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Complete Machine Learning and Data Science: Zero to Mastery
Introduction
Complete Machine Learning and Data Science: Zero to Mastery (4:10)
Course Outline (5:59)
Exercise: Meet Your Classmates and Instructor
Your First Day (3:48)
Understanding Your Video Player (notes, video speed, subtitles + more)
Set Your Learning Streak Goal
Machine Learning 101
What Is Machine Learning? (6:52)
AI/Machine Learning/Data Science (4:51)
Exercise: Machine Learning Playground (6:16)
How Did We Get Here? (6:03)
Exercise: YouTube Recommendation Engine (4:24)
Types of Machine Learning (4:41)
Are You Getting It Yet?
What Is Machine Learning? Round 2 (4:44)
Section Review (1:48)
Let's Have Some Fun (+ Free Resources)
Machine Learning and Data Science Framework
Section Overview (3:08)
Introducing Our Framework (2:38)
6 Step Machine Learning Framework (4:59)
Types of Machine Learning Problems (10:32)
Types of Data (4:50)
Types of Evaluation (3:31)
Features In Data (5:22)
Modelling - Splitting Data (5:58)
Modelling - Picking the Model (4:35)
Modelling - Tuning (3:17)
Modelling - Comparison (9:32)
Overfitting and Underfitting Definitions
Experimentation (3:35)
Tools We Will Use (3:59)
Optional: Elements of AI
Unlimited Updates
The 2 Paths
The 2 Paths (3:27)
Python + Machine Learning Monthly
Data Science Environment Setup
Section Overview (1:09)
Introducing Our Tools (3:28)
What is Conda? (2:35)
Conda Environments (4:30)
Mac Environment Setup (17:26)
Mac Environment Setup 2 (14:11)
Windows Environment Setup (5:17)
Windows Environment Setup 2 (23:17)
Linux Environment Setup
Sharing your Conda Environment
Jupyter Notebook Walkthrough (10:20)
Jupyter Notebook Walkthrough 2 (16:17)
Jupyter Notebook Walkthrough 3 (8:10)
Course Check-In
Pandas: Data Analysis
Section Overview (2:27)
Downloading Workbooks and Assignments
Pandas Introduction (4:29)
Series, Data Frames and CSVs (13:21)
Data from URLs
Describing Data with Pandas (9:48)
Selecting and Viewing Data with Pandas (11:08)
Selecting and Viewing Data with Pandas Part 2 (13:06)
Manipulating Data (13:56)
Manipulating Data 2 (9:56)
Manipulating Data 3 (10:12)
Assignment: Pandas Practice
How To Download The Course Assignments (7:43)
Implement a New Life System
NumPy
Section Overview (2:40)
NumPy Introduction (5:17)
Quick Note: Correction In Next Video
NumPy DataTypes and Attributes (14:05)
Creating NumPy Arrays (9:22)
NumPy Random Seed (7:17)
Endorsements On LinkedIn
Viewing Arrays and Matrices (9:35)
Manipulating Arrays (11:31)
Manipulating Arrays 2 (9:44)
Standard Deviation and Variance (7:10)
Reshape and Transpose (7:26)
Dot Product vs Element Wise (11:45)
Exercise: Nut Butter Store Sales (13:04)
Comparison Operators (3:33)
Sorting Arrays (6:19)
Turn Images Into NumPy Arrays (7:37)
Assignment: NumPy Practice
Optional: Extra NumPy resources
Matplotlib: Plotting and Data Visualization
Section Overview (1:50)
Matplotlib Introduction (5:16)
Importing And Using Matplotlib (11:36)
Anatomy Of A Matplotlib Figure (9:19)
Scatter Plot And Bar Plot (10:09)
