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Complete A.I. Machine Learning and Data Science: Zero to Mastery

One of the most popular, highly rated A.I., machine learning and data science bootcamps online. It's also the most modern and up-to-date. Guaranteed. You'll go from complete beginner with no prior experience to getting hired as a Machine Learning Engineer this year.

You'll learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence), Python, Python with Tensorflow, Pandas & more!

3 Months

Average time students take to complete this course.

Last updated: February 2024

Course overview

Learn Data Science and Machine Learning from scratch, get hired, and have fun along the way. We are confident that this is the most comprehensive, modern and up-to-date course you will find on Data Science & Machine Learning (bold statement, we know). Fully updated for 2024, we use the latest version of Python, Tensorflow 2.0, and all the latest industry skills and trends!

What you'll learn

  • Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0
  • Present Data Science projects to management and stakeholders
  • Real life case studies and projects to understand how things are done in the real world
  • Implement Machine Learning algorithms
  • How to improve your Machine Learning models
  • Build a portfolio of work to have on your resume
  • Supervised and Unsupervised Learning
  • Explore large datasets using data visualization tools like Matplotlib and Seaborn
  • Learn NumPy and how it is used in Machine Learning
  • Learn to use the popular library Scikit-learn in your projects
  • Master Machine Learning and how use it on the job
  • Use modern tools that big tech companies like Google, Apple, Amazon and Facebook use
  • Learn which Machine Learning model to choose for each type of problem
  • Learn best practices when it comes to Data Science workflow
  • Learn how to program in Python using the latest Python 3
  • Learn to pre-process data, clean data, and analyze large data
  • Developer Environment setup for Data Science and Machine Learning
  • Machine Learning on Time Series data
  • Explore large datasets and wrangle data using Pandas
  • A portfolio of Data Science and Machine Learning projects to apply for jobs in the industry with all code and notebooks provided
  • Learn about Data Engineering and how tools like Hadoop, Spark, and Kafka are used in the industry
  • Learn how to apply Transfer Learning
  • Learn to perform Classification and Regression modelling

Machine Learning has applications in almost every industry and is becoming more widely used every single year.

  • Marketing
  • Finance
  • Healthcare and Patient Diagnosis
  • Cybersecurity
  • Retail
  • Transportation and Logistics
  • Agriculture
  • Internet of Things
  • Gaming and Entertainment
  • Fraud Detection
  • Anomaly Detection in Manufacturing
  • Government
  • Academia/Research
  • Recommendation Systems
  • Sports (every heard of Moneyball?!)

And so many more. It's everywhere. The skills you'll learn in this course can be applied to all of these industries and give you a lot of options for your career.

Graduates of Zero To Mastery are now working at Google, Tesla, Amazon, Apple, IBM, Uber, Facebook, Shopify + other top tech companies.

They are also working as top freelancers getting paid while working remotely around the world.

This can be you.

By enrolling today, you’ll also get to join our exclusive live online community classroom to learn alongside thousands of students, alumni, mentors, TAs and Instructors.

Most importantly, you will learn data science and machine learning from industry experts that have actual real-world experience having worked for top companies in Silicon Valley, Toronto and Australia.

This course is focused on efficiency so that you never have to waste your time on confusing, out of date or incomplete Machine Learning tutorials anymore.

This comprehensive and project-based course will introduce you to all of the modern skills of a Data Scientist and along the way, you will build many real-world projects to add to your portfolio.

You will also get access to all the code, workbooks and templates (Jupyter Notebooks) on Github, so that you can put them on your portfolio right away!

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 are actually looking for.

The curriculum is very hands-on as we walk you from start to finish of becoming a professional Machine Learning Engineer or Data Scientist.

The course covers 2 tracks:

1️⃣ Programmer: If you already know programming, you can dive right in and skip the section where we teach you Python from scratch.

2️⃣ No Programming Experience: If you're a complete beginner, not to worry. We take you from the very beginning and teach you Python from scratch and how to use it in our real world projects.

Don't worry, you will also be going way beyond the basics.

Once we make sure you know the basics like Machine Learning 101 and Python, we dive deep 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.

You will get experience with full-fledged Data Science and Machine Learning projects and access to bonus programming resources and cheatsheets.

The topics you'll learn in this Data Science & Machine Learning Bootcamp 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
  • and more!

