Note: In this tutorial, I am using a free bootstrap template from startbootstrap. You can find the code of this tutorial in Github. Below I am giving the URL.
$ python -m venv myvenv#activate
4. Install Django
$ pip install django==3.1.5
5. Start a project
$ django-admin startproject django_blog
6. Change Directory
$ cd django_blog
7. Start a app
$ django-admin startapp blog
8. Create 3 necessary folders —
What is Machine Learning?
Wikipedia — Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.
Top 10 Applications of Machine Learning:
Install this packages using pip:
pip install matplotlib
pip install sklearn
pip install numpy
# %matplotlib inline
from sklearn.datasets import fetch_openml
import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.model_selection import train_test_split
Here I am downloading our MNIST dataset and normalizing it. …
The pipeline itself will look as follows:
import cv2 #install using "pip install opencv-python"
import numpy as np #install using "pip install…
By seeing above image now you are very excited for implement it (like me). So not wasting too much time let’s jump to the code.
OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products.
import cv2 as cv
import numpy as np
img_path = "data/palm.jpg"
img = cv.imread(img_path)
Importing necessary packages, if you have not this packages, you can install it through ‘pip install [package_name]’.
import numpy as np
import pandas as pd
from keras.preprocessing.text import Tokenizer
from nltk import download
from nltk.corpus import stopwords
from tensorflow.keras.layers import Input, LSTM, Embedding, Dense, Concatenate, TimeDistributed
from tensorflow.keras.models import Model
from tensorflow.keras.callbacks import EarlyStopping
from attention import AttentionLayer
Finding a useful sentence from large articles or extracting an important text from a larger text is what we call a text summarization. But it is very difficult for human beings to find useful from large documents of text manually so we are using automatic text summarization. In this article, I am going to talk about the automatic text summary approach. So let’s get started.
Automatic text summarization is a machine learning problem of extracting short, useful, or simply important text summaries from a long document.
Real-World Application of Automatic Text Summarization:
There are many applications of Automatic Text Summarization. …
Data visualizations make big and small data easier for the human brain to understand, and visualization also makes it easier to detect patterns, trends, and outliers in groups of data.
Installation: pip install matplotlib
Here I am gonna show How to Implement SVM, Logistics Regression, Naive Bayes, Decision Tree, Random Forest in Python using Scikit-learn or sklearn. And yeah this is too easy to implement, just write three lines of Python code, and you get your Decision Tree classifier.
Because this is beauty of sklearn (Scikit-learn).
Note: You can get this notebook in my Github, I give you link below.
So let’s dirty our hands by some coding.
First we need a dataset, and I have a dataset of Market where you have to predict that customer purchasing item or not.
A chatbot also known as “conversational agents” is a computer program that simulates human conversation through voice commands or text chats or both. Like other AI tools, Chatbots will be used to further enhance human capabilities and free humans to be more creative and innovative, spending more of their time on strategic rather than tactical activities.
There are lots of applications for chatbots! These are the following some popular application that many Industries use: