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\ 0:00 Course Outline 5:59 Join Our Online Classroom! 10:00 Your First Day Learning 101\ 13:49 What Is Machine Learning 20:41 AIMachine LearningData Science 25:32 Exercise Machine Learning Playground 31:48 How Did We Get Here 37:51 Exercise YouTube Recommendation Engine 42:16 Types of Machine Learning 46:58 What Is Machine Learning Round 2 51:43 Section Review Learning and Data Science Framework\ 53:31 Section Overview 56:40 Introducing Our Framework 59:19 6 Step Machine Learning Framework 1:04:18 Types of Machine Learning Problems 1:14:50 Types of Data 1:19:41 Types of Evaluation 1:23:12 Features In Data 1:28:35 Modelling - Splitting Data 1:34:33 Modelling - Picking the Model 1:39:08 Modelling - Tuning 1:42:26 Modelling - Comparison 1:51:58 Experimentation 1:55:34 Tools We Will Use 2 Paths\ 1:59:34 The 2 Paths Science Environment Setup\ 2:03:01 Section Overview 2:04:11 Introducing Our Tools 2:07:40 What is Conda 2:10:15 Conda Environments 2:14:45 Mac Environment Setup 2:32:12 Mac Environment Setup 2 2:46:23 Windows Environment Setup 2:51:41 Windows Environment Setup 2 3:14:58 Jupyter Notebook Walkthrough 3:25:19 Jupyter Notebook Walkthrough 2 3:41:37 Jupyter Notebook Walkthrough 3 Data Analysis\ 3:49:47 Section Overview 3:52:15 Pandas Introduction 3:56:45 Series, Data Frames and CSVs 4:10:06 Describing Data with Pandas 4:19:55 Selecting and Viewing Data with Pandas 4:31:03 Selecting and Viewing Data with Pandas Part 2 4:44:11 Manipulating Data 4:58:07 Manipulating Data 2 5:08:05 Manipulating Data 3 5:18:17 How To Download The Course Assignments \ 5:26:00 Section Overview 5:28:41 NumPy Introduction 5:33:59 NumPy DataTypes and Attributes 5:48:05 Creating NumPy Arrays 5:57:27 NumPy Random Seed 6:04:45 Viewing Arrays and Matrices 6:14:20 Manipulating Arrays 6:25:52 Manipulating Arrays 2 6:35:37 Standard Deviation and Variance 6:42:47 Reshape and Transpose 6:50:14 Dot Product vs Element Wise 7:01:59 Exercise Nut Butter Store Sales 7:15:03 Comparison Operators 7:18:37 Sorting Arrays 7:24:57 Turn Images Into NumPy Arrays 7:32:34 Exercise Imposter Syndrome Plotting and Data Visualization\ 7:35:30 Section Overview 7:37:21 Matplotlib Introduction 7:42:38 Importing And Using Matplotlib 7:54:14 Anatomy Of A Matplotlib Figure 8:03:34 Scatter Plot And Bar Plot 8:13:44 Histograms And Subplots 8:22:24 Subplots Option 2 8:26:39 Quick Tip Data Visualizations 8:28:28 Plotting From Pandas DataFrames 8:34:26 Plotting From Pandas DataFrames 2 8:45:00 Plotting from Pandas DataFrames 3 8:53:32 Plotting from Pandas DataFrames 4 9:00:09 Plotting from Pandas DataFrames 5 9:08:38 Plotting from Pandas DataFrames 6 9:17:06 Plotting from Pandas DataFrames 7 9:28:26 Customizing Your Plots 9:38:36 Customizing Your Plots 2 9:48:18 Saving And Sharing Your Plots Creating Machine Learning Models\ 9:52:32 Section Overview 9:55:02 Scikit-learn Introduction 10:01:43 Refresher What Is Machine Learning 10:07:23 Scikit-learn Cheatsheet 10:13:36 Typical scikit-learn Workflow 10:36:51 Optional Debugging Warnings In Jupyter 10:55:49 Getting Your Data Ready Splitting Your Data 11:04:26 Quick Tip Clean, Transform, Reduce 11:09:29 Getting Your Data Ready Convert Data To Numbers 11:26:23 Getting Your Data Ready Handling Missing Values With Pandas 11:38:46 Getting Your Data Ready Handling Missing Values With Scikit-learn 11:56:15 NEW Choosing The Right Model For Your Data
