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

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 👇

🎁 [LIMITED TIME ONLY] Use code: YTMLDS10 to get 10% OFF (for life!)

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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
3:49:19 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
5:25:14
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.

This could be you 👆

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#zerotomastery #machinelearning

[thjdhfn]

Day 1: 1:09:18
Day 2: 3:49:11
Day 3: 5:17:07
Day 4: 6:27:17
Day 5: 7:35:41
Day 6: 8:04:04 (matplotlib is hard lol)
Day 7: 8:56:09

[alexsuen]

I benefited a lot from Daniel's PyTorch course, and I would like to follow him again on this course!☺

[christopheanfry]

Just Amazing!!! Will save some money and will take the full course!!! Thank you so much

[debajyatidey]

I was eagerly waiting for this to come here on YouTube from looong ago!

[rostyslavkhrinovskyi]

Thanks for this hard work! One of the best course about ML that I found.

[SonuKumar-jger]

I am waiting for git and GitHub bootcamp

[eduardosantosflores]

Upload de Business Intelligence with Python please

[kiransatyaraj]

Day 1: 1:02:55
Day 2: 1:55:24
Day 3: 3:11:39

[lakshaymalik]

How many more hours are there in full course?? Does it include scikit pytorch and other things??

[hamzaalisyed]

hello. can anybody guide me through one thing? I already have anaconda navigator, and all the required extensions downloaded. Is it still necessary to access through command prompt. Or is it fine with making project folders exteriorly and accessing through it?

[ahmadromman]

i have noticed that your jupyter autofill when you type [ ] () {} " ". but i have to type them twice everytime and it can be confusing, specially that the cursor is at the end everytime. is there way to make it autofill like you?

[pepadeliola]

Is taking this machine learning and data science bootcamp enough to get a job?

[z.r.]

Heeeey. I paid money for this somewhere else... :( what does that mean? Is this content obsolete?

[kavorka]

Guys I would like to share with you this. A while ago, I doubted ChatGPT4's code generation. But when I needed an algorithm that would form an array from the bits of another array of chars, a piece of code that required a lot of bit-wise operations, I decided to ask Bing ChatGPT4. It immediately generated the code, but with some links for more info, including links to stackoverflow. Until that moment, I thought it grabbed the code from those links, however this time I decided to check the links. Guess what, none of the links provided the solution, it was all from the AI itself. Then I realized Microsoft manually included these links to make the program looks like a search engine. Now I have a hard time continuing learning coding! Because I think am wasting my time.