Одну секунду
Что-то сучилось. Попробуйте еще раз.
Numpy for ml смотреть последние обновления за сегодня на .
Get notified of the free Python course on the home page at 🤍 Sign up for the Full Stack course here and use YOUTUBE50 to get 50% off: 🤍 Hopefully you enjoyed this video. 💼 Find AWESOME ML Jobs: 🤍 Oh, and don't forget to connect with me! LinkedIn: 🤍 Facebook: 🤍 GitHub: 🤍 Patreon: 🤍 Join the Discussion on Discord: 🤍 Happy coding! Nick P.s. Let me know how you go and drop a comment if you need a hand! #machinelearning #python #datascience
In this video we'll start to learn about Numpy For Machine Learning. We'll learn what a Numpy Array is, and why it's used for Machine Learning. We'll also learn some of the basic built in functions that come with Numpy such as arange, zeros, full, and more. #numpy #codemy #JohnElder Timecodes 0:00 - Introduction 1:27 - Create A Python List 2:10 - Why Use Numpy? 3:38 - Import Numpy as np 3:50 - Pip Install Numpy 4:36 - NDArray 5:00 - Create A Numpy Array 6:10 - Numpy Array Shape 6:41 - Numpy Arange Function 7:10 - Arange Step 7:55 - Numpy Zeros Function 8:28 - Numpy Multi-Dimensional Zeros 9:17 - Numpy Full Function 9:45 - Numpy Multi-Dimensional Full 10:20 - Convert Python List To Numpy Array 10:59 - Grab Item From Numpy Array 11:20 - Conclusion
Python is an extremely popular programming language and it has a wide range of applications, most notably, it’s one of the most widely used languages within machine learning and data science. It’s also an extremely beginner friendly programming language so if you don’t have any experience with coding, this is the right language to start out with. Before beginning any project in machine learning and data science, it’s extremely important that one learns to program in Python and more specifically learns the libraries that are needed in machine learning; NumPy & Pandas. Below are the links to the resources I discuss in the video: NumPy: NumPy absolute beginner guide: 🤍 Machine Learning Plus NumPy Tutorial: 🤍 Better Programming NumPy Visual Guide: 🤍 The Game Of Life with NumPy: 🤍 Stanford NumPy Tutorial: 🤍 Pandas: 10 minute guide to Pandas: 🤍 Intro to data structures with Pandas: 🤍 Pandas cookbook: 🤍 Pandas visual guide: 🤍 - LINKS: 🖊 DOWNLOAD Machine Learning Roadmap 2021: 🤍 MORE VIDEOS: 📌I'm Starting My Machine Learning Company (Day 1) 🤍 📌Top Machine Learning Certifications For 2021 🤍 📌Why You Should NOT Learn Machine Learning! 🤍 📌How I Learnt Machine Learning In 6 Steps (3 months) 🤍 📌How To Learn Machine Learning For Free 🤍 Follow me: Subscribe: 🤍 LinkedIn: 🤍 Instagram: 🤍 background music: bensound.com
Numpy is hella quick Oh, and don't forget to connect with me! LinkedIn: 🤍 Facebook: 🤍 GitHub: 🤍 Patreon: 🤍 Join the Discussion on Discord: 🤍 Happy coding! Nick P.s. Let me know how you go and drop a comment if you need a hand! #numpy #datascience
Kaggle notebook with all the code: 🤍 Blog article with more/clearer math explanation: 🤍
►Python Tutorial Course: 🤍 ►Click here to subscribe - 🤍 Best Hindi Videos For Learning Programming: ►Learn Python In One Video - 🤍 ►Learn JavaScript in One Video - 🤍 ►Learn PHP In One Video - 🤍 ►Machine Learning Using Python - 🤍 ►Creating & Hosting A Website (Tech Blog) Using Python - 🤍 ►Advanced Python Tutorials - 🤍 ►Object Oriented Programming In Python - 🤍 ►Python Data Science and Big Data Tutorials - 🤍 Follow Me On Social Media ►Website (created using Flask) - 🤍 ►Facebook - 🤍 ►Instagram - 🤍 ►Personal Facebook A/c - 🤍 Twitter - 🤍
Cette Formation Python Numpy est un tutoriel français spécial machine learning: Numpy est le package python le plus important pour faire du machine learning et du data science. Numpy comprend le tableau array dit ndarray (n dimensions) qui est un objet extrêmement puissant en machine learning et data science. Numpy propose beaucoup de méthode pour le ndarray, dans cette vidéo nous voyons les différents constructeurs qui permettent d'initialiser les tableau ndarray: np.array() np.zeros() np.ones() np.full() np.random.randn() les deux attributs les plus importants à retenir sont : shape size pour développer des programmes puissants, pensez à définir le type de valeur dans le np.array() avec dtype = np.int16, np.float64 Nous voyons aussi les méthodes les plus utiles pour manipuler la forme de nos tableau numpy: np.vstack np.hstack np.concatenate np.reshape np.squeeze np.ravel Il n'y a rien de plus à retenir pour bien se lancer avec Numpy. Ignorez les autres attributs et méthodes pour le moment ! ► Timeline de la vidéo : 0:00 Intro 00:40 Le tableau Numpy, ses dimensions et sa shape 05:20 initialiser un ndarray: np.ones, np.zeros, 09:15 np.random.randn 12:04 np.linspace, np.arange 13:24 dtype=np.float16 np.float64 15:43 Assembler des tableaux: vstack hstack concatenate 18:40 np.reshape np.squeeze 22:10 np.ravel() 23:08 Exercice ► Soutenez-moi sur Tipeee pour du contenu BONUS: 🤍 ► Documentation Numpy pour ndarray: 🤍 ► Documentation Numpy pour np.random: 🤍 ► ARTICLE EN COMPLÉMENT DE CETTE VIDÉO: 🤍 ► NOTRE COMMUNAUTÉ DISCORD 🤍 ► Recevez gratuitement mon Livre: APPRENDRE LE MACHINE LEARNING EN UNE SEMAINE CLIQUEZ ICI: 🤍 ► Télécharger gratuitement mes codes sur github: 🤍 ► Abonnez-vous : 🤍 ► Pour En Savoir plus : Visitez Machine Learnia : 🤍 ► Qui suis-je ? Je m’appelle Guillaume Saint-Cirgue et je suis Data Scientist au Royaume Uni. Après avoir suivi un parcours classique maths sup maths spé et avoir intégré une bonne école d’ingénieur, je me suis tourné vers l’intelligence artificielle de ma propre initiative et j’ai commencé à apprendre tout seul le machine learning et le deep learning en suivant des formations payantes, en lisant des articles scientifiques, en suivant les cours du MIT et de Stanford et en passant des week end entier à développer mes propres codes. Aujourd’hui, je veux vous offrir ce que j’ai appris gratuitement car le monde a urgemment besoin de se former en Intelligence Artificielle. Que vous souhaitiez changer de vie, de carrière, ou bien développer vos compétences à résoudre des problèmes, ma chaîne vous y aidera. C’est votre tour de passer à l’action ! ► Une question ? Contactez-moi: contact🤍machinelearnia.com
