Section 1 : Introduction
Lecture 1 Course Outline copy 00:05:59 Duration
Lecture 2 Join Our Online Classroom!
Lecture 3 Exercise Meet The Community
Lecture 4 Your First Day 00:03:48 Duration

Section 2 : Machine Learning 101
Lecture 1 What Is Machine Learning 00:06:52 Duration
Lecture 2 AIMachine LearningData Science 00:04:51 Duration
Lecture 3 Exercise Machine Learning Playground 00:06:16 Duration
Lecture 4 How Did We Get Here 00:06:03 Duration
Lecture 5 Exercise YouTube Recommendation Engine 00:04:25 Duration
Lecture 6 Types of Machine Learning 00:04:41 Duration
Lecture 7 Are You Getting It Yet
Lecture 8 What Is Machine Learning Round 2 00:04:45 Duration
Lecture 9 Section Review 00:01:48 Duration

Section 3 : Machine Learning and Data Science Framework
Lecture 1 Section Overview 00:03:09 Duration
Lecture 2 Introducing Our Framework 00:02:38 Duration
Lecture 3 Step Machine Learning Framework 00:04:59 Duration
Lecture 4 Types of Machine Learning Problems 00:10:32 Duration
Lecture 5 Types of Data 00:04:51 Duration
Lecture 6 Types of Evaluation 00:03:31 Duration
Lecture 7 Features In Data 00:05:22 Duration
Lecture 8 Modelling - Splitting Data
Lecture 9 Modelling - Picking the Model 00:04:35 Duration
Lecture 10 Modelling - Tuning 00:03:17 Duration
Lecture 11 Modelling - Comparison 00:09:32 Duration
Lecture 12 Overfitting and Underfitting Definitions
Lecture 13 Experimentation 00:03:35 Duration
Lecture 14 Tools We Will Use 00:04:00 Duration
Lecture 15 Optional Elements of AI

Section 4 : The 2 Paths
Lecture 1 The 2 Paths 00:03:27 Duration
Lecture 2 Python + Machine Learning Monthly
Lecture 3 Endorsements On LinkedIN

Section 5 : Data Science Environment Setup
Lecture 1 Section Overview 00:01:09 Duration
Lecture 2 Introducing Our Tools 00:03:29 Duration
Lecture 3 What is Conda 00:02:35 Duration
Lecture 4 Conda Environments 00:04:30 Duration
Lecture 5 Mac Environment Setup 00:17:27 Duration
Lecture 6 Mac Environment Setup 2 00:14:11 Duration
Lecture 7 Windows Environment Setup 00:05:17 Duration
Lecture 8 Windows Environment Setup 2 00:23:18 Duration
Lecture 9 Linux Environment Setup
Lecture 10 Sharing your Conda Environment
Lecture 11 Jupyter Notebook Walkthrough
Lecture 12 Jupyter Notebook Walkthrough 2 00:16:18 Duration
Lecture 13 Jupyter Notebook Walkthrough 3 00:08:10 Duration

Section 6 : Pandas Data Analysis
Lecture 1 Section Overview 00:02:27 Duration
Lecture 2 Downloading Workbooks and Assignments
Lecture 3 Pandas Introduction 00:04:29 Duration
Lecture 4 Series, Data Frames and CSVs 00:13:21 Duration
Lecture 5 Data from URLs
Lecture 6 Describing Data with Pandas 00:09:49 Duration
Lecture 7 Selecting and Viewing Data with Pandas 00:11:08 Duration
Lecture 8 Selecting and Viewing Data with Pandas Part 2 00:13:07 Duration
Lecture 9 Manipulating Data 00:13:57 Duration
Lecture 10 Manipulating Data 2 00:09:57 Duration
Lecture 11 Manipulating Data 3 00:10:12 Duration
Lecture 12 Assignment Pandas Practice
Lecture 13 How To Download The Course Assignments 00:07:43 Duration

Section 7 : NumPy
Lecture 1 Section Overview 00:02:41 Duration
Lecture 2 NumPy Introduction 00:05:18 Duration
Lecture 3 Quick Note Correction In Next Video
Lecture 4 NumPy DataTypes and Attributes 00:14:06 Duration
Lecture 5 Creating NumPy Arrays 00:09:22 Duration
Lecture 6 NumPy Random Seed 00:07:17 Duration
Lecture 7 Viewing Arrays and Matrices 00:09:35 Duration
Lecture 8 Manipulating Arrays 00:11:32 Duration
Lecture 9 Manipulating Arrays 2 00:09:44 Duration
Lecture 10 Standard Deviation and Variance 00:07:10 Duration
Lecture 11 Reshape and Transpose 00:07:27 Duration
Lecture 12 Dot Product vs Element Wise 00:11:45 Duration
Lecture 13 Exercise Nut Butter Store Sales 00:13:04 Duration
Lecture 14 Comparison Operators 00:03:34 Duration
Lecture 15 Sorting Arrays 00:06:20 Duration
Lecture 16 Turn Images Into NumPy Arrays 00:07:37 Duration
Lecture 17 Assignment NumPy Practice
Lecture 18 Optional Extra NumPy resources