By the end of this course, you will be a complete Data Scientist that can get hired at any company you can imagine.

You are going to use everything you learn in this course to build professional real-world projects like a Heart Disease Detection App, 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.

What's the bottom line?

Here’s the truth: most courses teach you Data Science and that's it.

They show you how to get started, but then you don’t know where to go from there or how to build your own projects.

Or, they'll 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, 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, and get hired 💪.

And you have nothing to lose. You can start learning right now and if this course isn't everything you expected, we'll refund you 100% within 30 days. No hassles and no questions asked.

When's the best time to get started? Today!

There's never a bad time to learn in-demand skills. But the sooner, the better. So start learning data science & machine learning today by joining the ZTM Academy. You'll have a clear roadmap to developing the skills to build your own projects, get hired, and advance your career.

Join Zero To Mastery Now

What you'll build

The best way you learn is by doing. Not just watching endless tutorials. That's why a key part of this course is the real-world projects that you'll get to build. Plus they'll look great on your portfolio.

Heart Disease Detector

Heart Disease Detector

Learn how to build a machine learning-powered classification model to predict whether a patient has heart disease or not based on their health parameters and heart measurements.

Bulldozer Predictor Model

Bulldozer Predictor Model

Take into account the previous sales history of bulldozers to build a machine learning-powered regression model to answer the question, "how much is that bulldozer worth?"

Dog Vision

Dog Vision

Harness the power of computer vision neural networks to build an image classification model capable of predicting over 100 different dog breeds. You'll even be able to try it on your own dog images!

Join Zero To Mastery Now

Don't just take our word for it

Our courses and community have helped 1,000s of Zero To Mastery students go from zero to getting hired to levelling up their skills and advancing their careers to new heights.

Erik Bustos

Andrei's courses are by far the best resources online. I've done 4 of his courses and I can say that he is one of the best teachers. This course is well done, exciting and fun! I highly recommend it.

Erik Bustos

Kevin Okinedo

The course is structured and beginner-friendly. Rather than wasting time on theory, it goes in-depth on the practical application of machine learning then gives you the theory later. If you are looking to become employable in the field, look no further.

Kevin Okinedo

This course is amazing! I’ve learned so much from a practical view and even if you don’t have much knowledge in math/programming, it’s still very approachable. Andrei and Daniel don’t disappoint, it’s worth every penny!!

Jan Montalvo

One of the best DS/ML courses! Often times when you want to learn DS/ML, the field is so vast that you get overwhelmed and confused about where to even begin. But the flow of this course takes all that confusion away and you indeed go from Zero to Mastery.

Luther

Very nice course: well structured & detailed. The explanations are clear & move you step-by-step through the different topics. Andrei & Daniel did really a good job. Thank you so much guys!

Mohamed Benosman

Andrei & Daniel do an excellent job of framing everything in an understandable way. I love Andrei's way of teaching within the context of learning "on the job" through a fictional company and providing expectations of what it's like to work in the field.

Shanay Murdock

Course curriculum

To make sure this course is a good fit for you, you can start learning machine learning and data science for free right now by clicking any of the PREVIEW links below.

Introduction

5 lectures

Complete Machine Learning and Data Science: Zero to Mastery4:10

PREVIEW

Course Outline5:59

PREVIEW

Exercise: Meet Your Classmates and Instructor

BEGIN

Your First Day3:48

PREVIEW

How-to's: Speed up videos, Downloading videos, Subtitles

BEGIN

Machine Learning 101

10 lectures

What Is Machine Learning?6:52

PREVIEW

AI/Machine Learning/Data Science4:51

PREVIEW

Exercise: Machine Learning Playground6:16

PREVIEW

How Did We Get Here?6:03

PREVIEW

Exercise: YouTube Recommendation Engine4:24

PREVIEW

Types of Machine Learning4:41

PREVIEW

Are You Getting It Yet?

