AutoKeras Project in Finance – A Guide to build a deep learning model in Python using in-vehicle coupon recommendation Data.
An end-to-end Data Science Tutorials on Clustering using German Credit Dataset in Python In this Learn by Coding tutorial, you will learn how to perform clustering (customer segmentation) using KMeans algorithm in Python for German Credit Dataset. This dataset is freely available. We learn how to plot / visualize different feature of the …
Applied Machine Learning and Data Science is made easy at SETScholars. SETScholars aims to guide you to become a Predictive Analytics & Data Science specialist by exploring machine learning & deep learning tools in Python, R & SQL. In this end-to-end learn by coding article, you will learn how to do an end-to-end predictive analytics project on Population Forecasting of India using ARIMA and FBProphet in Python.
Machine Learning and Artificial Intelligence Machine Learning and Artificial Intelligence are creating a huge buzz worldwide. The plethora of applications in Artificial Intelligence have changed the face of technology. These terms Machine Learning and Artificial Intelligence are often used interchangeably. However, there is a stark difference between the two that is still unknown to the industry professionals. …
Shi-Tomasi Corner Detector Preliminaries import cv2 import numpy as np from matplotlib import pyplot as plt Load image image_bgr = cv2.imread(‘images/plane_256x256.jpg’) image_gray = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2GRAY) Define Corner Parameters corners_to_detect = 10 minimum_quality_score = 0.05 minimum_distance = 25 Detect Corners corners = cv2.goodFeaturesToTrack(image_gray, corners_to_detect, minimum_quality_score, minimum_distance) corners = np.float32(corners) Mark Corners for corner in corners: x, …
Load Images Preliminaries import cv2 import numpy as np from matplotlib import pyplot as plt Load Image As Greyscale image = cv2.imread(‘images/plane.jpg’, cv2.IMREAD_GRAYSCALE) plt.imshow(image, cmap=’gray’), plt.axis(“off”) plt.show() Python Example for Beginners Special 95% discount 2000+ Applied Machine Learning & Data Science Recipes Portfolio Projects for Aspiring Data Scientists: Tabular Text & Image Data Analytics …
Enhance Contrast Of Greyscale Image Preliminaries import cv2 import numpy as np from matplotlib import pyplot as plt Load Image As Greyscale image = cv2.imread(‘images/plane_256x256.jpg’, cv2.IMREAD_GRAYSCALE) Enhance Image image_enhanced = cv2.equalizeHist(image) View Image plt.imshow(image_enhanced, cmap=’gray’), plt.axis(“off”) plt.show() Python Example for Beginners Special 95% discount 2000+ Applied Machine Learning & Data Science Recipes Portfolio Projects …
Detect Edges Preliminaries import cv2 import numpy as np from matplotlib import pyplot as plt Load image image_gray = cv2.imread(‘images/plane_256x256.jpg’, cv2.IMREAD_GRAYSCALE) Detect Edges median_intensity = np.median(image_gray) lower_threshold = int(max(0, (1.0 – 0.33) * median_intensity)) upper_threshold = int(min(255, (1.0 + 0.33) * median_intensity)) image_canny = cv2.Canny(image_gray, lower_threshold, upper_threshold) View Edges plt.imshow(image_canny, cmap=’gray’), plt.axis(“off”) plt.show() …
Blurring Images Preliminaries import cv2 import numpy as np from matplotlib import pyplot as plt Load Image As Greyscale image = cv2.imread(‘images/plane_256x256.jpg’, cv2.IMREAD_GRAYSCALE) Blur Image image_blurry = cv2.blur(image, (5,5)) View Image plt.imshow(image_blurry, cmap=’gray’), plt.xticks([]), plt.yticks([]) plt.show() Python Example for Beginners Special 95% discount 2000+ Applied Machine Learning & Data Science Recipes Portfolio Projects for …
Binarize Images Preliminaries import cv2 import numpy as np from matplotlib import pyplot as plt Load Image As Greyscale image_grey = cv2.imread(‘images/plane_256x256.jpg’, cv2.IMREAD_GRAYSCALE) Apply Adaptive Thresholding max_output_value = 255 neighorhood_size = 99 subtract_from_mean = 10 image_binarized = cv2.adaptiveThreshold(image_grey, max_output_value, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, neighorhood_size, subtract_from_mean) View Image plt.imshow(image_binarized, cmap=’gray’), plt.axis(“off”) plt.show() Python Example for Beginners Special 95% …
Preprocessing Iris Data Preliminaries from sklearn import datasets import numpy as np from sklearn.cross_validation import train_test_split from sklearn.preprocessing import StandardScaler Load Data iris = datasets.load_iris() X = iris.data y = iris.target Split Data For Cross Validation X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) Standardize Feature Data sc = StandardScaler() sc.fit(X_train) X_train_std = sc.transform(X_train) …
Preprocessing Categorical Features Often, machine learning methods (e.g. logistic regression, SVM with a linear kernel, etc) will require that categorical variables be converted into dummy variables (also called OneHot encoding). For example, a single feature Fruit would be converted into three features, Apples, Oranges, and Bananas, one for each category in the categorical feature. There are common ways to preprocess …