The Complete Machine Learning Online Course 4.0 + Bundle
Enroll in the most complete course bundle that teaches ABSOLUTELY EVERYONE to code in Python and master machine learning models!
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📈 Do you want a recession-proof job?
Do you want a job that is always in demand?
Do you want to work from home? 🏡
This Kickstarter will turn all your dreams into reality!
🐦 Get an early-bird rate for this 100-hour masterclass.
- Become a highly desirable programmer by mastering machine learning.
- Build exciting computer vision applications and more!
🌎 Join 1.4+ million students in high-quality courses featured at Harvard
- Lifetime access that never expires
- Project-based curriculum to superboost your portfolio
- Graduation certificate for every course
- Absolute beginner-friendly
▶️ Go from A to B as quickly as possible with action-packed video lessons.
- Build practical projects you can add to your resume
- Learn faster than anywhere else (we don't overteach theory)
- Get straight to the point with our crystal-clear tutorials
📙 Successful people are always learning.
Do you like to invest?
This course is the best self development investment you'll ever make.
You'll master all the ins and outs of crossing industries including tech, artificial intelligence, blockchain, robotics and MORE!
Ride the data science wave 🌊
Data science is the #1 skill to learn this year. Don't miss your chance to be the first to learn an emerging and growing technology.
Even if you're not a coder, you can learn the secrets of machine learning.
🧠 Machine learning will always be in demand
Build the next big machine learning app!
✔️ Learn how to:
- build 70 machine learning projects
- add machine learning and data science to your resume
🎁 This bundle:
- does not assume any level of experience
- is perfect for beginners
THE COMPLETE SOURCE CODE WILL BE AVAILABLE.
Level 1 - Learn to Code in Python
No math or programming experience necessary.
- Start your coding journey with a top 3 language.
- Build and run simple projects.
- Learn the fundamentals of computer programming.
Level 2 - Data Science
Learn how to analyze data, visualize data, and get valuable information, insights and predictions from datasets.
✔️ Learn how to use popular Python libraries:
Build fast, efficient NumPy arrays for Machine Learning and Data Science
Visualize data with Matplotlib's PyPlot
Build dataframes with Pandas for machine learning and data science
✔️ Beginners R Programming: Data Science and Machine Learning!
✔️ R Programming: Practical Data Science and Modeling
Level 3 - Introduction to Machine Learning
✔️ Machine Learning Theory for Business
- What is machine learning
- What machine learning can and cannot do
- Machine learning model examples
✔️ Microsoft Certified Azure Data Scientist Associate Preparation
- Build a cluster and pipeline in Azure Machine Learning
- Build a dataset in Microsoft Azure ML Studio
- Build a regression machine learning model with Azure Machine Learning
✔️ Google Cloud Professional Machine Learning Engineer Certification
- Introduction to Cloud Computing for Machine Learning
- Image classification with AutoML and Vertex AI in Google Cloud
- Query and visualize data with BigQuery SQL
✔️ Machine Learning Fundamentals
- Probability and statistics for machine leanring
- Distributions in machine learning
- Machine learning optimization
✔️ Data Engineering and Machine Learning Masterclass
- Load, clean and encode data
- Build regression and discretizer models
- Data transformation and feature selection
✔️ Build Machine Learning Models
- Image Recognition with MNIST dataset and Python
- AI Uninformed Search Algorithms
- Build regression and classification models with Python
Level 4 - Deep Learning and Neural Networks
✔️ Creative Machine Learning - Draw and Paint with 3 Neural Network Projects
- Train and test generator and discriminator models
- Transfer image styles with machine learning
- Approximate images with a neural network
✔️ The Deep Learning Masterclass - Convert Sketch to Photo
- Process photo and sketch image data
- Build a generative neural network to create images
- Build a discriminator neural network to classify images
✔️ Machine Learning and Deep Learning for Biology with Python and TensorFlow
- Predict Diabetes - Build Regression Machine Learning Models
- Cluster Blood Cells - Component analysis on fluorescent intensities
- Detect Cancer - Build models to classify malignant vs benign masses
- Heart Disease - Predict disease with machine learning
- Detect Malaria - Build a neural network with TensorFlow 2.0
✔️ Build Neural Networks with Python
- Linear algebra for deep learning
- Build convolutional neural networks for image classification
- Build a recurrent neural network
- Classify emotional sentiment of text
✔️ Text to Speech with Python Machine Learning, Deep Learning and Neural Networks
Dive into deep learning and master highly desirable skills.
- Add projects to your resume in no time.
- Learn a hireable skill and powerful technology
- Help businesses find customer trends, leverage data to cut costs, and much more.