Histograms And Subplots (8:40)
Subplots Option 2 (4:15)
Quick Tip: Data Visualizations (1:48)
Plotting From Pandas DataFrames (5:58)
Quick Note: Regular Expressions
Plotting From Pandas DataFrames 2 (10:33)
Plotting from Pandas DataFrames 3 (8:32)
Plotting from Pandas DataFrames 4 (6:36)
Plotting from Pandas DataFrames 5 (8:28)
Plotting from Pandas DataFrames 6 (8:27)
Plotting from Pandas DataFrames 7 (11:20)
Customizing Your Plots (10:09)
Customizing Your Plots 2 (9:41)
Saving And Sharing Your Plots (4:14)
Assignment: Matplotlib Practice
Scikit-learn: Creating Machine Learning Models
Section Overview (2:29)
Scikit-learn Introduction (6:41)
Quick Note: Upcoming Video
Refresher: What Is Machine Learning? (5:40)
Quick Note: Upcoming Videos
Scikit-learn Cheatsheet (6:12)
Typical scikit-learn Workflow (23:14)
Optional: Debugging Warnings In Jupyter (18:57)
Getting Your Data Ready: Splitting Your Data (8:37)
Quick Tip: Clean, Transform, Reduce (5:03)
Getting Your Data Ready: Convert Data To Numbers (16:54)
Note: Update to next video (OneHotEncoder can handle NaN/None values)
Getting Your Data Ready: Handling Missing Values With Pandas (12:22)
Extension: Feature Scaling
Note: Correction in the upcoming video
Getting Your Data Ready: Handling Missing Values With Scikit-learn (17:29)
NEW: Choosing The Right Model For Your Data (20:14)
NEW: Choosing The Right Model For Your Data 2 (Regression) (11:21)
Quick Note: Decision Trees
Quick Tip: How ML Algorithms Work (1:25)
Choosing The Right Model For Your Data 3 (Classification) (12:45)
Fitting A Model To The Data (6:45)
Making Predictions With Our Model (8:24)
predict() vs predict_proba() (8:33)
NEW: Making Predictions With Our Model (Regression) (8:48)
NEW: Evaluating A Machine Learning Model (Score) Part 1 (9:41)
NEW: Evaluating A Machine Learning Model (Score) Part 2 (6:47)
Evaluating A Machine Learning Model 2 (Cross Validation) (13:16)
Evaluating A Classification Model 1 (Accuracy) (4:46)
Evaluating A Classification Model 2 (ROC Curve) (9:04)
Evaluating A Classification Model 3 (ROC Curve) (7:44)
Reading Extension: ROC Curve + AUC
Evaluating A Classification Model 4 (Confusion Matrix) (11:01)
NEW: Evaluating A Classification Model 5 (Confusion Matrix) (14:22)
Evaluating A Classification Model 6 (Classification Report) (10:16)
NEW: Evaluating A Regression Model 1 (R2 Score) (9:59)
NEW: Evaluating A Regression Model 2 (MAE) (7:22)
NEW: Evaluating A Regression Model 3 (MSE) (9:49)
Machine Learning Model Evaluation
NEW: Evaluating A Model With Cross Validation and Scoring Parameter (25:19)
NEW: Evaluating A Model With Scikit-learn Functions (14:01)
Improving A Machine Learning Model (11:16)
Tuning Hyperparameters (23:15)
Tuning Hyperparameters 2 (14:23)
Tuning Hyperparameters 3 (14:59)
Note: Metric Comparison Improvement
Quick Tip: Correlation Analysis (2:28)
Saving And Loading A Model (7:28)
Saving And Loading A Model 2 (6:20)
Putting It All Together (20:19)
Putting It All Together 2 (11:34)
Scikit-Learn Practice
Supervised Learning: Classification + Regression
Milestone Projects!
