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Andrei Neagoie and Daniel Bourke

Become a complete A.I., 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 You will go from zero to mastery.

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Become a complete A.I., 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 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 This course is focused on efficiency: never spend time on confusing, out of date, incomplete Machine Learning tutorials anymore.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.. 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.

Enroll now

What's inside

Learning objectives

  • 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
  • Show more
  • Show less

Syllabus

Introduction
Course Outline
Join Our Online Classroom!
Exercise: Meet Your Classmates & Instructor
Read more
Asking Questions + Getting Help
Your First Day
Machine Learning 101
What Is Machine Learning?
AI/Machine Learning/Data Science
ZTM Resources
Exercise: Machine Learning Playground
How Did We Get Here?
Exercise: YouTube Recommendation Engine
Types of Machine Learning
Are You Getting It Yet?
What Is Machine Learning? Round 2
Section Review
Monthly Coding Challenges, Free Resources and Guides
Machine Learning and Data Science Framework
Section Overview
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 - Tuning
Modelling - Comparison
Overfitting and Underfitting Definitions
Experimentation
Tools We Will Use
Optional: Elements of AI
The 2 Paths
Python + Machine Learning Monthly
Endorsements On LinkedIN
Data Science Environment Setup

Note: If you already have Anaconda installed, feel free to keep using it. The following videos will walk-through downloading Miniconda and setting up an environment using Conda.

What is Conda?
Conda Environments
Mac Environment Setup
Mac Environment Setup 2
Windows Environment Setup
Windows Environment Setup 2
Linux Environment Setup
Sharing your Conda Environment
Jupyter Notebook Walkthrough
Jupyter Notebook Walkthrough 2
Jupyter Notebook Walkthrough 3
Pandas: Data Analysis
Downloading Workbooks and Assignments
Pandas Introduction
Series, Data Frames and 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
NumPy
NumPy Introduction
Quick Note: Correction In 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
Turn Images Into NumPy Arrays
Exercise: Imposter Syndrome
Assignment: NumPy Practice
Optional: Extra NumPy resources
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

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Offers professional skill development and deep expertise in Data Science and Machine Learning to launch your career in tech
Taught by recognized industry experts with experience at top tech companies like Google, Tesla, and Amazon
Uses the latest industry-standard tools and software, including Python 3 and Tensorflow 2.0
Covers the полный спектр skills needed for a career in Data Science and Machine Learning, including data exploration, visualization, modeling, and more
Provides numerous real-world projects and case studies to help you gain practical experience and build a portfolio
By the end of the course, you will be able to apply Data Science and Machine Learning to solve real-world problems and impress potential employers

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Complete A.I. & Machine Learning, Data Science Bootcamp with these activities:
Review basic software development skills
This activity will help you brush up on the software development skills you'll need to succeed in this course.
Browse courses on Software Development
Show steps
  • Review your notes from previous software development courses.
  • Work through some basic coding exercises.
  • Complete a small software development project.
Read 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'
This book will provide you with a great foundation in Machine Learning and the tools and libraries used in the industry.
Show steps
  • Purchase or borrow a copy of the book.
  • Read the book.
  • Complete the practice exercises in the book.
Attend a Machine Learning workshop
Attending a Machine Learning workshop will help you learn new skills and techniques from experienced professionals.
Show steps
  • Find a Machine Learning workshop that you're interested in.
  • Register for the workshop.
  • Attend the workshop and participate actively.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Work through programming exercises
Practicing coding challenges will help you solidify your understanding of Machine Learning
Show steps
  • Find online coding challenges or exercises.
  • Attempt to solve the coding challenge on your own.
  • Review the solution to the coding challenge.
Write a blog post or article about a Machine Learning topic
Writing about Machine Learning will help you understand the material
Show steps
  • Choose a topic that you're interested in.
  • Research the topic.
  • Summarize the key findings from your research.
  • Write your article.
  • Publish your article.
Volunteer at a local Machine Learning meetup or conference
Volunteering at a Machine Learning event will help you connect with other people in the field and learn from their experiences.
Show steps
  • Find a local Machine Learning meetup or conference.
  • Contact the organizers and express your interest in volunteering.
  • Help out at the event.
Contribute to an open source Machine Learning project
Contributing to an open source project is a great way to build your skills and knowledge.
Browse courses on Machine Learning Projects
Show steps
  • Find an open source Machine Learning project that you're interested in.
  • Review the project's documentation.
  • Identify an area where you can contribute.
  • Create a pull request with your changes.
  • Collaborate with the project maintainers to get your changes merged.
Build a portfolio of Machine Learning projects
Building projects will help you apply what you've learned and prepare you for a career in Data Science
Browse courses on Machine Learning Projects
Show steps
  • Identify a problem that you can solve with Machine Learning.
  • Gather data and explore it.
  • Build and train a Machine Learning model.
  • Deploy your model and track its performance.