Level 5 - Computer Vision
✔️ Computer Vision and Deep Learning with OpenCV and Python - Build 15 Projects
Take a look at some of the applications you will build in this level:
In this level, you'll build 15 projects from beginner to advanced:
1) Process and Enhance Images
- Manipulate Images with OpenCV
- Basic Image Operations
- Analyze Images with OpenCV
- Build your first neural network with OpenCV
- And more
2) Process Videos
- Outline objects in a video
- Detect faces and eyes in a video
- Detect lanes for autonomous vehicle computer vision
- Build a motion alert video monitoring system
- Detect emotion in a video
- And more
3) Augmented Reality
- Swap faces with machine learning
Level 6 - Machine Learning App Development
✔️ CoreML SwiftUI Masterclass - Machine Learning App Development
✔️ Python and Android TensorFlow Lite - Machine Learning for App Development
✔️ Beginners Machine Learning Masterclass with Tensorflow JS
- Introduction to HTML
- Introduction to CSS
- Introduction to JavaScript
- Build Your First Tensors
- Visualize Data
- Train a Simple Model
- Generate and Visualize Data
- Build a Linear Regression Model
- Visualize Linear Regression with User Input
- Visualize Polynomial Regression with User Input
- Build a polynomial regression machine learning model
- K Nearest Neighbors Image Classification with Tensorflow JS
✔️ Beginners Guide to Neural Networks in Tensorflow JS
- Build Neural Network Components
- Build a Neural Network with Cross Validation
- Image Classification with a Neural Network
- Build a Neural Network for the XOR Algorithm
- Use Recurrent Neural Networks with Tensorflow JS
- Detect Objects in Images with a Neural Network
- Build a Deep Neural Network with Backpropagation
- Build a Neural Network with Gradient Descent
✔️ Advanced Machine Learning with TensorFlow.js
- Identify Text Toxicity Scores
- Build a Speech Recognition Drawing Site
- Manage TensorFlow Memory
- Build a Housing Linear Regression Project
- Build a Model on a Large Dataset
- Build a Logistic Regression Model
- Visualize Fast Fourier Transform
- Visualize Principal Component Analysis
✔️ Advanced Neural Networks with TensorFlow.js
- Build a Neural Network with One Hot Encoding
- Build a Neural Network to Detect Lines in Images
- Build an LSTM Recurrent Neural Network
- Build a Model to Classify Iris Species
- Build a Neural Network to Recognize Handwriting
- Build a Positive vs Negative Text Classifier
Level 7 - Blockchain Machine Learning
✔️ Python Crypto Trading Machine Learning
✔️ Python SQL Ethereum Data Science with Google BigQuery
✔️ Blockchain and Cryptocurrency Machine Learning - Build 12 Models, Decentralized Federated Learning and More
🛍 Get a pre-order price ONLY NOW.
This bundle is currently $37 USD only on Kickstarter. After, the bundle will be sold for a MINIMUM of $99 USD.
Requirements
- No programming or machine learning experience needed - We'll teach you everything you need to know.
- Any computer.
- We'll walk you through, step-by-step how to install and set up all software.
Welcome to The Complete Python and Machine Learning Course for Everybody 4.0, the only course you need to learn Machine Learning. With over 50,000 reviews, our courses are some of the HIGHEST RATED courses online!
This masterclass is without a doubt the most comprehensive course available anywhere online. Even if you have zero experience, this course will take you from beginner to professional.
We want to add more courses to this bundle and take your requests. Help our small business by pledging now.
🎉 Don't Miss Out
- This bundle is a taught by 5+ instructors with decades of experience.
- We've taught 1.3 million+ students how to code and many have gone on to become professional developers or start their own tech startup.
- You'll save $100,000, the average cost of 5 machine learning bootcamps. You'll learn completely online at your own pace. You'll get lifetime access to content that never expires.
- The course has been updated to be 2023 ready. You'll learn the latest tools and technologies used at large companies such as Google, Microsoft and Amazon.
We'll take you step-by-step through engaging video tutorials and teach you everything you need to know to succeed as a data analyst, machine learning specialist or similar.
⭐⭐⭐⭐⭐ Testimonials ⭐⭐⭐⭐⭐
📦 Sign up today, and look forward to:
- 100+ hours of HD Video Lectures
- Easy to view on mobile
- Source code
- Fully Fledged Projects
- Resources and Downloads
💻 Learn to build projects in:
- NumPy
- Matplotlib
- Financial analysis
- Microsoft Azure
- Google Cloud
- Google BigQuery
- SQL
- Data Engineering
- Image Classification
- Data analysis
- Data visualization
- Image transfer
- Text to speech conversion
- Computer vision
- Motion detection
- Self driving cars
- Emotion detection
- Facial recognition
- Augmented reality
- Image correction
- CoreML
- Swift
- SwiftUI
- Xcode
- Android Studio
- Kotlin
- XML
- TensorFlow Lite
- TensorFlow JS
- HTML
- CSS
- JavaScript
- Handwriting recognition
- Biological machine learning
- Linear algebra
- Probability
- Statistics
- R programming
- Data modeling
- Cryptocurrency
- Blockchain
- Artificial intelligence
- Python programming
- Decentralized machine learning
- Predicting stocks
- Charts
- Google's TensorFlow Python
- Differential privacy
- OpenPyXL automation
- Neural networks
- Deep learning
- Pandas
- Scikit
- Much more
📣 Frequently Asked Questions
How do I obtain a certificate?
Each certificate in this bundle is only awarded after you complete every lecture of the course.
Many of our students post their Mammoth Interactive certifications on LinkedIn. Not only that, but you will have projects to show employers on top of the certification.
Is this an eBook or videos?
The majority of this course bundle will be video tutorials (screencasts of practical coding projects step by step.) You will also get PDFs and ALL SOURCE CODE!
