip install sci-kit learn
pip install sci-kit image
pip install opencv-python==4.6.0.66
pip install tensorflow-datasets==4.6.0
Avoid duplication:
numpy (is installed when you install tensorflow)
pandas (is installed when you install seaborn)
scipy (is installed when you install sci-kit learn)
import tensorflow as tf
import numpy as np
import cv2
import PIL
import skimage
import sklearn
import pandas as pd
import matplot.pyplot as plt
import seaborn as sbn
import tensorflow_datasets as tfds
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