Intro to Machine Learning amp Data Science in 2025 Pandas NumPy Matplotlib

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http://youtube.com/watch?v=r67SfaiYaDI

Become a Machine Learning Engineer in 2025! Join Daniel Bourke Andrei Neagoie as they take you from complete beginner to learning the basics of Machine Learning Data Science. In this 10-hour beginner course, you'll learn: machine learning 101, environment setup, data analysis, and some popular ML libraries like Pandas, NumPy Matplotlib! • This Crash Course is ~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: https://zerotomastery.io/courses/mach... • 🎁 [LIMITED TIME ONLY] Use code: YTMLDS10 to get 10% OFF (for life!) • ========== • 🗂 Crash Course Files: https://links.zerotomastery.io/machin... • 📓 Course Handbook: https://dev.mrdbourke.com/zero-to-mas... • 🐍 Free Python Crash Course: • Python 101 Crash Course: Learn Python... • ========== • ⏲ 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 - https://github.com/mrdbourke/zero-to-... • 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 - https://github.com/mrdbourke/zero-to-... • 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: https://zerotomastery.io/testimonials • This could be you 👆 • ========== • Full ML Bootcamp 👉 https://zerotomastery.io/courses/mach... • #zerotomastery #machinelearning

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