Can't I just learn via YouTube?
YouTube tutorials prioritize clickbait, shock factor, and hacking the recommendation algorithm. This makes it hard to find quality content.
Our online courses are completely about education. You'll be taken from absolute beginner to advanced programmer. With no ads, clickbait or shock factor.
This bundle is much more streamlined and efficient than learning via Google or YouTube. We have curated a massive curriculum to take you from zero to starting a high-paying career.
How will I practice to ensure I'm learning?
With each section there will be a project, so if you can build the project along with us you are succeeding. There is also a challenge at the end of each section that you can take on to add more features to the project and advance the project in your own time.
Read more FAQs in the FAQ tab.
🌐 About Mammoth Interactive
Mammoth Interactive is a leading online course provider in everything from learning to code to becoming a YouTube star. Mammoth Interactive courses have been featured on Harvard’s edX, Business Insider and more.
Over 11 years, Mammoth Interactive has built a global student community with 1.3 million courses sold. Mammoth Interactive has released over 250 courses and 2,500 hours of video content.
Founder and CEO John Bura has been programming since 1997 and teaching since 2002. John has created top-selling applications for iOS, Xbox and more.
🤝 Trusted Small Business with 34 Kickstarter Campaigns
🔓 Unlock Your Completion Certificate
Upon completing each course in this bundle, you'll receive a Completion Certificate. You can feature this certificate on your resume and LinkedIn.
Your Instructor
Alexandra Kropf is Mammoth Interactive's CLO and a software developer with extensive experience in full-stack web development, app development and game development. She has helped produce courses for Mammoth Interactive since 2016, including the Coding Interview series in Java, JavaScript, C++, C#, Python and Swift.
Mammoth Interactive is a leading online course provider in everything from learning to code to becoming a YouTube star. Mammoth Interactive courses have been featured on Harvard’s edX, Business Insider and more.
Over 12 years, Mammoth Interactive has built a global student community with 4 million courses sold. Mammoth Interactive has released over 350 courses and 3,500 hours of video content.
Founder and CEO John Bura has been programming since 1997 and teaching
since 2002. John has created top-selling applications for iOS, Xbox and
more. John also runs SaaS company Devonian Apps, building
efficiency-minded software for technology workers like you.
Course Curriculum
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Start00. Intro To Course And Python (9:57)
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Start01. Variables (19:19)
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Start02. Type Conversion Examples (10:06)
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Start03. Operators (28:54)
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Start04. Collections (8:25)
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Start05. List Examples (19:41)
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Start06. Tuples Examples (8:36)
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Start07. Dictionaries Examples (14:26)
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Start08. Ranges Examples (8:32)
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Start09. Conditionals (6:43)
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Start10. If Statement Examples (21:32)
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Start11. Loops (29:42)
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Start12. Functions (17:01)
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Start13. Parameters And Return Values Examples (13:54)
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Start14. Classes And Objects (34:11)
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Start15. Inheritance Examples (17:29)
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Start16. Static Members Examples (11:05)
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Start17. Summary And Outro (4:08)
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Start00. Course Intro (5:11)
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Start01. Intro to Numpy (6:20)
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Start02. Installing Numpy (3:59)
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Start03. Creating Numpy Arrays (16:55)
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Start04. Creating Numpy Matrices (11:57)
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Start05. Getting and Setting Numpy Elements (16:59)
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Start06. Arithmetic Operations on Numpy Arrays (11:56)
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Start07. Numpy Functions Part 1 (19:13)
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Start08. Numpy Functions Part 2 (12:36)
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Start09. Summary and Outro (3:01)
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StartSource Files
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Start00. Course Intro.mp4 (6:05)
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Start01. Quick Intro to Machine Learning (9:01)
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Start02. Deep Dive into Machine Learning (6:01)
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Start03. Problems Solved with Machine Learning Part 1 (13:26)
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Start04. Problems Solved with Machine Learning Part 2 (16:25)
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Start05. Types of Machine Learning (10:15)
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Start06. How Machine Learning Works (11:40)
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Start07. Common Machine Learning Structures (13:51)
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Start08. Steps to Build a Machine Learning Program (16:34)
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Start09. Summary and Outro (2:49)
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StartIntro to Machine Learning Slides
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Start00. Course Intro (6:10)
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Start01. Intro to Tensorflow.mov (5:33)
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Start02. Installing Tensorflow (3:52)
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Start03. Intro to Linear Regression (9:26)
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Start04. Linear Regression Model - Creating Dataset (5:49)
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Start05. Linear Regression Model - Building the Model (7:22)
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Start06. Linear Regression Model - Creating a Loss Function (5:57)
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Start07. Linear Regression Model - Training the Model (12:42)
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Start08. Linear Regression Model - Testing the Model (5:22)
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Start09. Summary and Outro (2:55)
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StartIntro to Tensorflow Slides
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StartLinear_Regression
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StartSource Files
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Start00. Course Intro (6:19)
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Start01. How Machines Interpret Text (15:23)
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Start02. Building the Model Part 1 - Examining Dataset (12:27)
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Start03. Building the Model Part 2 - Formatting Dataset (15:14)
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Start04. Building the Model Part 3 - Building the Model (10:30)
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Start05. Building the Model Part 4 - Training the Model (5:42)
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Start06. Building the Model Part 5 - Testing the Model.mp4 (9:26)
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Start07. Course Summary and Outro (3:29)
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Start00. Course Intro (5:30)
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Start01. Intro to Pyplot (5:11)
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Start02. Installing Matplotlib (5:51)
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Start03. Basic Line Plot (7:53)
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Start04. Customizing Graphs (10:47)
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Start05. Plotting Multiple Datasets (8:10)
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Start06. Bar Chart (6:26)
