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Machine Learning
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‎MACHINE_LEARNING.md

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# Machine Learning
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Machine Learning is making the computer learn from studying data and statistics.
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Machine Learning is a step into the direction of artificial intelligence (AI).
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Machine Learning is a program that analyses data and learns to predict the outcome.
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# Definition
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Machine Leaarning enables a functions / program to predict the outcome based on what it have learned for dataset.
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# Data Set
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In the mind of a computer, a data set is any collection of data. It can be anything from an array to a complete database.
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We can split the data types into three main categories:
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Numerical - Integers
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Categorical - data are values that cannot be measured up against each other. Example: a color value,
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Ordinal - data are like categorical data, but can be measured up against each other. Example: school grades where A is better than B and so on.
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In Machine Learning (and in mathematics) there are often three values that interests us:
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Mean - The average value
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Median - The mid point value (after sorting)
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Mode - The most common value
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speed = [99,86,87,88,111,86,103,87,94,78,77,85,86]

‎libraries/machine_learning/graph.py

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"""
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@Author: Aseem Jain
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@Linkedin: https://www.linkedin.com/in/premaseem/
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@Github: https://github.com/premaseem/pythonLab/tree/master/challenge
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"""
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import numpy
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import matplotlib.pyplot as plt
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numpy.random.seed(2)
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x = numpy.random.normal(3, 1, 100)
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y = numpy.random.normal(150, 40, 100) / x
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train_x = x[:80]
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train_y = y[:80]
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test_x = x[80:]
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test_y = y[80:]
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mymodel = numpy.poly1d(numpy.polyfit(train_x, train_y, 3))
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myline = numpy.linspace(0, 6, 100)
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plt.scatter(train_x, train_y)
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plt.plot(myline, mymodel(myline))
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plt.show()

‎libraries/pymongo/mongo_docker.sh

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## Mongo db docker setup
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# look existing images in system
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docker images | grep mongo
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# pull docker image
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docker pull mongo
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# Create a /mongodata directory on the host system
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mkdir -p ~/mongodata
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# Start the Docker container with the run command using the mongo image. The /data/db directory in the container is mounted as /mongodata on the host.
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docker run -it -v ~/docker/mongodata:/data/db -p 27017:27017 --name mongodb -d mongo
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# -it ( run terminal iteractive mode)
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# -p 27017:27017 (port mapping)
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# -v mongodata:/data/db ( data volume mount)
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# --name mongodb ( container name or tag)
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# -d run in detached mode / background
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# check logs onf container : docker logs <container name>
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docker logs mongodb
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# connect to docker container bash : docker exec -it <container name> bash
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docker exec -it mongodb bash
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# list out docker containers
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docker ps --format "table {{.ID}}\t{{.Names}}\t{{.Ports}}"
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# remove all docker images
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#docker image prune -a
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‎libraries/pymongo/run.py

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"""
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@Author: Aseem Jain
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@profile: https://www.linkedin.com/in/premaseem/
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"""
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import pymongo
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mongo_client = pymongo.MongoClient("mongodb://localhost:27017/")
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print("Show Dbs",mongo_client.list_database_names())
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my_db = mongo_client["mydatabase"]
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my_col = my_db["customers"]
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my_dict = {
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"name" : " prem aseem",
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"type" : "gold customer"
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}
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# _id = my_col.insert_one(my_dict)
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# print(_id)
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print(my_col.find_one())
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print(my_col.count_documents({}))
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my_records = [
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{ "name": "Amy", "address": "Apple st 652"},
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{ "name": "Hannah", "address": "Mountain 21"},
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{ "name": "Michael", "address": "Valley 345"},
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{ "name": "Sandy", "address": "Ocean blvd 2"},
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{ "name": "Betty", "address": "Green Grass 1"},
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{ "name": "Richard", "address": "Sky st 331"},
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{ "name": "Susan", "address": "One way 98"},
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{ "name": "Vicky", "address": "Yellow Garden 2"},
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{ "name": "Ben", "address": "Park Lane 38"},
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{ "name": "William", "address": "Central st 954"},
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{ "name": "Chuck", "address": "Main Road 989"},
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{ "name": "Viola", "address": "Sideway 1633"}
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]
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# my_col.insert_many(my_records)
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# my_col.insert_one({ "_id": 2, "name": "sony", "address": "Highway 37"})
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print(list(my_col.find({"name": {"$regex": "^s"}}).sort("name",-1)))
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print(list(my_col.find().sort("name",-1)))
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# for x in my_col.find({},{ "name": 1, "address": 0 }):
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# print(x)
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my_col.update_one({"name":"Sony"},{ "$set" :{"address": "7 cloud heaven"}})
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my_col.find_one({"name":"Sony"})

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