Mô tả

This is a top selling Machine Learning and Data Science course just updated this month with the latest trends and skills for 2023! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 900,000+ engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. Graduates of Andrei’s courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Meta, + other top tech companies. You will go from zero to mastery!


Learn Data Science and Machine Learning from scratch, get hired, and have fun along the way with the most modern, up-to-date Data Science course on Udemy (we use the latest version of Python, Tensorflow 2.0 and other libraries). This course is focused on efficiency: never spend time on confusing, out of date, incomplete Machine Learning tutorials anymore. We are pretty confident that this is the most comprehensive and modern course you will find on the subject anywhere (bold statement, we know).

This comprehensive and project based course will introduce you to all of the modern skills of a Data Scientist and along the way, we will build many real world projects to add to your portfolio. You will get access to all the code, workbooks and templates (Jupyter Notebooks) on Github, so that you can put them on your portfolio right away! We believe this course solves the biggest challenge to entering the Data Science and Machine Learning field: having all the necessary resources in one place and learning the latest trends and on the job skills that employers want.


The curriculum is going to be very hands on as we walk you from start to finish of becoming a professional Machine Learning and Data Science engineer. The course covers 2 tracks. If you already know programming, you can dive right in and skip the section where we teach you Python from scratch. If you are completely new, we take you from the very beginning and actually teach you Python and how to use it in the real world for our projects. Don't worry, once we go through the basics like Machine Learning 101 and Python, we then get going into advanced topics like Neural Networks, Deep Learning and Transfer Learning so you can get real life practice and be ready for the real world (We show you fully fledged Data Science and Machine Learning projects and give you programming Resources and Cheatsheets)!

The topics covered in this course are:


- Data Exploration and Visualizations

- Neural Networks and Deep Learning

- Model Evaluation and Analysis

- Python 3

- Tensorflow 2.0

- Numpy

- Scikit-Learn

- Data Science and Machine Learning Projects and Workflows

- Data Visualization in Python with MatPlotLib and Seaborn

- Transfer Learning

- Image recognition and classification

- Train/Test and cross validation

- Supervised Learning: Classification, Regression and Time Series

- Decision Trees and Random Forests

- Ensemble Learning

- Hyperparameter Tuning

- Using Pandas Data Frames to solve complex tasks

- Use Pandas to handle CSV Files

- Deep Learning / Neural Networks with TensorFlow 2.0 and Keras

- Using Kaggle and entering Machine Learning competitions

- How to present your findings and impress your boss

- How to clean and prepare your data for analysis

- K Nearest Neighbours

- Support Vector Machines

- Regression analysis (Linear Regression/Polynomial Regression)

- How Hadoop, Apache Spark, Kafka, and Apache Flink are used

- Setting up your environment with Conda, MiniConda, and Jupyter Notebooks

- Using GPUs with Google Colab


By the end of this course, you will be a complete Data Scientist that can get hired at large companies. We are going to use everything we learn in the course to build professional real world projects like Heart Disease Detection, Bulldozer Price Predictor, Dog Breed Image Classifier, and many more. By the end, you will have a stack of projects you have built that you can show off to others.


Here’s the truth: Most courses teach you Data Science and do just that. They show you how to get started. But the thing is, you don’t know where to go from there or how to build your own projects. Or they show you a lot of code and complex math on the screen, but they don't really explain things well enough for you to go off on your own and solve real life machine learning problems.


Whether you are new to programming, or want to level up your Data Science skills, or are coming from a different industry, this course is for you. This course is not about making you just code along without understanding the principles so that when you are done with the course you don’t know what to do other than watch another tutorial. No! This course will push you and challenge you to go from an absolute beginner with no Data Science experience, to someone that can go off, forget about Daniel and Andrei, and build their own Data Science and Machine learning workflows.


Machine Learning has applications in Business Marketing and Finance, Healthcare, Cybersecurity, Retail, Transportation and Logistics, Agriculture, Internet of Things, Gaming and Entertainment, Patient Diagnosis, Fraud Detection, Anomaly Detection in Manufacturing, Government, Academia/Research, Recommendation Systems and so much more. The skills learned in this course are going to give you a lot of options for your career.