Career center

Learners who complete Complete A.I. & Machine Learning, Data Science Bootcamp will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
As a Machine Learning Engineer, you will be responsible for developing and implementing machine learning models. This course will help you develop the skills you need to succeed in this role, including machine learning algorithms, data analysis, and software engineering. You will also learn how to use popular tools and technologies used by Machine Learning Engineers, such as Python, TensorFlow, and Keras.
Data Scientist
As a Data Scientist, you will be responsible for collecting and analyzing data to help businesses make informed decisions. This course will help you develop the skills you need to succeed in this role, including data analysis, machine learning, and artificial intelligence. You will also learn how to use popular tools and technologies used by Data Scientists, such as Python, R, and SQL.
Data Analyst
As a Data Analyst, you will be responsible for collecting, cleaning, and analyzing data to help businesses make informed decisions. This course will help you develop the skills you need to succeed in this role, including data analysis, data visualization, and statistical modeling. You will also learn how to use popular tools and technologies used by Data Analysts, such as Python, SQL, and Excel.
Software Engineer
As a Software Engineer, you will be responsible for designing, developing, and testing software applications. This course will help you develop the skills you need to succeed in this role, including programming languages, software design, and software testing. You will also learn how to use popular tools and technologies used by Software Engineers, such as Python, Java, and C++.
Quant Analyst
As a Quant Analyst, you will be responsible for developing and implementing mathematical models to help businesses make informed decisions. This course will help you develop the skills you need to succeed in this role, including financial modeling, data analysis, and statistical modeling. You will also learn how to use popular tools and technologies used by Quant Analysts, such as Python, R, and SQL.
Business Analyst
As a Business Analyst, you will be responsible for analyzing business processes and identifying opportunities for improvement. This course will help you develop the skills you need to succeed in this role, including business analysis techniques, data analysis, and communication skills.
Data Engineer
As a Data Engineer, you will be responsible for building and maintaining data pipelines. This course will help you develop the skills you need to succeed in this role, including data engineering tools and technologies, data architecture, and data security.
Financial Analyst
As a Financial Analyst, you will be responsible for analyzing financial data and making recommendations to investors. This course will help you develop the skills you need to succeed in this role, including financial modeling, data analysis, and communication skills.
Statistician
As a Statistician, you will be responsible for collecting, analyzing, and interpreting data. This course will help you develop the skills you need to succeed in this role, including statistical modeling, data analysis, and communication skills.
Operations Research Analyst
As an Operations Research Analyst, you will be responsible for developing and implementing mathematical models to improve business operations. This course will help you develop the skills you need to succeed in this role, including operations research techniques, data analysis, and modeling skills.
Risk Analyst
As a Risk Analyst, you will be responsible for identifying and assessing risks. This course will help you develop the skills you need to succeed in this role, including risk management techniques, data analysis, and communication skills.
Actuary
As an Actuary, you will be responsible for assessing and managing financial risks. This course will help you develop the skills you need to succeed in this role, including actuarial science techniques, data analysis, and modeling skills.
Data Science Manager
As a Data Science Manager, you will be responsible for leading a team of data scientists and ensuring that the team is successful. This course will help you develop the skills you need to succeed in this role, including leadership skills, project management skills, and communication skills.
Professor
As a Professor, you will be responsible for teaching and conducting research in the field of Data Science. This course may be useful for you if you are interested in pursuing a career in academia.
Data Science Consultant
As a Data Science Consultant, you will be responsible for providing data science services to clients. This course may be useful for you if you are interested in starting your own business or working as a consultant.

Reading list

We've selected ten books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Complete A.I. & Machine Learning, Data Science Bootcamp.
Comprehensive guide to deep learning, covering topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for both beginners and experienced practitioners.
Provides a comprehensive guide to data science using Python. It covers topics such as data cleaning, data visualization, and machine learning. It valuable resource for both beginners and experienced practitioners.
Provides a comprehensive guide to causal inference in statistics. It covers topics such as graphical models, structural equation modeling, and counterfactuals. It valuable resource for both beginners and experienced practitioners.
Provides a comprehensive guide to deep learning for natural language processing. It covers topics such as text classification, machine translation, and text generation. It valuable resource for both beginners and experienced practitioners.
Provides a comprehensive guide to reinforcement learning. It covers topics such as Markov decision processes, value functions, and policy iteration. It valuable resource for both beginners and experienced practitioners.
Provides a practical guide to machine learning using popular libraries such as Scikit-Learn, Keras, and TensorFlow. It valuable resource for beginners who want to get started with machine learning.
Provides a practical guide to machine learning using Python. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for beginners who want to get started with machine learning.
Provides a practical guide to data science for business. It covers topics such as data mining, machine learning, and data visualization. It valuable resource for both beginners and experienced practitioners.
Provides a gentle introduction to data science. It covers topics such as data cleaning, data visualization, and machine learning. It valuable resource for beginners who want to get started with data science.

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