PREVIEW

What Is Machine Learning? Round 24:44

PREVIEW

Section Review1:48

PREVIEW

Unlimited Updates

BEGIN

Machine Learning and Data Science Framework

15 lectures

Section Overview3:08

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Introducing Our Framework2:38

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6 Step Machine Learning Framework4:59

PREVIEW

Types of Machine Learning Problems10:32

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Types of Data4:50

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Types of Evaluation3:31

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Features In Data5:22

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Modelling - Splitting Data5:58

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Modelling - Picking the Model4:35

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Modelling - Tuning3:17

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Modelling - Comparison9:32

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Overfitting and Underfitting Definitions

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Experimentation3:35

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Tools We Will Use3:59

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Optional: Elements of AI

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The 2 Paths

3 lectures

The 2 Paths3:27

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Python + Machine Learning Monthly

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Endorsements On LinkedIn

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Data Science Environment Setup

13 lectures

Section Overview1:09

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Introducing Our Tools3:28

PREVIEW

What is Conda?2:35

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Conda Environments4:30

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Mac Environment Setup17:26

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Mac Environment Setup 214:11

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Windows Environment Setup5:17

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Windows Environment Setup 223:17

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Linux Environment Setup

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Sharing your Conda Environment

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Jupyter Notebook Walkthrough10:20

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Jupyter Notebook Walkthrough 216:17

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Jupyter Notebook Walkthrough 38:10

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Pandas: Data Analysis

13 lectures

Section Overview2:27

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Downloading Workbooks and Assignments

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Pandas Introduction4:29

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Series, Data Frames and CSVs13:21

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Data from URLs

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Describing Data with Pandas9:48

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Selecting and Viewing Data with Pandas11:08

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Selecting and Viewing Data with Pandas Part 213:06

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Manipulating Data13:56

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Manipulating Data 29:56

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Manipulating Data 310:12

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Assignment: Pandas Practice

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How To Download The Course Assignments7:43

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NumPy

18 lectures

Section Overview2:40

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NumPy Introduction5:17

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Quick Note: Correction In Next Video

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NumPy DataTypes and Attributes14:05

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Creating NumPy Arrays9:22

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NumPy Random Seed7:17

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Viewing Arrays and Matrices9:35

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Manipulating Arrays11:31

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Manipulating Arrays 29:44

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Standard Deviation and Variance7:10

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Reshape and Transpose7:26

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Dot Product vs Element Wise11:45

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Exercise: Nut Butter Store Sales13:04

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Comparison Operators3:33

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Sorting Arrays6:19

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Turn Images Into NumPy Arrays7:37

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Assignment: NumPy Practice

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Optional: Extra NumPy resources

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Matplotlib: Plotting and Data Visualization

20 lectures

Section Overview1:50

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Matplotlib Introduction5:16

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Importing And Using Matplotlib11:36

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Anatomy Of A Matplotlib Figure9:19

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Scatter Plot And Bar Plot10:09

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Histograms And Subplots8:40

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Subplots Option 24:15

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Quick Tip: Data Visualizations1:48

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Plotting From Pandas DataFrames5:58

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Quick Note: Regular Expressions

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Plotting From Pandas DataFrames 210:33

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Plotting from Pandas DataFrames 38:32

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Plotting from Pandas DataFrames 46:36

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Plotting from Pandas DataFrames 58:28

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Plotting from Pandas DataFrames 68:27

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Plotting from Pandas DataFrames 711:20

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Customizing Your Plots10:09

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Customizing Your Plots 29:41

PREVIEW

Saving And Sharing Your Plots4:14

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Assignment: Matplotlib Practice

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Scikit-learn: Creating Machine Learning Models

52 lectures

Section Overview2:29

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Scikit-learn Introduction6:41

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Quick Note: Upcoming Video

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Refresher: What Is Machine Learning?5:40

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Quick Note: Upcoming Videos

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Scikit-learn Cheatsheet6:12

PREVIEW

Typical scikit-learn Workflow23:14

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Optional: Debugging Warnings In Jupyter18:57

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Getting Your Data Ready: Splitting Your Data8:37

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Quick Tip: Clean, Transform, Reduce5:03

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Getting Your Data Ready: Convert Data To Numbers16:54

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Note: Update to next video (OneHotEncoder can handle NaN/None values)

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Getting Your Data Ready: Handling Missing Values With Pandas12:22

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Extension: Feature Scaling

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Note: Correction in the upcoming video

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Getting Your Data Ready: Handling Missing Values With Scikit-learn17:29

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NEW: Choosing The Right Model For Your Data20:14

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NEW: Choosing The Right Model For Your Data 2 (Regression)11:21

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Quick Note: Decision Trees

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Quick Tip: How ML Algorithms Work1:25