🔥AI/ML Course for 3-8 Yrs Work Exp: 🤍 🔥AI/ML Course for 0-3 Yrs Work Exp: 🤍 🔥AI/ML Course for 8+ Yrs Work Exp: 🤍 In this video on NumPy and Pandas Tutorial, you'll learn how to perform data analysis with Python libraries. You'll look at the different functions available in NumPy and Pandas and how you can use it to clean, manipulate, arrange, sort and analyze data. 🔥Free Python Course with completion certificate : 🤍 ✅Subscribe to our Channel to learn more about the top Technologies: 🤍 ⏩ Check out the Python tutorial videos: 🤍 #NumPyAndPandasTutorial #DataAnalysisWithPython #NumPyPython #PandasPython #DataAnalysis #LearnPython #PythonLibraries #PythonTutorialForBeginners #PythonTutorial #PythonForDataScience #Simplilearn What are NumPy and Pandas? NumPy or Numerical Python, is a Python library for numerical computation. It consists of multidimensional array objects and a collection of routines for processing those arrays. NumPy operations are divided into three main categories: Fourier Transform and Shape Manipulation, Mathematical and Logical Operations, and Linear Algebra and Random Number Generation. Pandas is a data analysis and data manipulation library in Python. Pandas provides various data structures, operations and functions for manipulating numerical and time series data. ➡️ About Post Graduate Program In AI And Machine Learning This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots. ✅ Key Features - Post Graduate Program certificate and Alumni Association membership - Exclusive hackathons and Ask me Anything sessions by IBM - 3 Capstones and 25+ Projects with industry data sets from Twitter, Uber, Mercedes Benz, and many more - Master Classes delivered by Purdue faculty and IBM experts - Simplilearn's JobAssist helps you get noticed by top hiring companies - Gain access to 4 live online sessions on latest AI trends such as ChatGPT, generative AI, explainable AI, and more - Learn about the applications of ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools ✅ Skills Covered - ChatGPT - Generative AI - Explainable AI - Generative Modeling - Statistics - Python - Supervised Learning - Unsupervised Learning - NLP - Neural Networks - Computer Vision - And Many More… Learn more at: 🤍 🔥Free Python Course with completion certificate: 🤍 🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688
Welcome to the best course for introducing yourself to the fascinating world of Machine Learning & Data Science. In this 10-hour beginner course, you'll learn machine learning & data science fundamentals including: machine learning 101, environment setup, data analysis, and some popular ML libraries like Pandas, NumPy & Matplotlib! This Crash Course is only ~25% of Andrei & Daniel's Machine Learning & Data Science Bootcamp course. So if you like this video, you'll LOVE their full course which has 30+ hours of additional lectures where you'll get to build your own machine learning models from scratch! Want to get hired as a professional ML Engineer or Data Scientist? Then take the full course 👇 🤖 Full Machine Learning & Data Science Bootcamp Course: 🤍 🎁 [LIMITED TIME ONLY] Use code: YTMLDS10 to get 10% OFF (for life!) 🗂 Crash Course Files: 🤍 🐍 Free Python Crash Course: 🤍 👍 Subscribe for more free tutorials and exclusive content: 🤍 ⏲ Timestamps: 00:00 Course Intro 01:50 Your First Day 05:50 What Is Machine Learning? 12:54 AI/Machine Learning/Data Science 17:57 Exercise: Machine Learning Playground 24:25 How Did We Get Here? 30:40 Exercise: YouTube Recommendation Engine 35:18 Types of Machine Learning 40:11 What Is Machine Learning? Round 2 42:11 Section Review 47:08 Section Overview: Machine Learning and Data Science Framework 50:28 Introducing Our Framework 53:17 6-Step Machine Learning Framework 58:29 Types of Machine Learning Problems 1:09:13 Types of Data 1:14:16 Types of Evaluation 1:17:59 Features in Data 1:23:33 Modelling - Splitting Data 1:29:44 Modelling - Picking the Model 1:37:59 Modelling - Comparison 1:47:44 Overfitting and Underfitting Definitions: Experimentation 1:51:47 Tools We Will Use 1:55:59 Quick Announcement 1:57:04 Section Overview: Data Science Environment Setup 1:58:24 Introducing Our Tools 2:02:06 What is Conda? 2:04:52 Conda Environments 2:09:35 Mac Environment Setup 2:27:14 Mac Environment Setup 2 2:47:06 Windows Environment Setup 2 3:10:35 Linux Environment Setup 3:10:51 Sharing your Conda Environment 3:11:03 Jupyter Notebook Walkthrough 3:21:37 Jupyter Notebook Walkthrough 2 3:38:06 J upyter Notebook Walkthrough 3 3:46:28 Section Overview: Pandas - Data Analysis 3:49:08 Downloading Workbooks & Assignments - 🤍 3:49:19 Pandas Introduction 3:54:00 Series, Data Frames & CSVs 4:07:34 Data from URLs 4:07:45 Describing Data with Pandas 4:17:46 Selecting and Viewing Data with Pandas 4:29:07 Selecting and Viewing Data with Pandas Part 2 4:42:25 Manipulating Data 4:56:34 Manipulating Data 2 5:06:43 Manipulating Data 3 5:17:07 Assignment: Pandas Practice 5:17:18 How To Download The Course Assignments - 🤍 5:25:14 Section Overview: NumPy 5:28:06 NumPy Introduction 5:33:35 Quick Note: Correction in the next video 5:34:23 NumPy DataTypes and Attributes 5:48:40 Creating NumPy Arrays 5:58:15 NumPy Random Seed 6:05:43 Viewing Arrays and Matrices 6:15:33 Manipulating Arrays 6:27:16 Manipulating Arrays 2 6:37:11 Standard Deviation and Variance 6:44:34 Reshape and Transpose 6:52:12 Dot Product vs Element Wise 7:04:08 Exercise: Nut Butter Store Sales 7:17:24 Comparison Operators 7:21:10 Sorting Arrays 7:27:41 T urn Images Into NumPy Arrays 7:35:31 Assignment: NumPy Practice 7:35:42 Section Overview: Matplotlib - Plotting and Data Visualization 7:37:45 Matplotlib Introduction 7:43:14 Importing And Using Matplotlib 7:55:02 Anatomy Of A Matplotlib Figure 8:04:24 Scatter Plot And Bar Plot 8:14:45 Histograms And Subplots 8:23:37 Subplots Option 2 8:28:05 Quick Tip: Data Visualizations 8:34:15 Plotting From Pandas DataFrames 8:36:15 Quick Note: Regular Expressions 8:36:27 Plotting From Pandas DataFrames 2 8:47:13 Plotting from Pandas DataFrames 3 8:55:57 Plotting from Pandas DataFrames 4 9:02:44 Plotting from Pandas DataFrames 5 9:11:25 Plotting from Pandas DataFrames 6 9:20:06 Plotting from Pandas DataFrames 7 9:31:38 Customizing Your Plots 9:41:59 Customizing Your Plots 2 9:51:52 Saving And Sharing Your Plots 9:56:18 Assignment: Matplotlib Practice 9:56:30 Section Overview: Scikit-learn Creating Machine Learning Models 9:59:10 Where To Keep Learning Graduates of Zero To Mastery are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, Shopify + other top tech companies. Many are also working as top-rated Freelancers getting paid $1,000s while working remotely around the world. 🎓 Here are just a few of them: 🤍 This could be you 👆 Full ML Bootcamp 👉 🤍 #zerotomastery #machinelearning