You hear statements like Artificial Neural Network, or Artificial Intelligence (AI), and by the end of this course, you will finally understand what these mean!


Click “Enroll Now” and join others in our community to get a leg up in the industry, and learn Data Scientist and Machine Learning. We guarantee this is better than any bootcamp or online course out there on the topic. See you inside the course!


Taught By:

Daniel Bourke:
A self-taught Machine Learning Engineer who lives on the internet with an uncurable desire to take long walks and fill up blank pages.

My experience in machine learning comes from working at one of Australia's fastest-growing artificial intelligence agencies, Max Kelsen.

I've worked on machine learning and data problems across a wide range of industries including healthcare, eCommerce, finance, retail and more.

Two of my favourite projects include building a machine learning model to extract information from doctors notes for one of Australia's leading medical research facilities, as well as building a natural language model to assess insurance claims for one of Australia's largest insurance groups.

Due to the performance of the natural language model (a model which reads insurance claims and decides which party is at fault), the insurance company were able to reduce their daily assessment load by up to 2,500 claims.

My long-term goal is to combine my knowledge of machine learning and my background in nutrition to work towards answering the question "what should I eat?".

Aside from building machine learning models on my own, I love writing about and making videos on the process. My articles and videos on machine learning on Medium, personal blog and YouTube have collectively received over 5-million views.

I love nothing more than a complicated topic explained in an entertaining and educative matter. I know what it's like to try and learn a new topic, online and on your own. So I pour my soul into making sure my creations are accessible as possible.

My modus operandi (a fancy term for my way of doing things) is learning to create and creating to learn. If you know the Japanese word for this concept, please let me know.

Questions are always welcome.

--------

Andrei Neagoie:
Andrei is the instructor of the highest rated Development courses on Udemy as well as one of the fastest growing. His graduates have moved on to work for some of the biggest tech companies around the world like Apple, Google, Amazon, JP Morgan, IBM, UNIQLO etc... He has been working as a senior software developer in Silicon Valley and Toronto for many years, and is now taking all that he has learned, to teach programming skills and to help you discover the amazing career opportunities that being a developer allows in life. 

Having been a self taught programmer, he understands that there is an overwhelming number of online courses, tutorials and books that are overly verbose and inadequate at teaching proper skills. Most people feel paralyzed and don't know where to start when learning a complex subject matter, or even worse, most people don't have $20,000 to spend on a coding bootcamp. Programming skills should be affordable and open to all. An education material should teach real life skills that are current and they should not waste a student's valuable time.   Having learned important lessons from working for Fortune 500 companies, tech startups, to even founding his own business, he is now dedicating 100% of his time to teaching others valuable software development skills in order to take control of their life and work in an exciting industry with infinite possibilities. 

Andrei promises you that there are no other courses out there as comprehensive and as well explained. He believes that in order to learn anything of value, you need to start with the foundation and develop the roots of the tree. Only from there will you be able to learn concepts and specific skills(leaves) that connect to the foundation. Learning becomes exponential when structured in this way. 

Taking his experience in educational psychology and coding, Andrei's courses will take you on an understanding of complex subjects that you never thought would be possible.  

See you inside the course!

Bạn sẽ học được gì

Become a Data Scientist and get hired

Master Machine Learning and use it on the job

Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0

Use modern tools that big tech companies like Google, Apple, Amazon and Meta use

Present Data Science projects to management and stakeholders

Learn which Machine Learning model to choose for each type of problem

Real life case studies and projects to understand how things are done in the real world

Learn best practices when it comes to Data Science Workflow

Implement Machine Learning algorithms

Learn how to program in Python using the latest Python 3

How to improve your Machine Learning Models

Learn to pre process data, clean data, and analyze large data.

Build a portfolio of work to have on your resume

Developer Environment setup for Data Science and Machine Learning

Supervised and Unsupervised Learning

Machine Learning on Time Series data

Explore large datasets using data visualization tools like Matplotlib and Seaborn

Explore large datasets and wrangle data using Pandas

Learn NumPy and how it is used in Machine Learning

A portfolio of Data Science and Machine Learning projects to apply for jobs in the industry with all code and notebooks provided

Learn to use the popular library Scikit-learn in your projects

Learn about Data Engineering and how tools like Hadoop, Spark and Kafka are used in the industry

Learn to perform Classification and Regression modelling

Learn how to apply Transfer Learning

Yêu cầu

  • No prior experience is needed (not even Math and Statistics). We start from the very basics.
  • A computer (Linux/Windows/Mac) with internet connection.
  • Two paths for those that know programming and those that don't.
  • All tools used in this course are free for you to use.

