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Complete A.I. Machine Learning and Data Science: Zero to Mastery
Introduction
Complete A.I. Machine Learning and Data Science: Zero to Mastery (4:10)
Course Outline (5:59)
Exercise: Meet Your Classmates and Instructor
Course Resources
Your First Day (3:48)
ZTM Plugin + Understanding Your Video Player
Set Your Learning Streak Goal
Asking Questions + Getting Help
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
Quick Note: Upcoming Videos
Describing Data with Pandas (9:48)
Selecting and Viewing Data with Pandas (11:08)
Quick Note: Upcoming Video
Selecting and Viewing Data with Pandas Part 2 (13:07)
Manipulating Data (13:56)
Manipulating Data 2 (9:57)
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)