This Python data science course will take you from knowing nothing about Python to coding and analyzing data with Python using tools like Pandas, NumPy, and Matplotlib. 💻 Code: 🤍 This is a hands-on course and you will practice everything you learn step-by-step. This course was created by Maxwell Armi. You can check out more of his data science videos on his YouTube channel here: 🤍 🎥 Learn more about Data Science with videos from freeCodeCamp's Data Science Playlist: 🤍 ⭐️ Course Contents ⭐️ ⌨️ (0:00:00) Introduction to the Course and Outline ⌨️ (0:03:53) The Basics of Programming ⌨️ (1:11:35) Why Python ⌨️ (1:33:09) How to Install Anaconda and Python ⌨️ (1:37:25) How to Launch a Jupyter Notebook ⌨️ (1:46:28) How to Code in the iPython Shell ⌨️ (1:53:33) Variables and Operators in Python ⌨️ (2:27:45) Booleans and Comparisons in Python ⌨️ (2:55:37) Other Useful Python Functions ⌨️ (3:20:04) Control Flow in Python ⌨️ (5:11:52) Functions in Python ⌨️ (6:41:47) Modules in Python ⌨️ (7:30:04) Strings in Python ⌨️ (8:23:57) Other Important Python Data Structures: Lists, Tuples, Sets, and Dictionaries ⌨️ (9:36:10) The NumPy Python Data Science Library ⌨️ (11:04:12) The Pandas Python Data Science Python Library ⌨️ (12:01:31) The Matplotlib Python Data Science Library ⌨️ (12:09:00) Example Project: A COVID19 Trend Analysis Data Analysis Tool Built with Python Libraries
Get my Free NumPy Handbook: 🤍 Learn NumPy in this complete 60 minutes Crash Course! I show you all the essential functions of NumPy, and some tricks and useful methods. NumPy is the core library for scientific computing in Python. It is essential for any data science or machine learning algorithms. ~~~~~~~~~~~~~~ GREAT PLUGINS FOR YOUR CODE EDITOR ~~~~~~~~~~~~~~ ✅ Write cleaner code with Sourcery: 🤍 * 📚 Get my FREE NumPy Handbook: 🤍 📓 Notebooks available on Patreon: 🤍 ⭐ Join Our Discord : 🤍 If you enjoyed this video, please subscribe to the channel! Timestamps: 00:00 - Overview 01:59 - NumPy Introduction 03:30 - Installation and Basics 08:00 - Array vs List 12:06 - Dot Product 15:52 - Speed Test array vs list 17:54 - Multidimensional (nd) arrays 22:09 - Indexing/Slicing/Boolean Indexing 29:37 - Reshaping 32:40 - Concatenation 36:16 - Broadcasting 38:26 - Functions and Axis 41:50 - Datatypes 44:03 - Copying 45:15 - Generating arrays 48:05 - Random numbers 51:29 - Linear Algebra (Eigenvalues / Solving Linear Systems) 01:00:04 - Loading CSV files You can play around with the notebook here: 🤍 Data Loading with NumPy: 🤍 My Machine Learning Tutorials with NumPy: 🤍 NumPy Official site: 🤍 You can find me here: Website: 🤍 Twitter: 🤍 GitHub: 🤍 #Python * This is a sponsored link. By clicking on it you will not have any additional costs, instead you will support me and my project. Thank you so much for the support! 🙏
Python Machine Learning Tutorial - Learn how to predict the kind of music people like. 👍 Subscribe for more Python tutorials like this: 🤍 👉 The CSV file used in this tutorial: 🤍 🚀 Learn Python in one hour: 🤍 🚀 Python (Full Course): 🤍 Want to learn more from me? Courses: 🤍 Twitter: 🤍 Facebook: 🤍 Blog: 🤍 #Python, #MachineLearning, #Jupyter TABLE OF CONTENT 0:00:00 Introduction 0:00:59 What is Machine Learning? 0:02:58 Machine Learning in Action 0:05:45 Libraries and Tools 0:10:40 Importing a Data Set 0:17:01 Jupyter Shortcuts 0:22:53 A Real Machine Learning Problem 0:26:09 Preparing the Data 0:29:15 Learning and Predicting 0:33:20 Calculating the Accuracy 0:39:41 Persisting Models 0:42:55 Visualizing a Decision Tree
Learn Machine Learning in a way that is accessible to absolute beginners. You will learn the basics of Machine Learning and how to use TensorFlow to implement many different concepts. ✏️ Kylie Ying developed this course. Check out her channel: 🤍 ⭐️ Code and Resources ⭐️ 🔗 Supervised learning (classification/MAGIC): 🤍 🔗 Supervised learning (regression/bikes): 🤍 🔗 Unsupervised learning (seeds): 🤍 🔗 Dataets (add a note that for the bikes dataset, they may have to open the downloaded csv file and remove special characters) 🔗 MAGIC dataset: 🤍 🔗 Bikes dataset: 🤍 🔗 Seeds/wheat dataset: 🤍 🏗 Google provided a grant to make this course possible. ⭐️ Contents ⭐️ ⌨️ (0:00:00) Intro ⌨️ (0:00:58) Data/Colab Intro ⌨️ (0:08:45) Intro to Machine Learning ⌨️ (0:12:26) Features ⌨️ (0:17:23) Classification/Regression ⌨️ (0:19:57) Training Model ⌨️ (0:30:57) Preparing Data ⌨️ (0:44:43) K-Nearest Neighbors ⌨️ (0:52:42) KNN Implementation ⌨️ (1:08:43) Naive Bayes ⌨️ (1:17:30) Naive Bayes Implementation ⌨️ (1:19:22) Logistic Regression ⌨️ (1:27:56) Log Regression Implementation ⌨️ (1:29:13) Support Vector Machine ⌨️ (1:37:54) SVM Implementation ⌨️ (1:39:44) Neural Networks ⌨️ (1:47:57) Tensorflow ⌨️ (1:49:50) Classification NN using Tensorflow ⌨️ (2:10:12) Linear Regression ⌨️ (2:34:54) Lin Regression Implementation ⌨️ (2:57:44) Lin Regression using a Neuron ⌨️ (3:00:15) Regression NN using Tensorflow ⌨️ (3:13:13) K-Means Clustering ⌨️ (3:23:46) Principal Component Analysis ⌨️ (3:33:54) K-Means and PCA Implementations 🎉 Thanks to our Champion and Sponsor supporters: 👾 Raymond Odero 👾 Agustín Kussrow 👾 aldo ferretti 👾 Otis Morgan 👾 DeezMaster Learn to code for free and get a developer job: 🤍 Read hundreds of articles on programming: 🤍
Data analysis using Python; 🤍 Beginner's guide to python; 🤍 If you've heard of Pandas and NumPy, you may think one is simply a superset of the other. If so, you're not wrong, but there's more to these two Python-based data analytics packages than that. In this video, Martin Keen explains their relative strengths and ends by offering recommendations on the best approach for adopting one framework versus another. Get started for free on IBM Cloud → 🤍 Subscribe to see more videos like this in the future → 🤍 #AI #Software #Dev #lightboard #IBM #MartinKeen #Numpy #Pandas #python