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Start07. Pie Chart (9:13)
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Start08. Histogram (10:14)
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Start09. 3D Plotting (6:28)
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Start10. Course Outro (4:09)
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StartPyplot Code
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Start00. Panda Course Introduction (5:43)
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Start01. Intro to Pandas (7:55)
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Start02. Installing Pandas (5:28)
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Start03. Creating Pandas Series (20:34)
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Start04. Date Ranges (11:29)
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Start05. Getting Elements from Series (19:21)
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Start06. Getting Properties of Series (13:04)
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Start07. Modifying Series (19:02)
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Start08. Operations on Series (11:48)
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Start09. Creating Pandas DataFrames (22:57)
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Start10. Getting Elements from DataFrames (25:12)
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Start11. Getting Properties from DataFrames (17:44)
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Start12. Dataframe Modification (36:24)
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Start13. DataFrame Operations (20:09)
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Start14 DataFrame Comparisons and Iteration
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Start15. Reading CSVs (12:00)
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Start16.Summary and Outro (4:14)
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StartSource Files
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Start1) 1st Hour - Course Overview and Data Setup (57:35)
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Start2) 2nd Hour - Functions in R (54:57)
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Start3) 3rd Hour - Regression Model (63:39)
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Start4) 4th Hour - Regression Models Continued and Classification Models (57:04)
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Start5) 5th Hour - Classification Models Continued, RMark Down and Excel (78:31)
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StartSource Code
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Start00 What Is Machine Learning (5:26)
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Start01 Hash Table Or Dictionary Visualized With Time And Space Complexity (4:19)
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Start02 Types Of Machine Learning (12:09)
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Start03 What Is Supervised Learning (9:59)
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Start04 What Is Unsupervised Learning (7:43)
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Start05 How Does A Machine Learning Agent Learn (7:38)
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Start06 Performance Of A Machine Learning Algorithm (4:14)
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Start01 Why use the cloud for machine learning (2:38)
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Start02 Benefits of cloud computing- (1:23)
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Start03 Public vs private cloud computing (3:18)
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Start04 Managed vs unmanaged cloud computing (1:30)
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Start05 IaaS vs PaaS vs SaaS in cloud computing (3:33)
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Start06 Google Cloud vs AWS vs Azure for Machine Learning (3:32)
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StartSource Files
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Start01 Uniform Distribution (5:25)
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Start02 Gaussian Distribution (3:45)
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Start03 Log-Normal Distribution (3:28)
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Start04 Exponential Distribution (3:04)
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Start05 Laplace Distribution (1:54)
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Start06 Binomial Distribution (9:05)
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Start07 Multinomial Distribution (3:59)
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Start08 Poisson Distribution (4:21)
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StartSource Files
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Start01 Compare Decision Tree And Linear Regression Models (6:26)
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Start01C What Is The Kbins Discretizer (4:54)
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Start02 Bin Data With Kbins Discretizer (3:42)
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Start03 Compare Binned Regression Models (3:39)
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Start04 Build A Linear Regression Model On Stacked Data (3:20)
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Start05A What Is K Means Clustering (11:58)
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Start01 Build Univariate Nonlinear Transformatio (1:55)
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Start01 What Is Gaussian Probability Distribution- (2:31)
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Start01B What Is Poisson Distribution (1:08)
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Start02 Build X and Y Data With Poisson Distribution In Numpy (3:34)
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Start02C What Is Logarithmic Data Transformation (2:34)
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Start03 Build A Ridge Regression Model (3:41)
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Start00. Course Intro (6:57)
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Start01. Intro to Image Recognition (6:40)
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Start02. Intro to MNIST (4:42)
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Start03. Building a CNN Part 1 - Obtaining Data (15:40)
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Start04. Building a CNN Part 2 - Building the Model
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Start05. Building a CNN Part 3 - Adding Loss and Optimizer Functions (4:57)
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Start06. Building a CNN Part 4 - Train and Test Functions (10:58)
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Start07. Building a CNN Part 5 - Train and Test the Model (9:17)
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Start08. MNIST Image Recognition with Keras Sequential Model (13:24)
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Start09. Summary and Outro (2:55)
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StartImage Recognition with MNIST
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StartSource Files
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Start01 What Are Search Algorithms (7:21)
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Start02 Depth First Search (9:00)
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Start02b Build A Depth First Search Algorithm (8:26)
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Start03 What Is Breadth First Search (BFS) (5:08)
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Start03b Build A Breadth First Search Algorithm (6:56)
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Start04 Depth Limited Search (3:58)
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Start05 Iterative Deepening Depth First Search (5:32)
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Start06 What Is Uniform Cost Search (6:04)
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Start06b Build A Uniform Cost Search Algorithm (8:07)
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Start07 Bidirectional Search (4:44)
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StartSource Files
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Start01 How Does A Machine Learning Agent Learn (7:37)
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Start02 What Is Inductive Learning (4:10)
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Start03 Make Decisions With Decision Trees-3 (10:50)
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Start04 Performance Of A Machine Learning Algorithm-4 (4:13)
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Start05 Handle Noise In Data-5 (5:20)
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Start06 Statistical Learning-6 (3:56)
