Course Intro

Your First Day

What Is Machine Learning?

AI/Machine Learning/Data Science

Exercise: Machine Learning Playground

How Did We Get Here?

Exercise: YouTube Recommendation Engine

Types of Machine Learning

What Is Machine Learning? Round 2

Section Review

Section Overview: Machine Learning and Data Science Framework

Introducing Our Framework

6-Step Machine Learning Framework

Types of Machine Learning Problems

Types of Data

Types of Evaluation

Features in Data

Modelling - Splitting Data

Modelling - Picking the Model

Modelling - Comparison

Overfitting and Underfitting Definitions: Experimentation

Tools We Will Use

Quick Announcement

Section Overview: Data Science Environment Setup

Introducing Our Tools

What is Conda?

Conda Environments

Mac Environment Setup

Mac Environment Setup 2

Windows Environment Setup 2

Linux Environment Setup

Sharing your Conda Environment

Jupyter Notebook Walkthrough

Jupyter Notebook Walkthrough 2

J upyter Notebook Walkthrough 3

Section Overview: Pandas - Data Analysis

Downloading Workbooks & Assignments

Pandas Introduction

Series, Data Frames & CSVs

Data from URLs

Describing Data with Pandas

Selecting and Viewing Data with Pandas

Selecting and Viewing Data with Pandas Part 2

Manipulating Data

Manipulating Data 2

Manipulating Data 3

Assignment: Pandas Practice

How To Download The Course Assignments

Section Overview: NumPy

NumPy Introduction

Quick Note: Correction in the next video

NumPy DataTypes and Attributes

Creating NumPy Arrays

NumPy Random Seed

Viewing Arrays and Matrices

Manipulating Arrays

Manipulating Arrays 2

Standard Deviation and Variance

Reshape and Transpose

Dot Product vs Element Wise

Exercise: Nut Butter Store Sales

Comparison Operators

Sorting Arrays

T urn Images Into NumPy Arrays

Assignment: NumPy Practice

Section Overview: Matplotlib - Plotting and Data Visualization

Matplotlib Introduction

Importing And Using Matplotlib

Anatomy Of A Matplotlib Figure

Scatter Plot And Bar Plot

Histograms And Subplots

Subplots Option 2

Quick Tip: Data Visualizations

Plotting From Pandas DataFrames

Quick Note: Regular Expressions

Plotting From Pandas DataFrames 2

Plotting from Pandas DataFrames 3

Plotting from Pandas DataFrames 4

Plotting from Pandas DataFrames 5

Plotting from Pandas DataFrames 6

Plotting from Pandas DataFrames 7

Customizing Your Plots

Customizing Your Plots 2

Saving And Sharing Your Plots

Assignment: Matplotlib Practice

Section Overview: Scikit-learn Creating Machine Learning Models

Where To Keep Learning

Zero To Mastery

Become a Machine Learning Engineer in 2024! 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 👇

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🗂 Crash Course Files: https://links.zerotomastery.io/machine-learning-crash-course 
🐍 Free Python Crash Course: https://youtu.be/4uBbCUjJ_G8
👍 Subscribe for more free tutorials and exclusive content: https://links.zerotomastery.io/youtube

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⏲  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-mastery-ml
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-mastery-ml
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

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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 👆

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Full ML Bootcamp 👉 https://zerotomastery.io/courses/machine-learning-and-data-science-bootcamp/

#zerotomastery #machinelearning

Intro to Machine Learning & Data Science in 2024 (+Pandas, NumPy, Matplotlib)

Intro to Machine Learning & Data Science in 2024 (+Pandas, NumPy, Matplotlib)

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