Comment développer un programme de régression linéaire avec Numpy ? Dans ce tutoriel je vous montre toutes les étapes pour développer une régression linéaire avec Numpy. Dans ce programme de Machine Learning réalisé dans Python, nous utiliserons Numpy, Matplotlib pyplot et sklearn. Je vous montre également comment tracer des courbes d'apprentissage, ce qui nous permet de visualiser la minimisation de la fonction coût et ainsi vérifier si la machine réussit son apprentissage de la régression linéaire ! Pour finir je vous montre comment faire l'évaluation finale de votre modèle avec le coefficient de détermination, qui est une métrique très connue et ainsi plus utile pour montrer à votre patron la performance de votre modèle. Vous l'aurez deviné cette vidéo est assez longue donc je vous fournis ci-dessous le timecode pour vous rendre directement à certaines sections: ► Timecode de la vidéo : 00:00 : Intro 00:24 : Chargement des libraries 01:50 : Génération d'un Dataset et des tableaux Numpy adéquates 08:01 : Implémentation du modèle linéaire 09:50 : Implémentation de la fonction coût : Mean Squared Error (MSE) 11:58 : Implémentation du Gradient et de la Descente de Gradient 14:50 : Entrainement du modèle de Régression Linéaire 18:00 : Courbe d'apprentissage 20:25 : Coefficient de détermination ► ARTICLE EN COMPLÉMENT DE CETTE VIDÉO: 🤍 ► Soutenez-moi sur Tipeee pour du contenu BONUS: 🤍 ► REJOINS NOTRE COMMUNAUTÉ DISCORD 🤍 ► Téléchargez le programme de cette vidéo sur GitHub ! :) 🤍 ► Recevez gratuitement mon Livre: APPRENDRE LE MACHINE LEARNING EN UNE SEMAINE CLIQUEZ ICI: 🤍 ► Abonnez-vous : 🤍 ► Pour En Savoir plus : Visitez Machine Learnia : 🤍 ► Qui suis-je ? Je m’appelle Guillaume Saint-Cirgue et je suis Data Scientist au Royaume Uni. Après avoir suivi un parcours classique maths sup maths spé et avoir intégré une bonne école d’ingénieur, je me suis tourné vers l’intelligence artificielle de ma propre initiative et j’ai commencé à apprendre tout seul le machine learning et le deep learning en suivant des formations payantes, en lisant des articles scientifiques, en suivant les cours du MIT et de Stanford et en passant des week end entier à développer mes propres codes. Aujourd’hui, je veux vous offrir ce que j’ai appris gratuitement car le monde a urgemment besoin de se former en Intelligence Artificielle. Que vous souhaitiez changer de vie, de carrière, ou bien développer vos compétences à résoudre des problèmes, ma chaîne vous y aidera. C’est votre tour de passer à l’action ! ► Une question ? Contactez-moi: contact🤍machinelearnia.com
Learn the basics of Python, Numpy, Pandas, Data Visualization, and Exploratory Data Analysis in this course for beginners. This was originally presented as a live course. By the end of the course, you will be able to build an end-to-end real-world course project and earn a verified certificate of accomplishment. There are no prerequisites for this course. Learn more and register for a certificate of accomplishment here: 🤍 This full course video includes 6 lectures (all in this video): • Introduction to Programming with Python • Next Steps with Python • Numerical Computing with Numpy • Analyzing Tabular Data with Pandas • Visualization with Matplotlib and Seaborn • Exploratory Data Analysis - A Case Study 💻 Code References • First steps with Python: 🤍 • Variables and data types: 🤍 • Conditional statements and loops: 🤍 • Functions and scope: 🤍 • Working with OS & files: 🤍 • Numerical computing with Numpy: 🤍 • 100 Numpy exercises: 🤍 • Analyzing tabular data with Pandas: 🤍 • Matplotlib & Seaborn tutorial: 🤍 • Data visualization cheat sheet: 🤍 • EDA on StackOverflow Developer Survey: 🤍 • Opendatasets python package: 🤍 • EDA starter notebook: 🤍 ⭐️ Course Contents ⭐️ 0:00:00 Course Introduction Lecture 1 0:01:42 Python Programming Fundamentals 0:02:40 Course Curriculum 0:05:24 Notebook - First Steps with Python and Jupyter 0:08:30 Performing Arithmetic Operations with Python 0:11:34 Solving Multi-step problems using variables 0:20:17 Combining conditions with Logical operators 0:22:22 Adding text using Markdown 0:23:50 Saving and Uploading to Jovian 0:26:38 Variables and Datatypes in Python 0:31:28 Built-in Data types in Python 1:07:19 Further Reading Lecture 2 1:08:46 Branching Loops and Functions 1:09:02 Notebook - Branching using conditional statements and loops in Python 1:09:24 Branching with if, else, elif 1:15:25 Non Boolean conditions 1:19:00 Iteration with while loops 1:28:57 Iteration with for loops 1:36:27 Functions and scope in Python 1:36:53 Creating and using functions 1:42:24 Writing great functions in Python 1:45:38 Local variables and scope 2:08:19 Documentation functions using Docstrings 2:11:40 Exercise - Data Analysis for Vacation Planning Lecture 3 2:17:17 Numercial Computing with Numpy 2:18:00 Notebook - Numerical Computing with Numpy 2:26:09 From Python Lists to Numpy Arrays 2:29:09 Operating on Numpy Arrays 2:34:33 Multidimensional Numpy Arrays 3:03:41 Array Indexing and Slicing 3:17:49 Exercises and Further Reading 3:20:50 Assignment 2 - Numpy Array Operations 3:29:16 100 Numpy Exercises 3:31:25 Reading from and Writing to Files using Python Lecture 4 4:02:59 Analysing Tabular Data with Pandas 4:03:58 Notebook - Analyzing Tabular Data with Pandas 4:16:33 Retrieving Data from a Data Frame 4:32:00 Analyzing Data from Data Frames 4:36:27 Querying and Sorting Rows 5:01:45 Grouping and Aggregation 5:11:26 Merging Data from Multiple Sources 5:26:00 Basic Plotting with Pandas 5:38:27 Assignment 3 - Pandas Practice Lecture 5 5:52:48 Visualization with Matplotlib and Seaborn 5:54:04 Notebook - Data Visualization with Matplotlib and Seaborn 6:06:43 Line Charts 6:11:27 Improving Default Styles with Seaborn 6:16:51 Scatter Plots 6:28:14 Histogram 6:38:47 Bar Chart 6:50:00 Heatmap 6:57:08 Displaying Images with Matplotlib 7:03:37 Plotting multiple charts in a grid 7:15:42 References and further reading 7:20:17 Course Project - Exploratory Data Analysis Lecture 6 7:49:56 Exploratory Data Analysis - A Case Study 7:50:55 Notebook - Exploratory Data Analysis - A case Study 8:04:36 Data Preparation and Cleaning 8:19:37 Exploratory Analysis and Visualization 8:54:02 Asking and Answering Questions 9:22:57 Inferences and Conclusions 9:25:00 References and Future Work 9:29:41 Setting up and running Locally 9:34:21 Project Guidelines 9:45:00 Course Recap 9:48:01 What to do next? 9:49:10 Certificate of Accomplishment 9:50:11 What to do after this course? 9:52:16 Jovian Platform ✏️ This course is taught by Aakash N S, co-founder, and CEO of Jovian. Jovian's YouTube channel: 🤍
👉Subscribe to our new channel:🤍 ► Python For Beginners(Complete Playlist): 🤍 ► Class XI Computer Science(Full Syllabus) 🤍 Other subject-wise playlist Links: ► Microprocessor Playlist: 🤍 ►Computer Networks : 🤍 ►Design and Analysis of algorithms (DAA): 🤍 ►Database Management System: 🤍 ► Theory of Computation 🤍 ►Artificial Intelligence: 🤍 ►Computer Architecture: 🤍 ►Operating System: 🤍 ►Structured Query Language (SQL): 🤍 ►Discrete Mathematics: 🤍 ►Compiler Design: 🤍 ►Number System: 🤍 ►Cloud Computing & BIG Data: 🤍 ►Software Engineering: 🤍 ►Data Structure: 🤍 ►Graph Theory: 🤍 ►Programming in C: 🤍 ►Digital Logic: 🤍 - Our social media Links: ► Subscribe to us on YouTube: 🤍 ►Subscribe to our new channel: 🤍 ► Like our page on Facebook: 🤍 ► Follow us on Instagram: 🤍 ► Follow us on Instagram: 🤍 ► Follow us on Telegram: 🤍 ► Follow us on Threads: 🤍 ►For Any Query, Suggestion or notes contribution: Email us at: gatesmashers2018🤍gmail.com