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StartSource Files
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Start05.01 What Is Logistic Regression-1 (4:26)
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Start05.03 Prepare Data For Logistic Regression-2 (12:19)
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Start05.03a How To Prepare Data-3 (8:52)
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Start05.04 Build A Logistic Regression Model-4
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Start05.04a How To Build A Logistic Regression Model-5 (3:28)
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Start05.04b What Is Optimization-6 (12:10)
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Start05.05 Optimize The Logistic Regression Model-7 (12:44)
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Start05.05a How To Optimize A Logistic Regression Model-8
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Start05.06 Train The Model-9 (10:09)
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Start05.07 Test The Model-10
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Start05.08 Visualize Results-11 (5:38)
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Start05 Source Files
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Start06.01 What Is Gradient Boosting-1 (1:54)
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Start06.02 Prepare Data For Gradient Boosted Classification-2 (7:19)
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Start06.03 Build Binary Classes-3 (6:12)
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Start06.04a How To Shape Data For Classification-4 (2:58)
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Start06.04b Shape Data For Classification-5 (7:06)
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Start06.05a How To Build A Boosted Trees Classifier-6 (4:03)
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Start06.05b Build A Boosted Trees Classifier-7 (4:37)
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Start06 Source Files
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Start01 What Is A Generative Neural Network (7:26)
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Start02 What Is A Convolutional Neural Network (7:04)
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Start03 How To Build A Convolutional Neural Network (14:04)
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Start04 How To Build A Dense Layer (2:42)
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Start05 How To Build A Batch Normalization Layer (1:52)
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Start06 Leaky Relu Activation Function (6:04)
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Start07 Transposed Convolution Layer (5:17)
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Start08 Hyperbolic Tangent (Tanh) Activation Function (2:59)
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StartSource Files
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Start00 Style Transfer Project Overview (5:36)
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Start01 Load The Model (4:57)
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Start02 Load Images (6:53)
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Start03 Reformat Image For Machine Learning (7:03)
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Start04 Load Original And Style Images (6:27)
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Start05 Display Processed Images (10:58)
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Start06 Extract Image Features (6:59)
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Start07 Calculate The Style Representation (6:01)
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Start08 Optimize The Model (5:27)
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Start09 Use Machine Learning To Transfer Image Style (13:54)
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StartSource Files
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Start01 Load And Process Image (7:14)
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Start02 Build A Training Dataset (6:49)
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Start03 Visualize Training Dataset (5:36)
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Start04 Build A Testing Dataset (4:04)
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Start05 Build A Neural Network (7:25)
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Start06 Train The Neural Network (4:40)
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Start07 Visualize Image Approximation Results (5:14)
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StartSource Files
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Start00 Project Preview (2:56)
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Start01 Load And Analyze Blood Cell Data (10:39)
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Start02 Clean Data With Missing Values (12:28)
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Start03 Process Data For Machine Learning (8:53)
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Start04A What Is Principal Component Analysis (7:27)
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Start04B Reduce Data Dimensionality With Principal Component Analysis (4:55)
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StartSource Files
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Start01A What Is Cross Validation (8:25)
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Start01B Find Model Error With Cross Validation (3:46)
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Start02A What Is Grid Search Cross Validation (5:47)
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Start02B Find Optimal Hyperparameters With Grid Search (9:37)
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Start03A What Is Nested Cross Validation (14:29)
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Start03B Find Best Model Parameters With Nested Cross Validation (4:43)
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Start04A What Is The Decision Tree Model (10:51)
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Start04B Compare Models With Nested Cross Validation (4:00)
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StartSource Files
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Start00. Intro And Demo-1 (6:48)
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Start01. General Interface Intro-2 (15:06)
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Start02. File System Introduction-3 (13:24)
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Start03. Viewcontroller Intro-4 (6:53)
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Start04. Storyboard File Intro-5 (17:28)
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Start05. Connecting Outlets And Actions-6 (14:12)
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Start06. Running An Application-7 (10:06)
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Start07. Debugging An Application-8 (11:40)
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StartXCode Intro
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Start00. Language Basics Topics List (5:14)
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Start00. Learning Goals (4:24)
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Start01. Intro To Variables And Constants (16:16)
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Start02. Primitive Types (19:07)
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Start03. Strings (19:11)
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Start04. Nil Values (13:16)
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Start05. Tuples (14:39)
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Start06. Type Conversions (23:40)
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Start07. Assignment Operators (11:43)
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Start08. Conditional Operators (12:51)
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StartVariables and Constants Text.playground
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Start00. Topics List And Learning Objectives (4:06)
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Start01. Intro To If And Else Statements (10:07)
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Start02. Else If Statements (9:13)
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Start03. Multiple Simultaneous Tests (12:58)
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Start04. Intro To Switch Statements (9:47)
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Start05. Advanced Switch Statement Techniques (16:25)
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Start06. Testing For Nil Values (12:15)
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Start07. Intro To While Loops (14:51)
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Start08A. Intro To For...In Loops (14:39)