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Choosing The Right Model For Your Data 3 (Classification)12:45

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Fitting A Model To The Data6:45

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Making Predictions With Our Model8:24

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predict() vs predict_proba()8:33

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NEW: Making Predictions With Our Model (Regression)8:48

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NEW: Evaluating A Machine Learning Model (Score) Part 19:41

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NEW: Evaluating A Machine Learning Model (Score) Part 26:47

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Evaluating A Machine Learning Model 2 (Cross Validation)13:16

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Evaluating A Classification Model 1 (Accuracy)4:46

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Evaluating A Classification Model 2 (ROC Curve)9:04

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Evaluating A Classification Model 3 (ROC Curve)7:44

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Reading Extension: ROC Curve + AUC

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Evaluating A Classification Model 4 (Confusion Matrix)11:01

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NEW: Evaluating A Classification Model 5 (Confusion Matrix)14:22

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Evaluating A Classification Model 6 (Classification Report)10:16

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NEW: Evaluating A Regression Model 1 (R2 Score)9:59

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NEW: Evaluating A Regression Model 2 (MAE)7:22

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NEW: Evaluating A Regression Model 3 (MSE)9:49

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Machine Learning Model Evaluation

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NEW: Evaluating A Model With Cross Validation and Scoring Parameter25:19

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NEW: Evaluating A Model With Scikit-learn Functions14:01

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Improving A Machine Learning Model11:16

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Tuning Hyperparameters23:15

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Tuning Hyperparameters 214:23

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Tuning Hyperparameters 314:59

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Note: Metric Comparison Improvement

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Quick Tip: Correlation Analysis2:28

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Saving And Loading A Model7:28

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Saving And Loading A Model 26:20

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Putting It All Together20:19

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Putting It All Together 211:34

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Scikit-Learn Practice

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Supervised Learning: Classification + Regression

1 lectures

Milestone Projects!

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Milestone Project 1: Supervised Learning (Classification)

23 lectures

Section Overview2:09

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Project Overview6:09

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Project Environment Setup10:58

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Step 1~4 Framework Setup12:06

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Getting Our Tools Ready9:04

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Exploring Our Data8:33

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Finding Patterns10:02

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Finding Patterns 216:47

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Finding Patterns 313:36

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Preparing Our Data For Machine Learning8:51

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Choosing The Right Models10:15

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Experimenting With Machine Learning Models6:31

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Tuning/Improving Our Model13:49

PREVIEW

Tuning Hyperparameters11:27

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Tuning Hyperparameters 211:49

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Tuning Hyperparameters 37:06

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Quick Note: Confusion Matrix Labels

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Evaluating Our Model10:59

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Evaluating Our Model 25:54

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Evaluating Our Model 38:49

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Finding The Most Important Features16:07

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Reviewing The Project9:13

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Exercise: Imposter Syndrome2:55

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Milestone Project 2: Supervised Learning (Time Series Data)

21 lectures

Section Overview1:07

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Project Overview4:24

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Downloading the data for the next two projects

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Project Environment Setup10:52

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Step 1~4 Framework Setup8:36

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Exploring Our Data14:16

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Exploring Our Data 26:16

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Feature Engineering15:24

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Turning Data Into Numbers15:38

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Filling Missing Numerical Values12:49

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Filling Missing Categorical Values8:27

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Fitting A Machine Learning Model7:16

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Splitting Data10:00

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Challenge: What's wrong with splitting data after filling it?

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Custom Evaluation Function11:13

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Reducing Data10:36

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RandomizedSearchCV9:32

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Improving Hyperparameters8:11

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Preproccessing Our Data13:15

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Making Predictions9:17

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Feature Importance13:50

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Data Engineering

13 lectures

Data Engineering Introduction3:23

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What Is Data?6:42

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What is a Data Engineer?4:20

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What is A Data Engineer 2?5:35

PREVIEW

What is a Data Engineer 3?5:03

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What is a Data Engineer 4?3:22

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Types of Databases6:50

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Quick Note: Upcoming Video

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Optional: OLTP Databases10:54

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Optional: Learn SQL

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Hadoop, HDFS and MapReduce4:22

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Apache Spark and Apache Flink2:07

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Kafka and Stream Processing4:33

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Neural Networks: Deep Learning, Transfer Learning and TensorFlow 2