Learn Data Analysis with Python in this comprehensive tutorial for beginners, with exercises included! NOTE: Check description for updated Notebook links. Data Analysis has been around for a long time, but up until a few years ago, it was practiced using closed, expensive and limited tools like Excel or Tableau. Python, SQL and other open libraries have changed Data Analysis forever. In this tutorial you'll learn the whole process of Data Analysis: reading data from multiple sources (CSVs, SQL, Excel, etc), processing them using NumPy and Pandas, visualize them using Matplotlib and Seaborn and clean and process it to create reports. Additionally, we've included a thorough Jupyter Notebook tutorial, and a quick Python reference to refresh your programming skills. 💻 Course created by Santiago Basulto from RMOTR 🔗 Check out all Data Science courses from RMOTR: 🤍 ⚠️ Note: Instead of loading the notebooks on notebooks.ai, you should use Google Colab instead. Here are instructions on loading a notebook directly from GitHub into Google Colab: 🤍 ⭐️ Course Contents ⭐️ ⌨️ Part 1: Introduction What is Data Analysis, why Python?, what other options are there? what's the cycle of a Data Analysis project? What's the difference between Data Analysis and Data Science? 🔗 Slides for this section: 🤍 ⌨️ Part 2: Real Life Example of a Python/Pandas Data Analysis project (00:11:11) A demonstration of a real life data analysis project using Python, Pandas, SQL and Seaborn. Don't worry, we'll dig deeper in the following sections 🔗 Notebooks: 🤍 ⌨️ Part 3: Jupyter Notebooks Tutorial (00:30:50) A step by step tutorial to learn how to use Juptyer Notebooks 🔗 Twitter Cheat Sheet: 🤍 🔗 Notebooks: 🤍 ⌨️ Part 4: Intro to NumPy (01:04:58) Learn why NumPy was such an important library for the data-processing world in Python. Learn about low level details of computations and memory storage, and why tools like Excel will always be limited when processing large volumes of data. 🔗 Notebooks: 🤍 ⌨️ Part 5: Intro to Pandas (01:57:08) Pandas is arguably the most important library for Data Processing in the Python world. Learn how it works and how its main data structure, the Data Frame, compares to other tools like spreadsheets or DFs used for Big Data 🔗 Notebooks: 🤍 ⌨️ Part 6: Data Cleaning (02:47:18) Learn the different types of issues that we'll face with our data: null values, invalid values, statistical outliers, etc, and how to clean them. 🔗 Notebooks: 🤍 ⌨️ Part 7: Reading Data from other sources (03:25:15) 🔗 Notebooks: 🤍 ⌨️ Part 8: Python Recap (03:55:19) If your Python or coding skills are rusty, check out this section for a quick recap of Python main features and control flow structures. 🔗 Notebooks: 🤍 Learn to code for free and get a developer job: 🤍 Read hundreds of articles on programming: 🤍
Comprendre les matrices et le calcul matriciel est indispensable pour développer des programmes de machine Learning (et encore plus de Deep Learning). Avec Python, on utilise le package Numpy pour créer et manipuler des matrices. Dans cette vidéo, nous voyons les points essentiels sur les matrices: vérifier les dimensions, effectuer la transposée, addition, soustraction, et produit matriciel. Écrivez TOUJOURS les dimensions de vos matrices pour éviter de faire des erreurs mathématiques dans vos codes ! ► ARTICLE EN COMPLÉMENT DE CETTE VIDÉO: 🤍 ► Soutenez-moi sur Tipeee pour du contenu BONUS: 🤍 ► REJOINS NOTRE COMMUNAUTÉ DISCORD 🤍 ► Recevez gratuitement mon Livre: APPRENDRE LE MACHINE LEARNING EN UNE SEMAINE CLIQUEZ ICI: 🤍 ► Abonnez-vous : 🤍 ► Pour En Savoir plus : Visitez Machine Learnia : 🤍 ► Qui suis-je ? Je m’appelle Guillaume Saint-Cirgue et je suis Data Scientist au Royaume Uni. Après avoir suivi un parcours classique maths sup maths spé et avoir intégré une bonne école d’ingénieur, je me suis tourné vers l’intelligence artificielle de ma propre initiative et j’ai commencé à apprendre tout seul le machine learning et le deep learning en suivant des formations payantes, en lisant des articles scientifiques, en suivant les cours du MIT et de Stanford et en passant des week end entier à développer mes propres codes. Aujourd’hui, je veux vous offrir ce que j’ai appris gratuitement car le monde a urgemment besoin de se former en Intelligence Artificielle. Que vous souhaitiez changer de vie, de carrière, ou bien développer vos compétences à résoudre des problèmes, ma chaîne vous y aidera. C’est votre tour de passer à l’action ! ► Une question ? Contactez-moi: contact🤍machinelearnia.com
Numpy is the first thing you want to know when you're starting to learn about ML. Feel free to ask any doubt you got in the comments or mail us at (provided below). DON'T FORGET TO SUBSCRIBE.. SUBSCRIBED? THEN LIKE, SHARE AND COMMENT.. * Timeskip for practical: 02:55 GitHub link: 🤍 * Email: soyokazeha🤍gmail.com Instagram: 🤍
In this video, learn Python Numpy Full Tutorial For Beginners | Numpy Full Course in 4 Hours 🔥. Timestamps: 00:00:00 | What is a NumPy 00:12:48 | Python Lists vs. NumPy Arrays 00:22:40 | NumPy Array Creation 00:38:28 | Special Types of Arrays ( filled with specific value) 00:53:44 | Creating Random Valued Arrays 01:05:59 | NumPy - Data Types 01:22:57 | Shape and Reshape in NumPy 01:38:02 | NumPy - Arithmetic Operations 02:02:37 | Broadcasting with NumPy Arrays 02:17:04 | Indexing & Slicing 02:45:57 | NumPy Array Iterating 03:00:39 | The Difference Between Copy and View 03:06:14 | Join & Split Function 03:23:28 | Search , Sort , Search Shorted, Filter Functions 03:40:39 | Shuffle, Unique, Resize, Flatten, Ravel Functions 03:52:22 | Insert and Delete Function 04:03:23 | NumPy - Matrix 04:16:24 | Matrix Function 💎 Get Access to Premium Videos and Live Streams: 🤍 WsCube Tech is a leading Web, Mobile App & Digital Marketing company, and institute in India. We help businesses of all sizes to build their online presence, grow their business, and reach new heights. 👉For Digital Marketing services (Brand Building, SEO, SMO, PPC, SEM, Content Writing), Web Development and App Development solutions, visit our website: 🤍 👉Want to learn new skills and improve existing ones with in-depth and practical sessions? Enroll in our advanced online courses now and make yourself job-ready: 🤍 All the courses are job-oriented, up-to-date with the latest algorithms and modules, fully practical, and provide you hands-on projects. 👉 Want to learn and acquire skills in English? Visit WsCube Tech English channel: 🤍 📞 For more info about the courses, call us: +91-7878985501, +91-9269698122 ✅ CONNECT WITH THE FOUNDER (Mr. Kushagra Bhatia) - 👉 Instagram - 🤍 👉 LinkedIn - 🤍 👉 Facebook - 🤍 Connect with WsCube Tech on social media for the latest offers, promos, job vacancies, and much more: ► Subscribe: 🤍 ► Facebook: 🤍 ► Twitter: 🤍 ► Instagram: 🤍 ► LinkedIn : 🤍 ► Youtube: 🤍 ► Website: 🤍 | Thanks |- #NumpyTutorial #Numpy #NumpyCourse