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Start08B Intro To For...In Loops (Cont'd) (11:19)
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Start09. Complex Loops And Loop Control Statements (20:05)
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StartControl Flow Text.playground
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Start00. Topics List And Learning Objectives (4:16)
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Start01. Intro To Functions (20:19)
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Start02. Function Parameters (12:01)
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Start03. Return Statements (14:26)
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Start04A. Parameter Variations - Argument Labels (9:23)
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Start04B. Parameter Variations - Default Values (5:50)
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Start04C. Parameters Variations - Inout Parameters (9:03)
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Start04D. Parameter Variations - Variadic Parameters (11:12)
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Start05. Returning Multiple Values Simultaneously (7:46)
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StartFunctions Text.playground
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Start00. Topics List And Learning Objectives (5:25)
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Start01. Intro To Classes (16:24)
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Start02A. Properties As Fields - Add To Class Implementation (13:43)
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Start02B. Custom Getters And Setters (8:44)
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Start02C. Calculated Properties (24:12)
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Start02D. Variable Scope And Self (13:15)
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Start02E. Lazy And Static Variables (14:35)
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Start03A. Behaviour And Instance Methods (16:38)
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Start03B. Class Type Methods (7:42)
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Start04. Class Instances As Field Variables (8:52)
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Start05A. Inheritance, Subclassing And Superclassing (24:06)
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Start05B. Overriding Initializers (13:41)
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Start05C. Overriding Properties (16:30)
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Start05D. Overriding Methods (10:33)
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Start06. Structs Overview (20:24)
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Start07. Enumerations (16:30)
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Start08. Comparisons Between Classes, Structs And Enums (14:40)
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StartSource files
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Start01 Build A Text Object-1 (9:25)
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Start02 Build An Image Object-2 (3:41)
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Start03 Add An Image From The Web-3 (3:32)
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Start04 Add An Image From The Web-4 (10:32)
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Start05 Build A Button-5 (5:07)
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Start06 Build A Toggle Button-6 (7:09)
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Start07 Build A Slider-7 (9:09)
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Start08 Build A View From A Collection-8 (7:10)
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StartSource Files
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Start01 Load A CoreML Model Into A New Xcode Project (11:00)
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Start02 Add Images For Classification (6:31)
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Start03 Enable User To Loop Through Image (5:40)
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Start04 Import CoreML Model Into The View (5:28)
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Start05 Resize Image For Model (6:26)
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Start05A Resizing Image Overview (7:44)
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Start06 Convert Image To Buffer For Model (8:55)
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Start06A Image To Buffer Overview (6:55)
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Start07 Test The Model On Image Classification (14:31)
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StartSource files
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Start00 Tip - How To Unhide Library Folder (1:22)
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Start01 Build A New Xcode Project To Compile Model (4:44)
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Start02 Build A Playground With Object Detection Model (4:28)
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Start03 Instantiate A Model Object (6:12)
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Start04 Build An Image Analysis Request (7:23)
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Start05 Resize Image For Model (9:36)
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Start06 Convert Image To Buffer For Model (9:47)
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Start07 Test Object Detection On Image (4:53)
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StartSource Files
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Start00. Intro To Course And Python (9:57)
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Start01. Variables (19:19)
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Start02. Type Conversion Examples (10:06)
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Start03. Operators (28:54)
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Start04. Collections (8:25)
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Start05. List Examples (19:41)
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Start06. Tuples Examples (8:36)
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Start07. Dictionaries Examples (14:26)
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Start08. Ranges Examples (8:32)
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Start09. Conditionals (6:43)
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Start10. If Statement Examples (21:32)
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Start11. Loops (29:42)
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Start12. Functions (17:01)
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Start13. Parameters And Return Values Examples (13:54)
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Start14. Classes And Objects (34:11)
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Start15. Inheritance Examples (17:29)
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Start16. Static Members Examples (11:05)
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Start17. Summary And Outro (4:08)
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Start00. Introduction (3:27)
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Start01. Downloading And Installing Android Studio (6:53)
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Start02. Exploring Android Studio Interface (12:59)
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Start03. Understanding File Hierarchy (12:19)
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Start04. Exploring Activity-Layout Relationship (19:36)
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Start05. Setting Up An Emulator (7:01)
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Start06. Running App And Implementing User Interaction (6:45)
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Start07. Debugging An App (6:11)
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Start08. Summary And Outro (4:07)
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Start00. Introduction (6:12)
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Start01. Introduction To Variables (7:04)
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Start02. Basic Operations (9:18)
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Start03. Nullable Variables (5:24)
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Start04. Collections Intro (7:27)
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Start05. Mutable Lists And Arrays (6:53)
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Start06. If Statements And Expressions (8:11)
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Start07. When Statements And Expressions (3:30)
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Start08. While Loops (6:46)
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Start09. For In Loops (4:55)