Milestone Project 1: Supervised Learning (Classification)
Section Overview (2:09)
Project Overview (6:09)
Project Environment Setup (10:58)
Step 1~4 Framework Setup (12:06)
Note: Code update for next video
Getting Our Tools Ready (9:04)
Exploring Our Data (8:33)
Finding Patterns (10:02)
Finding Patterns 2 (16:47)
Finding Patterns 3 (13:36)
Preparing Our Data For Machine Learning (8:51)
Choosing The Right Models (10:15)
Experimenting With Machine Learning Models (6:31)
Tuning/Improving Our Model (13:49)
Tuning Hyperparameters (11:27)
Tuning Hyperparameters 2 (11:49)
Tuning Hyperparameters 3 (7:06)
Quick Note: Confusion Matrix Labels
Evaluating Our Model (10:59)
Evaluating Our Model 2 (5:54)
Evaluating Our Model 3 (8:49)
Finding The Most Important Features (16:07)
Reviewing The Project (9:13)
Exercise: Imposter Syndrome (2:55)
Milestone Project 2: Supervised Learning (Time Series Data)
Section Overview (1:07)
Project Overview (4:24)
Downloading the data for the next two projects
Project Environment Setup (10:52)
Step 1~4 Framework Setup (8:36)
Exploring Our Data (14:16)
Exploring Our Data 2 (6:16)
Feature Engineering (15:24)
Turning Data Into Numbers (15:38)
Filling Missing Numerical Values (12:49)
Filling Missing Categorical Values (8:27)
Fitting A Machine Learning Model (7:16)
Splitting Data (10:00)
Challenge: What's wrong with splitting data after filling it?
Custom Evaluation Function (11:13)
Reducing Data (10:36)
RandomizedSearchCV (9:32)
Improving Hyperparameters (8:11)
Preproccessing Our Data (13:15)
Making Predictions (9:17)
Feature Importance (13:50)
Data Engineering
Data Engineering Introduction (3:23)
What Is Data? (6:42)
What is a Data Engineer? (4:20)
What is A Data Engineer 2? (5:35)
What is a Data Engineer 3? (5:03)
What is a Data Engineer 4? (3:22)
Types of Databases (6:50)
Quick Note: Upcoming Video
Optional: OLTP Databases (10:54)
Optional: Learn SQL
Hadoop, HDFS and MapReduce (4:22)
Apache Spark and Apache Flink (2:07)
Kafka and Stream Processing (4:33)
Neural Networks: Deep Learning, Transfer Learning and TensorFlow 2
Section Overview (2:06)
Deep Learning and Unstructured Data (13:36)
Setting Up With Google
Setting Up Google Colab (7:17)
Google Colab Workspace (4:23)
Uploading Project Data (6:52)
Setting Up Our Data (4:40)
Setting Up Our Data 2 (1:32)
Importing TensorFlow 2 (12:43)
Optional: TensorFlow 2.0 Default Issue (3:38)
Using A GPU (8:59)
Optional: GPU and Google Colab (4:27)
Optional: Reloading Colab Notebook (6:49)
Loading Our Data Labels (12:04)
Preparing The Images (12:32)
Turning Data Labels Into Numbers (12:11)
Creating Our Own Validation Set (9:18)
Preprocess Images (10:25)
Preprocess Images 2 (11:00)
Turning Data Into Batches (9:37)
Turning Data Into Batches 2 (17:54)
Visualizing Our Data (12:41)
Preparing Our Inputs and Outputs (6:37)
Optional: How machines learn and what's going on behind the scenes?
Building A Deep Learning Model (11:42)
Building A Deep Learning Model 2 (10:53)
Building A Deep Learning Model 3 (9:05)
Building A Deep Learning Model 4 (9:12)
Summarizing Our Model (4:52)
Evaluating Our Model (9:26)
Preventing Overfitting (4:20)
Training Your Deep Neural Network (19:09)
Evaluating Performance With TensorBoard (7:30)
Make And Transform Predictions (15:04)
Transform Predictions To Text (15:19)
Visualizing Model Predictions (14:46)
Visualizing And Evaluate Model Predictions 2 (15:52)
Visualizing And Evaluate Model Predictions 3 (10:39)
Saving And Loading A Trained Model (13:34)
Training Model On Full Dataset (15:01)
Making Predictions On Test Images (16:54)
Submitting Model to Kaggle (14:14)
Making Predictions On Our Images (15:15)
Finishing Dog Vision: Where to next?