NumPy is a Python library for manipulating large, multi-dimensional arrays, which is fundamental to machine learning. This module will cover all you need to know about NumPy, which allows you to start immediately by performing machine learning and artificial intelligence tasks. Topics include: How to create NumPy arrays with different ranks and dimensions? How to inspect the shape, dimension, and data type of an NumPy array? How to create NumPy arrays with pre-filled numbers (zeros, ones, random values)? How to copy, sort, reshape, transpose, and flatten NumPy arrays? How to add or remove elements from an NumPy array? How to combine or split an NumPy array horizontally or vertically? How to index, slice, and subset an NumPy array? How to do scalar math operations using NumPy? How to do vector math operations (e.g., dot product) using NumPy? How to calculate sum, mean, and other statistics by row or column in NumPy? How can we understand axis = 0 and axis = 1 in NumPy? Code used in this module can be downloaded from GitHub: 🤍 Hashtags: #ai #artificialintelligence #tutorial #tutorials #numpy #coding #numpytutorial #googlecolab #programming #machinelearning #deeplearning #python #pythonprogramming #pythontutorial #aitutorial
Machine Learning is the process of teaching a computer how perform a task with out explicitly programming it. The process feeds algorithms with large amounts of data to gradually improve predictive performance. #ai #python #100SecondsOfCode 🔗 Resources Machine Learning Tutorials 🤍 What is ML 🤍 Neural Networks 🤍 ML Wiki 🤍 🔥 Watch more with Fireship PRO Upgrade to Fireship PRO at 🤍 Use code lORhwXd2 for 25% off your first payment. 🎨 My Editor Settings - Atom One Dark - vscode-icons - Fira Code Font Topics Covered - Convolutional Neural Networks - Machine Learning Basics - How Data Science Works - Big Data and Feature Engineering - Artificial Intelligence History - Supervised Machine Learning
Numpy conditional filtering using where. 🔊 Watch till last for a detailed description 💯 Read Full Blog with Code 🤍 💬 Leave your comments and doubts in the comment section 📌 Save this channel and video for watch later 👍 Like this video to show your support and love ❤️ ~~~~~~~~ 🆓 Watch My Top Free Data Science Videos 👉🏻 Python for Data Scientist 🤍 👉🏻 Machine Learning for Beginners 🤍 👉🏻 Feature Selection in Machine Learning 🤍 👉🏻 Text Preprocessing and Mining for NLP 🤍 👉🏻 Natural Language Processing (NLP) Tutorials 🤍 👉🏻 Deep Learning with TensorFlow 2.0 and Keras 🤍 👉🏻 COVID 19 Data Analysis and Visualization Masterclass 🤍 👉🏻 Machine Learning Model Deployment Using Flask at AWS 🤍 👉🏻 Make Your Own Automated Email Marketing Software in Python 🤍 * 🤝 BE MY FRIEND 🌍 Check Out ML Blogs: 🤍 🐦Add me on Twitter: 🤍 📄 Follow me on GitHub: 🤍 📕 Add me on Facebook: 🤍 💼 Add me on LinkedIn: 🤍 👉🏻 Complete Udemy Courses: 🤍 ⚡ Check out my Recent Videos: 🤍 🔔 Subscribe me for Free Videos: 🤍 🤑 Get in touch for Promotion: info🤍kgptalkie.com ✍️🏆🏅🎁🎊🎉✌️👌⭐⭐⭐⭐⭐ ENROLL in My Highest Rated Udemy Courses to 🔑 Unlock Data Science Interviews 🔎 and Tests 📚 📗 NLP: Natural Language Processing ML Model Deployment at AWS Build & Deploy ML NLP Models with Real-world use Cases. Multi-Label & Multi-Class Text Classification using BERT. Course Link: 🤍 📊 📈 Data Visualization in Python Masterclass: Beginners to Pro Visualization in matplotlib, Seaborn, Plotly & Cufflinks, EDA on Boston Housing, Titanic, IPL, FIFA, Covid-19 Data. Course Link: 🤍 📘 📙 Natural Language Processing (NLP) in Python for Beginners NLP: Complete Text Processing with Spacy, NLTK, Scikit-Learn, Deep Learning, word2vec, GloVe, BERT, RoBERTa, DistilBERT Course Link: 🤍 📈 📘 2021 Python for Linear Regression in Machine Learning Linear & Non-Linear Regression, Lasso & Ridge Regression, SHAP, LIME, Yellowbrick, Feature Selection & Outliers Removal. You will learn how to build a Linear Regression model from scratch. Course Link: 🤍 📙📊 2021 R 4.0 Programming for Data Science || Beginners to Pro Learn Latest R 4.x Programming. You Will Learn List, DataFrame, Vectors, Matrix, DateTime, DataFrames in R, GGPlot2, Tidyverse, Machine Learning, Deep Learning, NLP, and much more. Course Link: 🤍
This is episode 1 of Python libraries for Machine Learning. It is based on NumPy - Numerical Python. Click here to subscribe: 🤍 Topics to be covered in this video are, 1) What is NumPy? 2) Why do we need to use NumPy? 3) Pros and Cons of NumPy 4) Conclusion Stay home, keep learning keep watching Nur AI. - GitHub: 🤍 Instagram: 🤍 Facebook: 🤍 Twitter: Take a look at Ai_with_NUR (🤍with_nur): 🤍 - Introduction to python: 🤍 🔔Please leave a like and subscribe for more content Got a question on the topic?🤔. Please share it in the comment section below. - Handle by: Neha Attara and Urmi Mahajan #NumPy #pythonlibraries #NumpyforMachinelearning #ai #nurai #detection#python3 #py #animation #machinelearning #ai #deeplearning #artificialintelligence #lockdown2020 #workfromhome #neuralnetworks #ml #dl #datascience #skills #technology #realtime ai,ml,dl,artificial intelligence,machine learning,deep learning,numpy,pandas,real time,scikit learn,sci py,imutils,tensorflow,cnn,model,training,computer engineering,A real-time age gender prediction,python,python3,keras,face,human, real time
Thank you for watching the video! You can learn data science FASTER at 🤍! Master Python at 🤍 Learn SQL & Relational Databases at 🤍 Learn NumPy, Pandas, and Python for Data Science at 🤍 Become a Machine Learning Expert at 🤍 Don't forget to subscribe if you enjoyed the video :D
In this video I have explained NumPy arrays. How to install numPy pip install numpy How to import v import numpy as np Working of NumPy with arrays creating NumPy array, difference between NumPy and list why we need NumPy array which one is faster numPy array or List I have also covered all the almost all the functions which are there in NumPy I have covered slicing of array How to do arithmetic operations on numPy arrays. etc #NumPy #Python #Data Science #Machine Learning Please watch to video till the end to understand everything in NumPy Please like and share my videos if you really like. Please subscribe to my Channel it will really motivate me to create more videos. Thank you!