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Start10. Introduction To Functions (7:55)
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Start11. Functions With Parameters And Return Values (7:29)
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Start12. Classes And Objects Introductions (16:37)
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Start13. Subclassing And Superclassing (13:12)
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Start14. Summary And Outro (4:41)
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StartSource FIles
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Start00 Project Preview (2:17)
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Start01 Build A Linear Regression Model With Python (15:06)
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Start02 Convert Python Model To Tensorflow Lite (5:38)
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Start03 Build A New Android Studio App (7:39)
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Start04 Build App Layout (10:18)
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Start05 Load Machine Learning Model (4:53)
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Start06 Use Machine Learning Model (5:18)
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StartSource files
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Start01 Build A Deep Neural Network With Gradient Descent From Scratch-1 (9:21)
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Start03 Build A Deep Neural Network With Gradient Descent With Tensorflow Js-2 (11:24)
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Start04 Build A Deep Neural Network With Backpropagation-3 (7:03)
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Start05 Build The Backpropagation-4 (16:56)
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StartSource Files 11
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Start01. Course Requirements-1 (3:41)
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Start02. Html Styles Crash Course-2 (4:45)
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Start03. Adding Code To The Css-3 (4:46)
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Start04. Adding In Ids To The Css-4 (5:16)
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Start05. Classes In Css-5 (2:39)
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Start06. Font Families-6 (5:04)
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Start07. Colors In Css-7 (5:44)
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Start08. Padding In Css-8 (3:06)
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Start09. Text Align And Transforms-9 (3:14)
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Start10. Margins And Width-10 (5:33)
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Start11. Changing The Body-11 (4:11)
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Start12. Latin Text Generator-12 (1:57)
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Start13. Adding In A Horizontal Menu With Html And Css-13 (7:53)
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Start15. Adding A Background Image-14 (4:04)
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Start15. Playing Around With Margins In Css-15 (2:20)
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Start01. Course Requirements-1 (2:56)
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Start02. What Is Jsbin-2 (3:15)
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Start03. Setting Up The Html Document-3 (2:41)
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Start04. Header Tags And Paragraphs Tags-4 (4:06)
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Start05. Styles-5 (3:32)
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Start06. Bold Underline And Italic Tags-6 (3:10)
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Start07. Adding In A Link-7 (1:38)
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Start08. Adding In A Image-8 (3:01)
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Start09. Adding A Link To An Image-9 (1:55)
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Start10. Lists-10 (4:03)
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Start11. Tables-11 (3:29)
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Start13. Different Kinds Of Input-12 (4:59)
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Start14. Adding In A Submit Button-13 (3:01)
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Start15. Scripts And Style Tags-14 (3:27)
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Start01. Course Requirements-1 (4:44)
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Start02. Html, Css And Javascript Crash Course-2 (4:54)
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Start03. Adding In Functions-3 (4:36)
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Start04. Scaling Functions-4 (4:27)
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Start05. Changing The Text In Javascript-5 (4:50)
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Start06. Variables-6 (5:40)
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Start07. Arrays-7 (5:30)
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Start08. Objects-8 (6:36)
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Start09. Variable Scope-9 (5:04)
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Start10. Adding User Input Text-10 (5:05)
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Start11. Calling Functions-11 (3:56)
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Start12. If Statements-12 (4:49)
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Start13. Else If And Else Statements-13 (4:05)
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Start14. Changing The Style With Javascript-14 (5:49)
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Start01 Load Json Data-1 (7:34)
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Start02 Convert Json Data To Tensor-2 (9:08)
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Start03 Visualize Dataset With Tf-Vis-3 (5:38)
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Start04 Build And Train Model-4 (10:22)
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Start05 Visualize Model's Training Epochs-5 (9:12)
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Start06 Make A Prediction-6 (13:49)
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Start07 Visualize Prediction-7 (9:09)
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StartSource Files 05
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Start01 Load Dataset From Json File-1 (6:48)
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Start02 Visualize Dataset's Features-2 (9:26)
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Start03 Build A Multi Layer Model-3 (7:43)
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Start04 Extract Inputs And Outputs-4 (7:10)
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Start05 Normalize Data-5 (4:47)
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Start06 Train The Model-6 (6:01)
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Start07 Evaluate Model Performance-7 (6:12)
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StartSource Files 06
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Start00 What Is Logistic Regression-1 (4:32)
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Start00B Calculate Logistic Regression Accuracy-2 (5:20)
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Start01 Build A Logistic Regression Model-3 (7:08)
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Start02 Train The Logistic Regression Model-4 (15:20)
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Start03 Visualize Logistic Regression Results-5 (12:52)
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Start04 Visualize Original Data-6 (12:13)
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Start05 Visualize Model Error-7 (7:37)
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StartSource Files 07
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Start00 What Is Principal Component Analysis-1 (6:13)
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Start01 Build Principal Component Analysis-2 (6:24)
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Start02 Calculate Variance Of Data And Principal Component Analysis-3 (9:28)
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Start03 Visualize Data Slices-4 (12:01)
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Start04 Visualize Principal Component Analysis Results-5 (3:03)
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StartSource Files 09
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Start01 Process Iris Data-1 (7:37)
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Start02 Convert Data To Tensors-2 (8:45)
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Start03 Separate Training And Testing Data-3 (8:54)
