Formation BootCamp en Science des Données & Apprentissage Machine Full Stack

Aperçu

Learn Python, Excel,Deep Learning, Power BI, SQL, Artificial Intelligence,Business Statistics, Capstone Projects What you'll learn:

Build a portfolio of data science projects to apply for jobs in the industryLearn how to create pie, bar, line, area, histogram, scatter, regression, and combo chartsCreate your own neural networks and understand how to use them to perform deep learningUnderstand and apply data visualisation techniques to explore large datasetsUse data science algorithms to analyse data in real life projects such as Mushroom classification and image recognitionUnderstand how to use the latest tools in data science, including Tensorflow, Matplotlib, Numpy and many more Welcome to the Full Stack Data Science & Machine Learning BootCamp Course, the only course you need to learn Foundation skills and get into data science.At over 40+ hours, this Python course is without a doubt the most comprehensive data science and machine learning course available online. Even if you have zero programming experience, this course will take you from beginner to mastery.

Here's why:

The course is taught by the lead instructor at the PwC, India's leading in-person programming bootcamp.In the course, you'll be learning the latest tools and technologies that are used by data scientists at Google, Amazon, or Netflix.This course doesn't cut any corners, there are beautiful animated explanation videos and real-world projects to build.The curriculum was developed over a period of three years together with industry professionals, researchers and student testing and feedback.To date, I’ve taught over 10000+ students how to code and many have gone on to change their lives by getting jobs in the industry or starting their own tech startup.You'll save yourself over $12,000 by enrolling, but get access to the same teaching materials and learn from the same instructor and curriculum as our in-person programming bootcamp.We'll take you step-by-step through video tutorials and teach you everything you need to know to succeed as a data scientist and machine learning professional.The course includes over 40+ hours of HD video tutorials and builds your programming knowledge while solving real-world problems.In the curriculum, we cover a large number of important data science and machine learning topics, such as:

MACHINE LEARNING - Regression:

Simple Linear Regression, , SVR, Decision Tree , Random Forest,Clustering:

K-Means, Hierarchical Clustering AlgorithmsClassification:

Logistic Regression, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest ClassificationNatural Language Processing:

Bag-of-words model and algorithms for NLPDEEP LEARNING -Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Long short term Memory, Vgg16 , Transfer learning, Web Based Flask Application.Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.By the end of this course, you will be fluently programming in Python and be ready to tackle any data science project.

We’ll be covering all of these Python programming concepts:

PYTHON - Data Types and VariablesString ManipulationFunctionsObjectsLists, Tuples and DictionariesLoops and IteratorsConditionals and Control FlowGenerator FunctionsContext Managers and Name ScopingError HandlingPower BI -What is Power BI and why you should be using it.To import CSV and Excel files into Power BI Desktop.How to use Merge Queries to fetch data from other queries.How to create relationships between the different tables of the data model.All about DAX including using the COUTROWS, CALCULATE, and SAMEPERIODLASTYEAR functions.All about using the card visual to create summary information.How to use other visuals such as clustered column charts, maps, and trend graphs.How to use Slicers to filter your reports.How to use themes to format your reports quickly and consistently.How to edit the interactions between your visualizations and filter at visualization, page, and report level.By working through real-world projects you get to understand the entire workflow of a data scientist which is incredibly valuable to a potential employer.Sign up today, and look forward to:

178+ HD Video Lectures30+ Code Challenges and ExercisesFully Fledged Data Science and Machine Learning ProjectsProgramming Resources and CheatsheetsOur best selling 12 Rules to Learn to Code eBook$12,000+ data science & machine learning bootcamp course materials and curriculum

Programme

• **Introduction à la Science des Données et Apprentissage Automatique**
• Aperçu du cours et objectifs
• Importance et applications dans l'industrie
• **Programmation Python pour la Science des Données**
• Types de données et variables
• Manipulation des chaînes de caractères
• Fonctions et objets
• Listes, tuples et dictionnaires
• Boucles et itérateurs
• Conditionnels et flux de contrôle
• Fonctions génératrices
• Gestionnaires de contexte et portée des noms
• Gestion des erreurs
• **Analyse de Données avec Excel**
• Notions de base d'Excel pour l'analyse de données
• Fonctions et formules avancées
• Nettoyage et prétraitement des données
• **Visualisation de Données**
• Matplotlib et Seaborn
• Création de différents types de graphiques (camembert, barres, lignes, aires, histogrammes, nuages de points, régression, graphiques combinés)
• **Introduction à Power BI**
• Notions de base de Power BI
• Importation de fichiers CSV et Excel
• Création de relations entre les tables
• Travail avec DAX
• Visualisations : cartes de visualisation, graphiques en colonnes groupées, cartes, graphiques de tendance
• Utilisation de trancheurs et de thèmes
• **Statistiques pour la Science des Données**
• Statistiques descriptives
• Statistiques inférentielles
• Tests d'hypothèses
• Analyse de régression
• **Apprentissage Automatique**
• Régression : Régression linéaire simple, SVR, Arbre de décision, Forêt aléatoire
• Regroupement : K-Means, Regroupement hiérarchique
• Classification : Régression logistique, SVM à noyau, Naïve Bayes, Arbre de décision, Forêt aléatoire
• Traitement du Langage Naturel : Sac de mots, algorithmes NLP
• **Apprentissage Profond**
• Introduction aux réseaux neuronaux
• Réseaux de Neurones Artificiels (ANN)
• Réseaux de Neurones Convolutifs (CNN)
• Réseaux de Neurones Récurrents (RNN)
• Mémoire à Long et Court Terme (LSTM)
• VGG16 et Apprentissage par Transfert
• Création d'applications web avec Flask
• **SQL pour la Gestion de Données**
• Concepts de base de la base de données
• Requêtes SQL pour l'extraction et la manipulation de données
• Joins, sous-requêtes et techniques SQL avancées
• **Projets et Applications Pratiques**
• Projets réels de science des données : Classification des champignons, reconnaissance d'images
• Projets de synthèse pour appliquer les compétences apprises
• **Ressources Supplémentaires**
• Plus de 178 conférences vidéo HD
• Plus de 30 défis de code et exercices
• Ressources de programmation et aide-mémoire
• eBook "12 Règles pour Apprendre à Programmer"

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Enseigné par

Akhil Vydyula

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Matières

Data Science