In this video, you’ll learn about Numpy Python for Machine Learning and the basics of the Numpy Python Library. This library is essential to Machine Learning. You’ll learn about how to convert Python lists and matrices into Numpy arrays; how to utilize built-in functions unique to the Numpy library such as np.zeros(), np.ones(), np.eye(), and np.arange(); what the methods and attributes of the Numpy Library are including reshape(), max(), min(), argmax(), and argmin(); and how to use np.random with specific commands such as randint(), rand(), and randn() to generate random arrays and matrices. After watching this video and learning the basics of Numpy, there’s an activity right below this description for you to try out on your own using the skills you learned here. Enjoy! NUMPY ACTIVITY: Create a random 1 dimensional numpy array with some values larger than 1 and some smaller than -1 and reshape this array to be 2 or more rows. TIMESTAMP: 00:00 Introduction & Recap of Last Video 00:34 Lesson Structure for Numpy Array Basics Video 01:02 Numpy Array using Python List and Matrix 02:24 Numpy Array using Built-In Numpy Functions 06:28 Numpy Array Methods and Attributes 08:55 Numpy Array using Random 10:28 Call-to-action: Numpy Array Basics About Acadaimy Acadaimy empowers individuals to rapidly learn and master the fascinating field of Artificial Intelligence through easy-to-consume bite-sized videos delivered on a weekly basis. Topics include artificial intelligence, machine learning, ai applications, supervised learning, unsupervised learning, pattern recognition, artificial general intelligence, deep learning, data science, big data and more. While most immediately suitable for beginners, Acadaimy's AI training and accompanying examples and projects aims to deliver value to anyone ultimately seeking expertise in artificial intelligence. Subscribe to Acadaimy: 🤍
Learn how to get statistics of arrays like max, min, std, and variance. 🔊 Watch till last for a detailed description 💯 Read Full Blog with Code 🤍 💬 Leave your comments and doubts in the comment section 📌 Save this channel and video for watch later 👍 Like this video to show your support and love ❤️ ~~~~~~~~ 🆓 Watch My Top Free Data Science Videos 👉🏻 Python for Data Scientist 🤍 👉🏻 Machine Learning for Beginners 🤍 👉🏻 Feature Selection in Machine Learning 🤍 👉🏻 Text Preprocessing and Mining for NLP 🤍 👉🏻 Natural Language Processing (NLP) Tutorials 🤍 👉🏻 Deep Learning with TensorFlow 2.0 and Keras 🤍 👉🏻 COVID 19 Data Analysis and Visualization Masterclass 🤍 👉🏻 Machine Learning Model Deployment Using Flask at AWS 🤍 👉🏻 Make Your Own Automated Email Marketing Software in Python 🤍 * 🤝 BE MY FRIEND 🌍 Check Out ML Blogs: 🤍 🐦Add me on Twitter: 🤍 📄 Follow me on GitHub: 🤍 📕 Add me on Facebook: 🤍 💼 Add me on LinkedIn: 🤍 👉🏻 Complete Udemy Courses: 🤍 ⚡ Check out my Recent Videos: 🤍 🔔 Subscribe me for Free Videos: 🤍 🤑 Get in touch for Promotion: info🤍kgptalkie.com ✍️🏆🏅🎁🎊🎉✌️👌⭐⭐⭐⭐⭐ ENROLL in My Highest Rated Udemy Courses to 🔑 Unlock Data Science Interviews 🔎 and Tests 📚 📗 NLP: Natural Language Processing ML Model Deployment at AWS Build & Deploy ML NLP Models with Real-world use Cases. Multi-Label & Multi-Class Text Classification using BERT. Course Link: 🤍 📊 📈 Data Visualization in Python Masterclass: Beginners to Pro Visualization in matplotlib, Seaborn, Plotly & Cufflinks, EDA on Boston Housing, Titanic, IPL, FIFA, Covid-19 Data. Course Link: 🤍 📘 📙 Natural Language Processing (NLP) in Python for Beginners NLP: Complete Text Processing with Spacy, NLTK, Scikit-Learn, Deep Learning, word2vec, GloVe, BERT, RoBERTa, DistilBERT Course Link: 🤍 📈 📘 2021 Python for Linear Regression in Machine Learning Linear & Non-Linear Regression, Lasso & Ridge Regression, SHAP, LIME, Yellowbrick, Feature Selection & Outliers Removal. You will learn how to build a Linear Regression model from scratch. Course Link: 🤍 📙📊 2021 R 4.0 Programming for Data Science || Beginners to Pro Learn Latest R 4.x Programming. You Will Learn List, DataFrame, Vectors, Matrix, DateTime, DataFrames in R, GGPlot2, Tidyverse, Machine Learning, Deep Learning, NLP, and much more. Course Link: 🤍
In this video, learn NumPy Arrays - How to Create NumPy Array | Machine Learning Tutorial. Find all the videos of the NumPy Tutorial for Beginner to Advanced Course in this playlist: 🤍 💎 Get Access to Premium Videos and Live Streams: 🤍 WsCube Tech is a leading Web, Mobile App & Digital Marketing company, and institute in India. We help businesses of all sizes to build their online presence, grow their business, and reach new heights. 👉For Digital Marketing services (Brand Building, SEO, SMO, PPC, SEM, Content Writing), Web Development and App Development solutions, visit our website: 🤍 👉Want to learn new skills and improve existing ones with in-depth and practical sessions? Enroll in our advanced online courses now and make yourself job-ready: 🤍 All the courses are job-oriented, up-to-date with the latest algorithms and modules, fully practical, and provide you hands-on projects. 👉 Want to learn and acquire skills in English? Visit WsCube Tech English channel: 🤍 📞 For more info about the courses, call us: +91-7878985501, +91-9269698122 ✅ CONNECT WITH THE FOUNDER (Mr. Kushagra Bhatia) - 👉 Instagram - 🤍 👉 LinkedIn - 🤍 Connect with WsCube Tech on social media for the latest offers, promos, job vacancies, and much more: ► Subscribe: 🤍 ► Facebook: 🤍 ► Twitter: 🤍 ► Instagram: 🤍 ► LinkedIn : 🤍 ► Youtube: 🤍 ► Website: 🤍 | Thanks |- #machinelearning #python #numpy
La régression linéaire multiple et la régression polynomiale, c'est plus simple qu'il n'y parait ! Si vous comprenez bien les formes matricielles de la régression linéaire simple, alors il suffit de modifier légèrement le contenu de quelques matrices pour développer des modelés non linéaires bien plus riches et complexe. Avec Numpy, je vous montre comment arriver à développer de tels modèles en seulement quelques lignes de codes. C'est souvent à ce stade que les gens commencent à se dire wow ! c'est du Machine Learning, on développe des modèles à plusieurs dimensions... Mais vraiment, ce ne sont que des mathématiques simples et vous allez vraiment tout comprendre avec cette vidéo :) ► ARTICLE EN COMPLÉMENT DE CETTE VIDÉO: 🤍 ► Soutenez-moi sur Tipeee pour du contenu BONUS: 🤍 ► REJOINS NOTRE COMMUNAUTÉ DISCORD 🤍 ► Recevez gratuitement mon Livre: APPRENDRE LE MACHINE LEARNING EN UNE SEMAINE CLIQUEZ ICI: 🤍 ► Abonnez-vous : 🤍 ► Pour En Savoir plus : Visitez Machine Learnia : 🤍 ► Qui suis-je ? Je m’appelle Guillaume Saint-Cirgue et je suis Data Scientist au Royaume Uni. Après avoir suivi un parcours classique maths sup maths spé et avoir intégré une bonne école d’ingénieur, je me suis tourné vers l’intelligence artificielle de ma propre initiative et j’ai commencé à apprendre tout seul le machine learning et le deep learning en suivant des formations payantes, en lisant des articles scientifiques, en suivant les cours du MIT et de Stanford et en passant des week end entier à développer mes propres codes. Aujourd’hui, je veux vous offrir ce que j’ai appris gratuitement car le monde a urgemment besoin de se former en Intelligence Artificielle. Que vous souhaitiez changer de vie, de carrière, ou bien développer vos compétences à résoudre des problèmes, ma chaîne vous y aidera. C’est votre tour de passer à l’action ! ► Une question ? Contactez-moi: contact🤍machinelearnia.com