Nội dung khoá học

21 sections

Introduction

5 lectures
Course Outline
05:59
Join Our Online Classroom!
04:01
Exercise: Meet Your Classmates & Instructor
01:43
Asking Questions + Getting Help
00:48
Your First Day
03:48

Machine Learning 101

11 lectures
What Is Machine Learning?
06:52
AI/Machine Learning/Data Science
04:51
ZTM Resources
04:23
Exercise: Machine Learning Playground
06:16
How Did We Get Here?
06:03
Exercise: YouTube Recommendation Engine
04:24
Types of Machine Learning
04:41
Are You Getting It Yet?
00:07
What Is Machine Learning? Round 2
04:44
Section Review
01:48
Monthly Coding Challenges, Free Resources and Guides
00:40

Machine Learning and Data Science Framework

15 lectures
Section Overview
03:08
Introducing Our Framework
02:38
6 Step Machine Learning Framework
04:58
Types of Machine Learning Problems
10:32
Types of Data
04:50
Types of Evaluation
03:31
Features In Data
05:22
Modelling - Splitting Data
05:58
Modelling - Picking the Model
04:35
Modelling - Tuning
03:17
Modelling - Comparison
09:32
Overfitting and Underfitting Definitions
01:11
Experimentation
03:35
Tools We Will Use
03:59
Optional: Elements of AI
00:32

The 2 Paths

3 lectures
The 2 Paths
03:27
Python + Machine Learning Monthly
00:22
Endorsements On LinkedIN
00:40

Data Science Environment Setup

13 lectures
Section Overview
01:09
Introducing Our Tools
03:28
What is Conda?
02:35
Conda Environments
04:30
Mac Environment Setup
17:26
Mac Environment Setup 2
14:11
Windows Environment Setup
05:17
Windows Environment Setup 2
23:17
Linux Environment Setup
00:23
Sharing your Conda Environment
01:06
Jupyter Notebook Walkthrough
10:20
Jupyter Notebook Walkthrough 2
16:17
Jupyter Notebook Walkthrough 3
08:10

Pandas: Data Analysis

13 lectures
Section Overview
02:27
Downloading Workbooks and Assignments
00:25
Pandas Introduction
04:29
Series, Data Frames and CSVs
13:21
Data from URLs
00:24
Describing Data with Pandas
09:48
Selecting and Viewing Data with Pandas
11:08
Selecting and Viewing Data with Pandas Part 2
13:06
Manipulating Data
13:56
Manipulating Data 2
09:56
Manipulating Data 3
10:12
Assignment: Pandas Practice
00:52
How To Download The Course Assignments
07:43

NumPy

19 lectures
Section Overview
02:40
NumPy Introduction
05:17
Quick Note: Correction In Next Video
00:41
NumPy DataTypes and Attributes
14:05
Creating NumPy Arrays
09:22
NumPy Random Seed
07:17
Viewing Arrays and Matrices
09:35
Manipulating Arrays
11:31
Manipulating Arrays 2
09:44
Standard Deviation and Variance
07:10
Reshape and Transpose
07:26
Dot Product vs Element Wise
11:45
Exercise: Nut Butter Store Sales
13:04
Comparison Operators
03:33
Sorting Arrays
06:19
Turn Images Into NumPy Arrays
07:37
Exercise: Imposter Syndrome
02:55
Assignment: NumPy Practice
00:56
Optional: Extra NumPy resources
00:26

