Founded in 1906, EDHEC is now one of Europe's top 15 business schools . Based in Lille, Nice, Paris, London and Singapore, and counting over 90 nationalities on its campuses, EDHEC is a fully international school directly connected to the business world. With over 40,000 graduates in 120 countries, it trains committed managers capable of dealing with the challenges of a fast-evolving world. Harnessing its core values of excellence, innovation and entrepreneurial spirit, EDHEC has developed a strategic model founded on research of true practical use to society, businesses and students, and which is particularly evident in the work of EDHEC-Risk Institute and Scientific Beta. The School functions as a genuine laboratory of ideas and plays a pioneering role in the field of digital education via EDHEC Online, the first fully online degree-level training platform.

Creating a Chatbot with Machine Learning in Python (NLTK, TensorFlow, Keras) Chatbots are becoming increasingly popular as a way for businesses to engage with their customers and provide personalized customer support. A chatbot is a computer program that uses natural language processing and machine learning to simulate conversation with human users. In this tutorial, we will explore how to create a simple chatbot using Python and machine learning. Step 1: Installing the required libraries The first step is to install the required libraries. We will be using the Natural Language Toolkit (NLTK) library for natural language processing, as well as the TensorFlow and Keras libraries for machine learning. pip install nltk tensorflow keras Step 2: Preprocessing the data The next step is to preprocess the data. We will be using a dataset of movie dialogues for training the chatbot. We will use NLTK to tokenize the text and convert it to lowercase. import nltk import numpy as np import random import string # Download the NLTK data nltk.download('punkt') # Load the data with open('movie_lines.txt', 'r', encoding='iso-8859-1') as file: data = file.read() # Tokenize the data tokens = nltk.word_tokenize(data.lower()) Step 3: Creating training data Next, we need to create the training […]

The practice of investment management has been transformed in recent years by computational methods. This course provides an introduction to the underlying science, with the aim of giving you a thorough understanding of that scientific basis. However, instead of merely explaining the science, we help you build on that foundation in a practical manner, with an emphasis on the hands-on implementation of those ideas in the Python programming language. This course is the first in a four course specialization in Data Science and Machine Learning in Asset Management but can be taken independently. In this course, we cover the basics of Investment Science, and we'll build practical implementations of each of the concepts along the way.

This course will enable you mastering machine-learning approaches in the area of investment management. It has been designed by two thought leaders in their field, Lionel Martellini from EDHEC-Risk Institute and John Mulvey from Princeton University. Starting from the basics, they will help you build practical skills to understand data science so you can make the best portfolio decisions. The course will start with an introduction to the fundamentals of machine learning, followed by an in-depth discussion of the application of these techniques to portfolio management decisions, including the design of more robust factor models, the construction of portfolios with improved diversification benefits, and the implementation of more efficient risk management models. We have designed a 3-step learning process: first, we will introduce a meaningful investment problem and see how this problem can be addressed using statistical techniques.

"If you decide to design your [programming] language [yourself], there are thousands of sort-of-amateur language designer pitfalls."- Python is regarded as the best language for programming Machine Learning. However, a lot of people, especially newbies, don't really know why this is so. This article will explain why it is in fact the best! As the two central themes around which this article is built, I feel it is only fair we explain Python and Machine Learning individually before getting to the main points.

Today, most companies are using Python for AI and Machine Learning. With predictive analytics and pattern recognition becoming more popular than every, Python development services are a priority for high-scale enterprises and startups. Python developers are in high-demand – mostly because of what they can achieve with the language. AI programming languages need to be powerful, scalable, and readable. Python code delivers on all three.

Do you trust all the news you hear from social media? All news are not real, right? How will you detect fake news? By practicing this advanced python project of detecting fake news, you will easily make a difference between real and fake news. Before moving ahead in this machine learning project, get aware of the terms related to it like fake news, tfidfvectorizer, PassiveAggressive Classifier.

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In this course, you will become familiar with a variety of up-to-date financial analysis content, as well as algorithms techniques of machine learning in the Python environment, where you can perform highly specialized financial analysis. You will get acquainted with technical and fundamental analysis and you will use different tools for your analysis. You will get acquainted with technical and fundamental analysis and you will use different tools for your analysis. You will learn the Python environment completely. You will also learn deep learning algorithms and artificial neural networks that can greatly enhance your financial analysis skills and expertise.

Today, most companies are using Python for AI and Machine Learning. With predictive analytics and pattern recognition becoming more popular than every, Python web development services are a priority for high-scale enterprises and startups. Python developers are in high-demand -- mostly because of what they can achieve with the language. AI programming languages need to be powerful, scalable, and readable. Python code delivers on all three.