In this video, learn Learn About Shape and Reshaping in NumPy Arrays | Machine Learning Tutorial. Find all the videos of the NumPy Tutorial for Beginner to Advanced Course in this playlist: 🤍 💎 Get Access to Premium Videos and Live Streams: 🤍 WsCube Tech is a leading Web, Mobile App & Digital Marketing company, and institute in India. We help businesses of all sizes to build their online presence, grow their business, and reach new heights. 👉For Digital Marketing services (Brand Building, SEO, SMO, PPC, SEM, Content Writing), Web Development, and App Development solutions, visit our website: 🤍 👉Want to learn new skills and improve existing ones with in-depth and practical sessions? Enroll in our advanced online courses now and make yourself job-ready: 🤍 All the courses are job-oriented, up-to-date with the latest algorithms and modules, fully practical, and provide you hands-on projects. 👉 Want to learn and acquire skills in English? Visit WsCube Tech English channel: 🤍 📞 For more info about the courses, call us: +91-7878985501, +91-9269698122 ✅ CONNECT WITH THE FOUNDER (Mr. Kushagra Bhatia) - 👉 Instagram - 🤍 👉 LinkedIn - 🤍 Connect with WsCube Tech on social media for the latest offers, promos, job vacancies, and much more: ► Subscribe: 🤍 ► Facebook: 🤍 ► Twitter: 🤍 ► Instagram: 🤍 ► LinkedIn : 🤍 ► Youtube: 🤍 ► Website: 🤍 | Thanks |- #Numpy #Python #Machinelearning
SUBSCRIBE for more great content: 🤍 This is a much asked question do here's how I learnt machine learning successfully! In this video, I'm gonna share with you guys the 6 keys factors when it comes to machine learning, so that you guy know what really matters. Machine Learning is an every-growing field of importance in todays' world and it has firmly secured its place in our future as well. So it's really imperative that we make efforts to learn it. First things first, Math. You can't escape it, but don't panic, make Math a long term learning goal. Next up, take a basic machine learning course which will teach you basic algorithms like linear regression. I also made a video on Linear Regression which you can check out here: 🤍 Next, learning Python, and Numpy. As these are part of the building blocks of your machine learning foundation. Once you've done so, practice with some basic projects and then work on your data preparation skills (this will take time to hone). Also, familiarize yourself with ML Libraries like Scikit learn and TensorFlow. But most importantly, practice and don't stop learning! SOCIALS: linkedIn: 🤍 instagram: 🤍 Resources 1. Math - mainly linear algebra, probability, statistics. -Linear Algebra: 🤍 -Statistics & Probability: 🤍 2. Basic Machine Learning courses: -🤍 -🤍 3.Learn Python & Numpy. Basic python: -🤍 -🤍 Advanced python: -🤍 Numpy: -🤍 4.Data Preparation: Extremely Important! 5. Machine learning Libraries: Scikit-learn: 🤍 TensorFlow: 🤍 6. Practice! No Zero Days!
Hello all, through this channel i am sharing my journey to transition from mechanical engineering to data science via daily short video, I share my experiences and challenges of making the career switch, and how I'm overcoming them. I'll be sharing insights and tips that have helped me along the way, as am sharing the tools I'm using to build my data science skills. If you're also considering a career change or interested in the field of data science, be sure to subscribe to my channel for more content like this!"
Hello all, through this channel i am sharing my journey to transition from mechanical engineering to data science via daily short video, I share my experiences and challenges of making the career switch, and how I'm overcoming them. I'll be sharing insights and tips that have helped me along the way, as am sharing the tools I'm using to build my data science skills. If you're also considering a career change or interested in the field of data science, be sure to subscribe to my channel for more content like this!"
Detailed explanation of NumPy in Python. Link for jupyter Notebook: 🤍
TensorFlow is a tool for machine learning capable of building deep neural networks with high-level Python code. It provides developer-friendly APIs that help software engineers train, analyze, and deploy ML models. #programming #deeplearning #100secondsofcode 🔗 Resources TensorFlow Docs 🤍 Fashion MNIST Tutorial 🤍 Neural Networks Overview for Data Scientists 🤍 Machine Learning in 100 Seconds 🤍 🔥 Get More Content - Upgrade to PRO Upgrade to Fireship PRO at 🤍 Use code lORhwXd2 for 25% off your first payment. 🎨 My Editor Settings - Atom One Dark - vscode-icons - Fira Code Font 🔖 Topics Covered - What is TensorFlow? - How to build a neural network with TensorFlow - What is TensorFlow used for? - Who created TensorFlow? - How neural networks work - Easy neural network tutorial - What is a mathematical Tensor?
It can be not obvious how to pass Numpy array across separate services when running ML infra in separate containers. When data preparation service runs in a different container than training service. In this tutorial, I show how to convert Numpy to list and JSON to be able to send it through RabbitMQ message broker and consume it on the receiver side. Katana ML Skipper code: 🤍 0:00 Introduction 0:44 Skipper 2:16 Example 7:48 Summary CONNECT: - Subscribe to this YouTube channel - Twitter: 🤍 - LinkedIn: 🤍 - Facebook: 🤍 - Medium: 🤍 #Python #MachineLearning
.svg){kind=small}
![NumPy Tutorial - Basics in 20 Minutes! [Updated]](https://i.ytimg.com/vi/pJoYVxgI840/mqdefault.jpg "NumPy Tutorial - Basics in 20 Minutes! [Updated]")