Matplotlib: Plotting and Data Visualization

20 lectures
Section Overview
01:50
Matplotlib Introduction
05:16
Importing And Using Matplotlib
11:36
Anatomy Of A Matplotlib Figure
09:19
Scatter Plot And Bar Plot
10:09
Histograms And Subplots
08:40
Subplots Option 2
04:15
Quick Tip: Data Visualizations
01:48
Plotting From Pandas DataFrames
05:58
Quick Note: Regular Expressions
00:23
Plotting From Pandas DataFrames 2
10:33
Plotting from Pandas DataFrames 3
08:32
Plotting from Pandas DataFrames 4
06:36
Plotting from Pandas DataFrames 5
08:28
Plotting from Pandas DataFrames 6
08:27
Plotting from Pandas DataFrames 7
11:20
Customizing Your Plots
10:09
Customizing Your Plots 2
09:41
Saving And Sharing Your Plots
04:14
Assignment: Matplotlib Practice
00:51

Scikit-learn: Creating Machine Learning Models

52 lectures
Section Overview
02:29
Scikit-learn Introduction
06:41
Quick Note: Upcoming Video
00:18
Refresher: What Is Machine Learning?
05:40
Quick Note: Upcoming Videos
00:44
Scikit-learn Cheatsheet
06:12
Typical scikit-learn Workflow
23:14
Optional: Debugging Warnings In Jupyter
18:57
Getting Your Data Ready: Splitting Your Data
08:37
Quick Tip: Clean, Transform, Reduce
05:03
Getting Your Data Ready: Convert Data To Numbers
16:54
Note: Update to next video (OneHotEncoder can handle NaN/None values)
00:41
Getting Your Data Ready: Handling Missing Values With Pandas
12:22
Extension: Feature Scaling
01:17
Note: Correction in the upcoming video (splitting data)
00:46
Getting Your Data Ready: Handling Missing Values With Scikit-learn
17:29
NEW: Choosing The Right Model For Your Data
20:14
NEW: Choosing The Right Model For Your Data 2 (Regression)
11:21
Quick Note: Decision Trees
00:08
Quick Tip: How ML Algorithms Work
01:25
Choosing The Right Model For Your Data 3 (Classification)
12:45
Fitting A Model To The Data
06:45
Making Predictions With Our Model
08:24
predict() vs predict_proba()
08:33
NEW: Making Predictions With Our Model (Regression)
08:48
NEW: Evaluating A Machine Learning Model (Score) Part 1
09:41
NEW: Evaluating A Machine Learning Model (Score) Part 2
06:47
Evaluating A Machine Learning Model 2 (Cross Validation)
13:15
Evaluating A Classification Model 1 (Accuracy)
04:46
Evaluating A Classification Model 2 (ROC Curve)
09:04
Evaluating A Classification Model 3 (ROC Curve)
07:44
Reading Extension: ROC Curve + AUC
00:39
Evaluating A Classification Model 4 (Confusion Matrix)
11:01
NEW: Evaluating A Classification Model 5 (Confusion Matrix)
14:22
Evaluating A Classification Model 6 (Classification Report)
10:16
NEW: Evaluating A Regression Model 1 (R2 Score)
09:59
NEW: Evaluating A Regression Model 2 (MAE)
07:22
NEW: Evaluating A Regression Model 3 (MSE)
09:49
Machine Learning Model Evaluation
02:46
NEW: Evaluating A Model With Cross Validation and Scoring Parameter
25:19
NEW: Evaluating A Model With Scikit-learn Functions
14:01
Improving A Machine Learning Model
11:16
Tuning Hyperparameters
23:15
Tuning Hyperparameters 2
14:23
Tuning Hyperparameters 3
14:59
Note: Metric Comparison Improvement
00:49
Quick Tip: Correlation Analysis
02:28
Saving And Loading A Model
07:28
Saving And Loading A Model 2
06:20
Putting It All Together
20:19
Putting It All Together 2
11:34
Scikit-Learn Practice
00:51

Supervised Learning: Classification + Regression

1 lectures
Milestone Projects!
00:16

Milestone Project 1: Supervised Learning (Classification)