Storytelling + Communication: How To Present Your Projects
Section Overview (2:19)
Communicating Your Work (3:21)
Communicating With Managers (2:58)
Communicating With Co-Workers (3:42)
Weekend Project Principle (6:32)
Communicating With Outside World (3:28)
Storytelling (3:05)
Career Advice + Extra Bits
Endorsements On LinkedIn
Quick Note: Upcoming Video
What If I Don't Have Enough Experience? (15:03)
Learning Guideline
Quick Note: Upcoming Videos
JTS: Learn to Learn (1:59)
JTS: Start With Why (2:43)
Quick Note: Upcoming Videos
CWD: Git + Github (17:40)
CWD: Git + Github 2 (16:52)
Contributing To Open Source (14:44)
Contributing To Open Source 2 (9:42)
Exercise: Contribute To Open Source
Coding Challenges
Learn Python
What Is A Programming Language (6:24)
Python Interpreter (14:08)
How To Run Python Code (9:46)
Our First Python Program (7:43)
Latest Version Of Python (3:40)
Python 2 vs Python 3 (13:21)
Exercise: How Does Python Work? (2:09)
Learning Python (2:05)
Python Data Types (4:46)
How To Succeed
Numbers (11:09)
Math Functions (4:29)
DEVELOPER FUNDAMENTALS: I (4:07)
Operator Precedence (3:10)
Exercise: Operator Precedence
Optional: bin() and complex (4:02)
Variables (13:12)
Expressions vs Statements (1:36)
Augmented Assignment Operator (2:49)
Strings (5:29)
String Concatenation (1:16)
Type Conversion (3:03)
Escape Sequences (4:23)
Formatted Strings (8:23)
String Indexes (8:57)
Immutability (3:13)
Built-In Functions + Methods (10:03)
Booleans (3:21)
Exercise: Type Conversion (8:22)
DEVELOPER FUNDAMENTALS: II (4:42)
Exercise: Password Checker (7:21)
Lists (5:01)
List Slicing (7:48)
Matrix (4:11)
List Methods (10:28)
List Methods 2 (4:24)
List Methods 3 (4:52)
Common List Patterns (5:57)
List Unpacking (2:40)
None (1:51)
Dictionaries (6:20)
DEVELOPER FUNDAMENTALS: III (2:40)
Dictionary Keys (3:37)
Dictionary Methods (4:37)
Dictionary Methods 2 (7:04)
Tuples (4:46)
Tuples 2 (3:14)
Sets (7:24)
Sets 2 (8:45)
Learn Python Part 2
Breaking The Flow (2:34)
Conditional Logic (13:17)
Indentation In Python (4:38)
Truthy vs Falsey (5:17)
Ternary Operator (4:14)
Short Circuiting (4:02)
Logical Operators (6:56)
Exercise: Logical Operators (7:47)
is vs == (7:36)
For Loops (7:01)
Iterables (6:43)
Exercise: Tricky Counter (3:23)
range() (5:38)
enumerate() (4:37)
While Loops (6:28)
While Loops 2 (5:49)
break, continue, pass (4:15)
Our First GUI (8:48)
DEVELOPER FUNDAMENTALS: IV (6:34)
Exercise: Find Duplicates (3:54)
Functions (7:41)
Parameters and Arguments (4:24)
Default Parameters and Keyword Arguments (5:40)
return (13:11)
Exercise: Tesla
Methods vs Functions (4:33)
Docstrings (3:47)
Clean Code (4:38)
*args and **kwargs (7:56)
Exercise: Functions (4:18)
Scope (3:37)
Scope Rules (6:55)
global Keyword (6:13)
nonlocal Keyword (3:21)
Why Do We Need Scope? (3:38)
Pure Functions (9:23)
map() (6:30)
filter() (4:23)
zip() (3:28)
reduce() (7:31)
List Comprehensions (8:37)
Set Comprehensions (6:26)
Exercise: Comprehensions (4:36)
Python Exam: Testing Your Understanding
Modules in Python (10:54)
Quick Note: Upcoming Videos
Optional: PyCharm (8:19)
Packages in Python (10:45)
Different Ways To Import (7:03)
Next Steps
Bonus: Learn Advanced Statistics and Mathematics for FREE!
Statistics and Mathematics
Where To Go From Here?
Thank You (2:44)
Review This Course!
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Exercise: YouTube Recommendation Engine
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Machine Learning Playground