44 lectures

Section Overview2:06

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Deep Learning and Unstructured Data13:36

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Setting Up With Google

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Setting Up Google Colab7:17

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Google Colab Workspace4:23

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Uploading Project Data6:52

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Setting Up Our Data4:40

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Setting Up Our Data 21:32

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Importing TensorFlow 212:43

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Optional: TensorFlow 2.0 Default Issue3:38

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Using A GPU8:59

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Optional: GPU and Google Colab4:27

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Optional: Reloading Colab Notebook6:49

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Loading Our Data Labels12:04

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Preparing The Images12:32

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Turning Data Labels Into Numbers12:11

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Creating Our Own Validation Set9:18

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Preprocess Images10:25

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Preprocess Images 211:00

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Turning Data Into Batches9:37

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Turning Data Into Batches 217:54

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Visualizing Our Data12:41

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Preparing Our Inputs and Outputs6:37

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Optional: How machines learn and what's going on behind the scenes?

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Building A Deep Learning Model11:42

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Building A Deep Learning Model 210:53

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Building A Deep Learning Model 39:05

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Building A Deep Learning Model 49:12

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Summarizing Our Model4:52

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Evaluating Our Model9:26

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Preventing Overfitting4:20

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Training Your Deep Neural Network19:09

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Evaluating Performance With TensorBoard7:30

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Make And Transform Predictions15:04

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Transform Predictions To Text15:19

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Visualizing Model Predictions14:46

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Visualizing And Evaluate Model Predictions 215:52

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Visualizing And Evaluate Model Predictions 310:39

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Saving And Loading A Trained Model13:34

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Training Model On Full Dataset15:01

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Making Predictions On Test Images16:54

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Submitting Model to Kaggle14:14

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Making Predictions On Our Images15:15

PREVIEW

Finishing Dog Vision: Where to next?

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Storytelling + Communication: How To Present Your Projects

7 lectures

Section Overview2:19

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Communicating Your Work3:21

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Communicating With Managers2:58

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Communicating With Co-Workers3:42

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Weekend Project Principle6:32

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Communicating With Outside World3:28

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Storytelling3:05

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Career Advice + Extra Bits

14 lectures

Endorsements On LinkedIn

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Quick Note: Upcoming Video

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What If I Don't Have Enough Experience?15:03

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Learning Guideline

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Quick Note: Upcoming Videos

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JTS: Learn to Learn1:59

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JTS: Start With Why2:43

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Quick Note: Upcoming Videos

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CWD: Git + Github17:40

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CWD: Git + Github 216:52

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Contributing To Open Source14:44

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Contributing To Open Source 29:42

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Exercise: Contribute To Open Source

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Coding Challenges

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Learn Python

49 lectures

What Is A Programming Language6:24

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Python Interpreter14:08

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How To Run Python Code9:46

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Our First Python Program7:43

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Latest Version Of Python3:40

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Python 2 vs Python 313:21

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Exercise: How Does Python Work?2:09

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Learning Python2:05

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Python Data Types4:46

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How To Succeed

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Numbers11:09

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Math Functions4:29

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DEVELOPER FUNDAMENTALS: I4:07

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Operator Precedence3:10

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Exercise: Operator Precedence

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Optional: bin() and complex4:02

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Variables13:12

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Expressions vs Statements1:36

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Augmented Assignment Operator2:49

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Strings5:29

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String Concatenation1:16

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Type Conversion3:03

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Escape Sequences4:23

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Formatted Strings8:23

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String Indexes8:57

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Immutability3:13

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Built-In Functions + Methods10:03

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Booleans3:21

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Exercise: Type Conversion8:22

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DEVELOPER FUNDAMENTALS: II4:42

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Exercise: Password Checker7:21

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Lists5:01

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List Slicing7:48

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Matrix4:11

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List Methods10:28

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List Methods 24:24

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List Methods 34:52

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Common List Patterns5:57

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List Unpacking2:40

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None1:51

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Dictionaries6:20

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DEVELOPER FUNDAMENTALS: III2:40

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Dictionary Keys3:37

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Dictionary Methods4:37

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Dictionary Methods 27:04

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Tuples4:46

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Tuples 23:14

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Sets7:24

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Sets 28:45

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Learn Python Part 2

50 lectures

Breaking The Flow2:34

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Conditional Logic13:17

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Indentation In Python4:38

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Truthy vs Falsey5:17

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Ternary Operator4:14

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Short Circuiting4:02

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Logical Operators6:56

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Exercise: Logical Operators7:47