25 lectures
Section Overview
02:09
Project Overview
06:09
Project Environment Setup
10:58
Optional: Windows Project Environment Setup
04:52
Step 1~4 Framework Setup
12:06
Note: Code update for next video
00:41
Getting Our Tools Ready
09:04
Exploring Our Data
08:33
Finding Patterns
10:02
Finding Patterns 2
16:47
Finding Patterns 3
13:36
Preparing Our Data For Machine Learning
08:51
Choosing The Right Models
10:15
Experimenting With Machine Learning Models
06:31
Tuning/Improving Our Model
13:49
Tuning Hyperparameters
11:27
Tuning Hyperparameters 2
11:49
Tuning Hyperparameters 3
07:06
Quick Note: Confusion Matrix Labels
00:27
Evaluating Our Model
10:59
Note: Code change in upcoming video
00:42
Evaluating Our Model 2
05:54
Evaluating Our Model 3
08:49
Finding The Most Important Features
16:07
Reviewing The Project
09:13

Milestone Project 2: Supervised Learning (Time Series Data)

21 lectures
Section Overview
01:07
Project Overview
04:24
Downloading the data for the next two projects
00:47
Project Environment Setup
10:52
Step 1~4 Framework Setup
08:36
Exploring Our Data
14:16
Exploring Our Data 2
06:16
Feature Engineering
15:24
Turning Data Into Numbers
15:38
Filling Missing Numerical Values
12:49
Filling Missing Categorical Values
08:27
Fitting A Machine Learning Model
07:16
Splitting Data
10:00
Challenge: What's wrong with splitting data after filling it?
00:59
Custom Evaluation Function
11:13
Reducing Data
10:36
RandomizedSearchCV
09:32
Improving Hyperparameters
08:11
Preproccessing Our Data
13:15
Making Predictions
09:17
Feature Importance
13:50

Data Engineering

13 lectures
Data Engineering Introduction
03:23
What Is Data?
06:42
What Is A Data Engineer?
04:20
What Is A Data Engineer 2?
05:35
What Is A Data Engineer 3?
05:03
What Is A Data Engineer 4?
03:22
Types Of Databases
06:50
Quick Note: Upcoming Video
00:15
Optional: OLTP Databases
10:54
Optional: Learn SQL
00:12
Hadoop, HDFS and MapReduce
04:22
Apache Spark and Apache Flink
02:07
Kafka and Stream Processing
04:33

Neural Networks: Deep Learning, Transfer Learning and TensorFlow 2

44 lectures
Section Overview
02:06
Deep Learning and Unstructured Data
13:36
Setting Up With Google
00:20
Setting Up Google Colab
07:17
Google Colab Workspace
04:23
Uploading Project Data
06:52
Setting Up Our Data
04:40
Setting Up Our Data 2
01:32
Importing TensorFlow 2
12:43
Optional: TensorFlow 2.0 Default Issue
03:38
Using A GPU
08:59
Optional: GPU and Google Colab
04:27
Optional: Reloading Colab Notebook
06:49
Loading Our Data Labels
12:04
Preparing The Images
12:32
Turning Data Labels Into Numbers
12:11
Creating Our Own Validation Set
09:18
Preprocess Images
10:25
Preprocess Images 2
11:00
Turning Data Into Batches
09:37
Turning Data Into Batches 2
17:54
Visualizing Our Data
12:41
Preparing Our Inputs and Outputs
06:37
Optional: How machines learn and what's going on behind the scenes?
01:30
Building A Deep Learning Model
11:42
Building A Deep Learning Model 2
10:53
Building A Deep Learning Model 3
09:05
Building A Deep Learning Model 4
09:12
Summarizing Our Model
04:52
Evaluating Our Model
09:26
Preventing Overfitting
04:19
Training Your Deep Neural Network
19:09
Evaluating Performance With TensorBoard
07:30
Make And Transform Predictions
15:04
Transform Predictions To Text
15:19
Visualizing Model Predictions
14:45
Visualizing And Evaluate Model Predictions 2
15:52
Visualizing And Evaluate Model Predictions 3
10:39
Saving And Loading A Trained Model
13:33
Training Model On Full Dataset
15:01
Making Predictions On Test Images
16:54
Submitting Model to Kaggle
14:14
Making Predictions On Our Images
15:15
Finishing Dog Vision: Where to next?
01:52

Storytelling + Communication: How To Present Your Work

8 lectures
Section Overview
02:19
Communicating Your Work
03:21
Communicating With Managers
02:58
Communicating With Co-Workers
03:42
Weekend Project Principle
06:32
Communicating With Outside World
03:28
Storytelling
03:05
Communicating and sharing your work: Further reading
01:16

