Course Curriculum
Introduction
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Machine Learning 101
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- 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
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- 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
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Data Science Environment Setup
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- 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
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- 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
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- 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
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- 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
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- 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 (14:02)
- 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
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Milestone Project 1: Supervised Learning (Classification)
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- 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 (5:55)
- 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)
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- 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
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- Data Engineering Introduction (3:23)
- What Is Data? (6:42)
- What is a Data Engineer? (4:20)
- What is A Data Engineer 2? (5:36)
- 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
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- 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
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Career Advice + Extra Bits
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Learn Python
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Learn Python Part 2
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- Watch Python Basics 2 Section
- 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)
- 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
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Where To Go From Here?
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