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Start04 Create Training And Testing Datasets-4 (4:42)
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Start05 Build The Model-5 (9:29)
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Start06 Train The Model-6 (4:11)
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Start07 Make A Prediction-7 (8:45)
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StartSource Files 05
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Start00 What Is A Convolutional Neural Network-1 (19:29)
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Start01 Set Up Canvas To Load Image Data-2 (10:36)
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Start02 Load Mnist Dataset-3 (6:47)
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Start03 Separate Training And Testing Data-4 (5:40)
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Start04 Build The Model-5 (6:48)
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Start04A What Are The Network's Layers-6 (14:14)
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Start05 Train The Model-7 (11:27)
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Start06 Create Training Batches-8 (6:14)
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Start07 Create Testing Batches-9 (11:31)
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Start08 Fit Neural Network Through Data-10 (8:54)
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StartSource Files 07
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Start00A Project Preview (2:13)
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Start00B What Is Linear Regression (5:03)
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Start01 Collect Data From Blockchain Api (12:57)
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Start02 Join CSV Files With Blockchain Data (9:01)
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Start03 Process Data (4:06)
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Start04 Visualize Data (11:19)
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Start05 Create X And Y (6:15)
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Start06 Build A Linear Regression Model (4:59)
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Start07 Build A Polynomial Regression Model (5:54)
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StartSource Files 03
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Start00A Project Preview (3:02)
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Start00B What Is Unsupervised Learning (8:17)
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Start01 Collect Crypto Data With Cryptocompare API (9:35)
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Start02 Clean Data (8:10)
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Start03 Process Text Features (7:26)
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Start04A What Is Principal Component Analysis (7:27)
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Start04B Reduce Data Dimensionality With Principal Component Analysis (4:41)
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Start05A What Is K Means Clustering (11:58)
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Start05B Cluster Cryptocurrencies With K-Means Clustering (7:41)
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Start06 Machine Learning With Optimal Number Of Clusters (4:48)
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Start07 Visualize Clusters (5:25)
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StartSource Files 04
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Start00A Project Preview (2:13)
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Start00B What Is A Recurrent Neural Network (6:38)
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Start01 Load Stock Data With Yahoo Finance API (7:20)
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Start02 Visualize Data (8:27)
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Start03 Build A Training Dataset (8:04)
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Start04 Build Features And Labels (10:37)
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Start05 Build A Tensorflow Lstm Neural Network (12:05)
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Start06 Load Test Data With An API (7:32)
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Start07 Build Features And Labels For Testing The Neural Network (10:42)
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Start08 Visualize Model's Predictions (8:42)
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StartSource Files 07c
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Start00A Gradient Boosting Introduction (8:40)
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Start00B What Is A Light Gradient Boosted Regression Ensemble (5:08)
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Start01 Load Stock Data With Yahoo Finance API (5:08)
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Start02 Build A Light GBM (7:59)
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Start03 Find Best Number Of Trees (8:46)
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Start04 Find Best Tree Depth
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StartSource Files 09
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Start00 What Is Differential Privacy (7:18)
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Start01 Differential Privacy Project Introduction (13:16)
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Start02 Build An Initial Database (3:05)
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Start03 Build A Parallel Database (4:04)
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Start04 Build Multiple Parallel Databases (3:09)
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Start05 Determine If Query Leaks Private Data (5:12)
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Start06 Calculate Sensitivity Of Mean Query (6:29)
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Start07 Build Local Differential Privacy (9:09)
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StartSource Files 11
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Start00 What Is Federated Learning (6:28)
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Start01 Generate A Dataset (10:03)
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Start02 Build A Regular Model (7:43)
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Start03 Visualize Model Results (7:01)
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Start04 Build A Client-Side Model (2:51)
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Start05 Build An Aggregator Model (2:07)
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Start06 Generate Clients Dataset (9:26)
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Start07 Execute The Federated Learning Model (9:59)
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Start08 Evaluate The Model (3:36)
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StartSource Files 13
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Start01 Project Overview - Adaboost Stock Prediction (7:28)
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Start02 Build Stock Dataset For Machine Learning (5:33)
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Start03 Build An Adaboost Regression Machine Learning Model For Stock Prediction (6:47)
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Start04 Find Best Ml Model With Optimal Number Of Estimators (15:18)
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StartAdaBoost Source Files
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Start00 Project Overview - Classification Machine Learning For Crypto Stocks (7:05)
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Start01 Load And Prepare Crypto Data In Colab (8:32)
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Start02 Build Classification Models To Predict Stock (7:59)
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Start03 Build Tree Classification Models To Predict Crypto Price (3:25)
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Start04 Compare Classification Model Results With Numpy And Pandas (3:51)
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StartClassification source files
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Start01 Load Yfinance Data Into Colab (4:10)
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Start02 Build Trading Signals With Sma Windows (3:50)
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Start03 Calculate And Visualize Strategy Returns (2:48)
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Start04 Prepare Data For Machine Learning (6:40)
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Start05 Build A Support Vector Classifier With Sklearn (3:01)
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Start06 Calculate And Visualize Returns From Model (4:08)
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StartSVM Source files