Career Advice + Extra Bits

14 lectures
Endorsements On LinkedIn
00:40
Quick Note: Upcoming Video
00:19
What If I Don't Have Enough Experience?
15:02
Learning Guideline
00:10
Quick Note: Upcoming Videos
00:19
JTS: Learn to Learn
01:59
JTS: Start With Why
02:43
Quick Note: Upcoming Videos
00:10
CWD: Git + Github
17:40
CWD: Git + Github 2
16:52
Contributing To Open Source
14:08
Contributing To Open Source 2
09:40
Exercise: Contribute To Open Source
00:47
Coding Challenges
00:32

Learn Python

49 lectures
What Is A Programming Language
06:24
Python Interpreter
07:04
How To Run Python Code
04:53
Latest Version Of Python
01:28
Our First Python Program
07:43
Python 2 vs Python 3
06:40
Exercise: How Does Python Work?
02:09
Learning Python
02:05
Python Data Types
04:45
How To Succeed
00:12
Numbers
11:09
Math Functions
04:29
DEVELOPER FUNDAMENTALS: I
04:07
Operator Precedence
03:10
Exercise: Operator Precedence
00:18
Optional: bin() and complex
04:02
Variables
13:12
Expressions vs Statements
01:36
Augmented Assignment Operator
02:49
Strings
05:29
String Concatenation
01:16
Type Conversion
03:03
Escape Sequences
04:23
Formatted Strings
08:23
String Indexes
08:57
Immutability
03:13
Built-In Functions + Methods
10:03
Booleans
03:21
Exercise: Type Conversion
08:22
DEVELOPER FUNDAMENTALS: II
04:42
Exercise: Password Checker
07:21
Lists
05:01
List Slicing
07:48
Matrix
04:11
List Methods
10:28
List Methods 2
04:24
List Methods 3
04:52
Common List Patterns
05:57
List Unpacking
02:40
None
01:51
Dictionaries
06:20
DEVELOPER FUNDAMENTALS: III
02:40
Dictionary Keys
03:37
Dictionary Methods
04:37
Dictionary Methods 2
07:04
Tuples
04:46
Tuples 2
03:14
Sets
07:24
Sets 2
08:45

Learn Python Part 2

51 lectures
Breaking The Flow
02:34
Conditional Logic
13:17
Indentation In Python
04:38
Truthy vs Falsey
05:17
Ternary Operator
04:14
Short Circuiting
04:02
Logical Operators
06:56
Exercise: Logical Operators
07:47
is vs ==
07:36
For Loops
07:01
Iterables
06:43
Exercise: Tricky Counter
03:23
range()
05:38
enumerate()
04:37
While Loops
06:28
While Loops 2
05:49
break, continue, pass
04:15
Our First GUI
08:48
DEVELOPER FUNDAMENTALS: IV
06:34
Exercise: Find Duplicates
03:54
Functions
07:41
Parameters and Arguments
04:24
Default Parameters and Keyword Arguments
05:40
return
13:11
Exercise: Tesla
00:08
Methods vs Functions
04:33
Docstrings
03:47
Clean Code
04:38
*args and **kwargs
07:56
Exercise: Functions
04:18
Scope
03:37
Scope Rules
06:55
global Keyword
06:13
nonlocal Keyword
03:20
Why Do We Need Scope?
03:38
Pure Functions
09:23
map()
06:30
filter()
04:23
zip()
03:28
reduce()
07:31
List Comprehensions
08:37
Set Comprehensions
06:26
Exercise: Comprehensions
04:36
Python Exam: Testing Your Understanding
00:39
Modules in Python
10:54
Quick Note: Upcoming Videos
00:20
Optional: PyCharm
08:19
Packages in Python
10:45
Different Ways To Import
07:03
Next Steps
00:29
Bonus Resource: Python Cheatsheet
00:11

Extra: Learn Advanced Statistics and Mathematics for FREE!

1 lectures
Statistics and Mathematics
00:17

Where To Go From Here?

5 lectures
Become An Alumni
00:37
Thank You
02:44
Thank You Part 2
00:24
Course Review
1 question
The Final Challenge
1 question

BONUS SECTION

1 lectures
Special Bonus Lecture
00:16

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