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is vs ==7:36

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For Loops7:01

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Iterables6:43

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Exercise: Tricky Counter3:23

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range()5:38

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enumerate()4:37

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While Loops6:28

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While Loops 25:49

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break, continue, pass4:15

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Our First GUI8:48

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DEVELOPER FUNDAMENTALS: IV6:34

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Exercise: Find Duplicates3:54

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Functions7:41

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Parameters and Arguments4:24

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Default Parameters and Keyword Arguments5:40

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return13:11

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Exercise: Tesla

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Methods vs Functions4:33

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Docstrings3:47

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Clean Code4:38

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*args and **kwargs7:56

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Exercise: Functions4:18

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Scope3:37

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Scope Rules6:55

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global Keyword6:13

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nonlocal Keyword3:21

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Why Do We Need Scope?3:38

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Pure Functions9:23

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map()6:30

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filter()4:23

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zip()3:28

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reduce()7:31

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List Comprehensions8:37

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Set Comprehensions6:26

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Exercise: Comprehensions4:36

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Python Exam: Testing Your Understanding

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Modules in Python10:54

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Quick Note: Upcoming Videos

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Optional: PyCharm8:19

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Packages in Python10:45

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Different Ways To Import7:03

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Next Steps

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Bonus: Learn Advanced Statistics and Mathematics for FREE!

1 lectures

Statistics and Mathematics

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Where To Go From Here?

4 lectures

Become An Alumni

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Thank You2:44

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Review This Course!

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Become a ZTM Ambassador ➡ Refer new students. Earn cash.

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Meet your instructors

Your instructors aren’t just experts with years of real-world professional experience. They have been in your shoes. They make learning fun. They make complex topics feel simple. They will motivate you. They will push you. And they go above and beyond to help you succeed.

Andrei Neagoie

Hi, I'm Andrei Neagoie!

Andrei, lead instructor of Zero To Mastery Academy, has taught 1,000,000+ students worldwide how to code and get hired. ZTM grads work for world-class companies like Apple, Google, Amazon, Tesla, IBM, Facebook, Shopify and many more.

SEE MY BIO & COURSES

Andrei Neagoie

Senior Software Engineer

Daniel Bourke

Hi, I'm Daniel Bourke!

Daniel, a self-taught Machine Learning Engineer, has worked at one of Australia's fastest-growing artificial intelligence agencies, Max Kelsen, and is now using his expertise to teach thousands of students data science and machine learning.

SEE MY BIO & COURSES

Daniel Bourke

Machine Learning Engineer

Frequently asked questions

Are there any prerequisites for this course?

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

Who is this course for?

  • Anyone with zero experience (or beginner/junior) that wants to learn Machine Learning, Data Science, and Python
  • You are a programmer that wants to extend your skills into Data Science and Machine Learning to make yourself more valuable
  • Anyone who wants to learn these topics from industry experts that don’t only teach, but have actually worked in the field
  • You’re looking for one single course to teach you about Machine Learning and Data Science and get you caught up to speed with the industry
  • You want to learn the fundamentals and be able to truly understand the topics instead of just watching somebody code on your screen for hours without really “getting it”
  • You want to learn to use Deep Learning and Neural Networks with your projects
  • You want to add value to your own business or company you work for by using powerful Machine Learning tools

Do you provide a certificate of completion?

We definitely do and they are quite nice. You will also be able to add Zero To Mastery Academy to the education section of your LinkedIn profile as well.

Can I use the course projects in my portfolio?

Yes, you’d be crazy not to in our slightly biased opinion! All projects are downloadable and ready to use the minute you join.

Many of our students tell us the projects they built while following along with our courses were what got them interviews and because they built the projects themselves, they could confidently explain and walk through their work during the interview.

You know what that means? Job offer!

Are there subtitles?

Yes! We have high quality subtitles in 11 different languages: English, Spanish, French, German, Dutch, Romanian, Arabic, Hindi, Portuguese, Indonesian, and Japanese.

You can even adjust the text size, color, background and more so that the subtitles are perfect just for you!

Still have more questions about the Academy?

Still have more questions specific to the Academy membership? No problem, we answer some more here.

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