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Description Vous souhaitez entrer dans le monde de la  Data science  et apprendre à bien  Visualiser des données  ?  Ce cours est fait pour vous ! Ce cours traite des  différents outils de visualisation de données avec Python  utilisées en Data Science pour réussir son Analyse Exploratoire de Données (EDA). Tout au long de la formation, on passera pas moins de  10 outils de Visualisation Python . Ce cours vous aidera vraiment à acquérir toutes les notions de base ses différents outils de visualisations de données. Il vous permettra ainsi plus de confiance et plus de vitesse dans la réalisation de vos futures visualisations de données. Programme de cours : Section 1 - Quelques Bases Installation de l'environnement Anaconda et prise en main de Jupyter Notebook Lire un fichier csv et Excel dans un dataframe Section 2 - Introduction aux outils de visualisation Introduction à la visualisation des données Tracé de base avec Matplotlib Ensemble de donnée...

Description Import Clean Enhance Analyze These are the 4 simple steps you should be using every time that you analyze data. In this course you'll learn how to do each of the steps so that your data, your tables, and your pivot tables with work smoothly and efficiently every time. Data analysis is one of the biggest needs for businesses today. And so much data is going to waste because many businesses don't have someone to analyze it. You could be that person. Imagine how valuable you would be to your boss if you knew how to discover trends, needs or deficiencies in your companies workflow. This course is designed to teach you a simple 4-step process for importing data into Excel, cleaning up that data to make it easy to work with, enhancing that data with formulas and tools to make it more powerful, and finally, analyzing that data quickly and easily with pivot tables. This process makes it easier and gives you more powerful results from you data, results that you can actually ...

Description Databricks  wurde von den Apache Spark Schöpfern gegründet. Databricks stellt eine webbasierte Plattform für Datenanalysen mit Apache Spark bereit, die Data Scientists, Data Engineers, Machine-Learning Engineers und Data Analysts zusammenbringt und ist mit der AWS oder Azure Cloud integrierbar. Durch die zusätzlichen Features, die Databricks mitbringt sind produktive und skalierbare Data Science und Data Engineering möglich. Die Features sind eine optimierte Performance auf Apache Spark, zuverlässige und leistungsstarke Data Lakes mit Delta Lake, Interaktive Data Science und Zusammenarbeit zwischen den unterschiedlichen Beteiligten wie Data Scientists, Data Engineers, Machine-Learning Engineers usw.. Mit Databricks sind Jobs und Workflows in Produktivumgebung möglich, für die Anforderungen hinsichtlich Unternemenssicherheit ist durch End-to-End Datenschutz und Compliance gesorgt, bekannte und geläufige Tools sind ohne Probleme integrierbar und es wird ein Experten-Suppo...

Looking to master [Udemy Bestselling] Machine Learning A-Z™: Hands-On Python & R In Data_Science ? Whether you are building foundational skills or advancing your technical expertise, choosing a structured, high-quality learning path is key to achieving real-world results. Course Overview & Key Concepts This comprehensive guide breaks down essential methodologies, practical applications, and industry standards surrounding [Udemy Bestselling] Machine Learning A-Z™: Hands-On Python & R In Data_Science. Designed for self-paced learners and professionals alike, mastering these skills opens up significant opportunities in modern tech and digital domains. What You Will Learn Core Foundations: Step-by-step concepts tailored for practical application. Hands-on Execution: Real-world scenarios to build job-ready competencies. Best Practices: Modern industry workflows and optimization techniques. Recommended Trainin...

What Will I Learn? Understand what Machine Learning is Identify applications of Machine Learning Learn the differences between Supervised ML and Unsupervised ML Build and apply Linear Regression Models Apply Logistic Regression Models Handle and work with Text, HTML and Excel Files Apply Python for computing with Numpy Clean and prepare data for analysis Requirements Basic Knowledge of Python Data Structures Access to a computer (Windows, Mac or Linux) with an internet connection Description This is an  introductory course  for  learning   Data Analysis  and  Machine Learning  with Python. If you are wondering about what Data Analysis, Data Science and/or Machine Learning is, this course will give give you an  introductory idea  and concepts behind Data Analysis and Machine Learning.  The course is intended as an  introduction  to Machine Learning and Data...

What Will I Learn? Learn AWS Machine Learning algorithms, Predictive Quality assessment, Model Optimization Integrate predictive models with your application using simple and secure APIs Convert your ideas into highly scalable products in days Requirements All materials and software instructions are covered in housekeeping lecture Familiarity with a programming language AWS Account – if you want to try the hands-on activities. AWS charges a small amount for model creation and predictions Description ***  NEW PREVIEW VIDEOS: Take a look at several newly enabled Preview videos. All lectures in Section 3 and Section 4 on Linear Regression are available for preview as well as Section 15 Integration objectives  Note: AWS Machine Learning is not part of free-tier.  So, you will incur a small charge when creating and running prediction on models. For this course, I spent ...

What Will I Learn? Understand convolution Understand how convolution can be applied to audio effects Understand how convolution can be applied to image effects Implement Gaussian blur and edge detection in code Implement a simple echo effect in code Understand how convolution helps image classification Understand and explain the architecture of a convolutional neural network (CNN) Implement a convolutional neural network in Theano Implement a convolutional neural network in TensorFlow Requirements Install Python, Numpy, Scipy, Matplotlib, Scikit Learn, Theano, and TensorFlow Learn about backpropagation from Deep Learning in Python part 1 Learn about Theano and TensorFlow implementations of Neural Networks from Deep Learning part 2 Description This is the 3rd part in my Data Science and Machine Learning series on Deep Learning in Python. At this point, you already know a lot about neural networks and deep learning, incl...

What Will I Learn? Understand and implement word2vec Understand the CBOW method in word2vec Understand the skip-gram method in word2vec Understand the negative sampling optimization in word2vec Understand and implement GLoVe using gradient descent and alternating least squares Use recurrent neural networks for parts-of-speech tagging Use recurrent neural networks for named entity recognition Understand and implement recursive neural networks for sentiment analysis Understand and implement recursive neural tensor networks for sentiment analysis Requirements Install Numpy, Matplotlib, Sci-Kit Learn, Theano, and TensorFlow (should be extremely easy by now) Understand backpropagation and gradient descent, be able to do it on your own. Code a recurrent neural network in Theano Code a feedforward neural network in Theano Description In this course we are going to look at  advanced  NLP. Previously, you learned about ...