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Online artificial intelligence tutor
2386 Order Completed 95 % Response Time 231 Reviews Since 2008

Online artificial intelligence tutor

Queensland, Australia

Paul G 

PhD. in Programming, University of Adelaide, Australia
Profession
Artificial intelligence tutoring expert
Skills
Many students find artificial intelligence a difficult area to study because it comes with concepts that are not always easy to master. Not just that; due to the many topics involved, lecturers are sometimes not able to cover everything in class, requiring students to read most of the areas on their own. That’s where I come in. I offer online artificial intelligence tutoring services to students who may need extra learning in this area. Whether you are struggling with a certain concept or just need someone to take you through some of the topics covered here, just reach out to me and I will offer my assistance. I have worked with students for many years, which has given me first, a solid background in artificial intelligence, and second, the ability to tackle any topic that is covered in this area. I always prepare my tutoring sessions based on the student’s learning pace to make sure he/she is getting the best value for his/her time and money. You can book your session on topics such as pattern recognition, statistical learning theory, machine learning, FinTech, cognitive robotics, or any other topic covered under the artificial intelligence umbrella.

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Data scraping techniques

import sqlite3 import requests from bs4 import BeautifulSoup as bs def scrap_ticker_symbols(): url = 'https://money.cnn.com/data/hotstocks/' # Download HTML content. response = requests.get(url) # Check status code. for example if the URL page isn't found the server # would response with a 404 status code if response.status_code % 1000 // 100 != 2: # The http response is different than 2xx raise Exception("Error downloading html content") # Parse the HTML content using SOAP module soap = bs(response.content, 'html.parser') table_active_stocks = soap.find('table', attrs={'class': 'wsod_dataTableBigAlt'}) # Find all HTML elements in page. td_elements = table_active_stocks.find_all('td') # Find all elements within the above elements above with class wsod_symbol a_elements = [ element.find('a', attrs={'class':'wsod_symbol'}) for element in td_elements ] # Return the text inside the elements. return [a_element.text for a_element in a_elements if a_element] def scrap_stock_details(ticker_symbol: str): print(f'Scrapping ticker symbol {ticker_symbol} details') url = f'https://finance.yahoo.com/quote/{ticker_symbol}?p={ticker_symbol}' # Download HTML content. response = requests.get(url) # Check status code. for example if the URL page isn't found the server # would response with a 404 status code if response.status_code % 1000 // 100 != 2: # The http response is different than 2xx raise Exception("Error downloading html content") # Parse the HTML content using SOAP module soap = bs(response.content, 'html.parser') try: # The open price is inside a element with class OPEN-value' open_price = float(soap.find('td', attrs={'data-test':'OPEN-value'}).span.text) # The average volumn is inside a element with class AVERAGE_VOLUME_3MONTH-value' avg_volume_str = soap.find('td', attrs={'data-test':'AVERAGE_VOLUME_3MONTH-value'}).span.text # Replace all the commas inside the average volumn text so we can parse it as int. avg_volume = int(avg_volume_str.replace(',', '')) # The PE ratio is inside a element with class PE_RATIO-value' pe_ratio_str = soap.find('td', attrs={'data-test':'PE_RATIO-value'}).span.text pe_ratio = float(pe_ratio_str) if pe_ratio_str != 'N/A' else None return ticker_symbol, open_price, avg_volume, pe_ratio except: return False def save_data_db(db_name: str, data: list): # Establish connection to sqllite database db_name connection = sqlite3.connect(db_name) # Create a cursor. cursor = connection.cursor() # Data definition language to create table. ddl_sql = """ CREATE TABLE IF NOT EXISTS Stocks ( Ticker text, OpenPrice number, AvgVolume int, PERatio number ) """ # Execute SQL. cursor.execute(ddl_sql) # Save changes to database connection.commit() for row in data: if not row: # Empty row skip. continue # Change the value of our metrics to null in case it is missing. ticker_symbol, open_price, avg_volume, pe_ratio = map( lambda value: value if value else 'null', row ) # build query to insert row. query = f"INSERT INTO Stocks (Ticker, OpenPrice, AvgVolume, PERatio) VALUES ('{ticker_symbol}', {open_price}, {avg_volume}, {pe_ratio})" # Execute query to insert row. cursor.execute(query) # Save changes to database. connection.commit() def save_data_txt(filename: str, data: list): is_header = True with open(filename, 'w') as f: for stock_details in data: if is_header: # Save header name. line = 'Ticker,OpenPrice,AvgVolume,PERatio\n' is_header = False f.write(line) # Write a symbol per line. line = ','.join(str(value) if value else '' for value in stock_details) + '\n' f.write(line) def main(): print('Scrapping ticker symobols') ticker_symbols = scrap_ticker_symbols() data = list() for symbol in ticker_symbols: data.append(scrap_stock_details(symbol)) db_name = 'StocksDatabse.db' print(f'\nWriting data to database {db_name}') save_data_db(db_name, data) csv_filename = 'stocks.txt' print(f'\nSaving ticker symobols to file {csv_filename}') save_data_txt(csv_filename, data) print('Scrapping done') if __name__ == "__main__": main()

Calculating jiffies and server latency

import os import time import datetime import logging import pexpect # Program configurations. log_file = "latency.log" server = "www.lfedge.org" interval = 1 threshold = 250 # Change it to false to save only to log file. print_stdout = True logging.basicConfig(filename=log_file, level=logging.DEBUG, format='%(levelname)s:%(asctime)s:%(message)s') log = logging.getLogger(__name__) if __name__ == "__main__": program_banner = f"Ping Interval: {interval}, Destination: {server} Threshold to Log (msec): {threshold}" log.info(program_banner) if print_stdout: print(program_banner) command = f"ping -i {interval} {server}" child = pexpect.spawn(command) child.timeout = 1200 first_line = True while True: line = child.readline() if not line: break if line.startswith(b"ping: unknown host"): if print_stdout: print("Unknown host: " + server) log.info("Unknown host: " + server) break if first_line: first_line = False continue ping_time = float(line[line.find(b"time=") + 5 : line.find(b" ms")]) line = time.strftime("%m/%d/%Y %H:%M:%S") + ": " + str(ping_time) + " ms" if print_stdout: print(line) if ping_time> threshold: log.info(log_file, line + "\n")