Section 8 : Matplotlib Plotting and Data Visualization
Lecture 1 Section Overview 00:01:51 Duration
Lecture 2 Matplotlib Introduction 00:05:17 Duration
Lecture 3 Importing And Using Matplotlib 00:11:36 Duration
Lecture 4 Anatomy Of A Matplotlib Figure 00:09:19 Duration
Lecture 5 Scatter Plot And Bar Plot 00:10:09 Duration
Lecture 6 Histograms And Subplots 00:08:40 Duration
Lecture 7 Subplots Option 2 00:04:15 Duration
Lecture 8 Quick Tip Data Visualizations 00:01:48 Duration
Lecture 9 Plotting From Pandas DataFrames 00:05:58 Duration
Lecture 10 Quick Note Regular Expressions
Lecture 11 Plotting From Pandas DataFrames 2 00:10:33 Duration
Lecture 12 Plotting from Pandas DataFrames 3 00:08:33 Duration
Lecture 13 Plotting from Pandas DataFrames 4 00:06:36 Duration
Lecture 14 Plotting from Pandas DataFrames 5 00:08:29 Duration
Lecture 15 Plotting from Pandas DataFrames 6 00:08:29 Duration
Lecture 16 Plotting from Pandas DataFrames 7 00:11:20 Duration
Lecture 17 Customizing Your Plots 00:10:10 Duration
Lecture 18 Customizing Your Plots 2 00:09:41 Duration
Lecture 19 Saving And Sharing Your Plots 00:04:14 Duration
Lecture 20 Assignment Matplotlib Practice

Section 9 : Scikit-learn Creating Machine Learning Models
Lecture 1 Section Overview 00:02:30 Duration
Lecture 2 Scikit-learn Introduction 00:06:41 Duration
Lecture 3 Quick Note Upcoming Video
Lecture 4 Refresher What Is Machine Learning 00:05:40 Duration
Lecture 5 Quick Note Upcoming Videos
Lecture 6 Scikit-learn Cheatsheet 00:06:13 Duration
Lecture 7 Typical scikit-learn Workflow 00:23:14 Duration
Lecture 8 Optional Debugging Warnings In Jupyter 00:18:58 Duration
Lecture 9 Getting Your Data Ready Splitting Your Data 00:08:37 Duration
Lecture 10 Quick Tip Clean, Transform, Reduce 00:05:03 Duration
Lecture 11 Getting Your Data Ready Convert Data To Numbers 00:16:54 Duration
Lecture 12 Getting Your Data Ready Handling Missing Values With Pandas 00:12:22 Duration
Lecture 13 Extension Feature Scaling
Lecture 14 Note Correction in the upcoming video (splitting data)
Lecture 15 Getting Your Data Ready Handling Missing Values With Scikit-learn 00:17:29 Duration
Lecture 16 Choosing The Right Model For Your Data 00:14:54 Duration
Lecture 17 Choosing The Right Model For Your Data 2 (Regression) 00:08:41 Duration
Lecture 18 Quick Note Decision Trees
Lecture 19 Quick Tip How ML Algorithms Work 00:01:25 Duration
Lecture 20 Choosing The Right Model For Your Data 3 (Classification) 00:12:45 Duration
Lecture 21 Fitting A Model To The Data 00:06:45 Duration
Lecture 22 Making Predictions With Our Model 00:08:25 Duration
Lecture 23 predict() vs predict_proba() 00:08:33 Duration
Lecture 24 Making Predictions With Our Model (Regression) 00:06:50 Duration
Lecture 25 Evaluating A Machine Learning Model (Score) 00:08:58 Duration
Lecture 26 Evaluating A Machine Learning Model 2 (Cross Validation) 00:13:16 Duration
Lecture 27 Evaluating A Classification Model 1 (Accuracy) 00:04:46 Duration
Lecture 28 Evaluating A Classification Model 2 (ROC Curve) 00:09:04 Duration
Lecture 29 Evaluating A Classification Model 3 (ROC Curve) 00:07:44 Duration
Lecture 30 Reading Extension ROC Curve + AUC
Lecture 31 Evaluating A Classification Model 4 (Confusion Matrix) 00:11:01 Duration
Lecture 32 Evaluating A Classification Model 5 (Confusion Matrix) 00:08:07 Duration
Lecture 33 Evaluating A Classification Model 6 (Classification Report) 00:10:17 Duration
Lecture 34 Evaluating A Regression Model 1 (R2 Score) 00:09:13 Duration
Lecture 35 Evaluating A Regression Model 2 (MAE) 00:04:18 Duration
Lecture 36 Evaluating A Regression Model 3 (MSE) 00:06:34 Duration
Lecture 37 Machine Learning Model Evaluation
Lecture 38 Evaluating A Model With Cross Validation and Scoring Parameter 00:14:05 Duration
Lecture 39 Evaluating A Model With Scikit-learn Functions 00:12:15 Duration
Lecture 40 Improving A Machine Learning Model 00:11:17 Duration
Lecture 41 Tuning Hyperparameters 00:23:15 Duration
Lecture 42 Tuning Hyperparameters 2 00:14:23 Duration
Lecture 43 Tuning Hyperparameters 3 00:14:59 Duration
Lecture 44 Note Metric Comparison Improvement
Lecture 45 Quick Tip Correlation Analysis 00:02:28 Duration
Lecture 46 Saving And Loading A Model 00:07:29 Duration
Lecture 47 Saving And Loading A Model 2 00:06:20 Duration
Lecture 48 Putting It All Together 00:20:20 Duration
Lecture 49 Putting It All Together 2 00:02:09 Duration
Lecture 50 Scikit-Learn Practice

Section 10 : Supervised Learning Classification + Regression
Lecture 1 Milestone Projects!

Section 11 : Milestone Project 1 Supervised Learning (Classification)
Lecture 1 Section Overview 00:11:34 Duration
Lecture 2 Project Overview 00:06:10 Duration
Lecture 3 Project Environment Setup 00:10:59 Duration
Lecture 4 Optional Windows Project Environment Setup 00:04:52 Duration
Lecture 5 Step 1~4 Framework Setup 00:12:06 Duration
Lecture 6 Getting Our Tools Ready 00:09:04 Duration
Lecture 7 Exploring Our Data 00:08:34 Duration
Lecture 8 Finding Patterns 00:10:03 Duration
Lecture 9 Finding Patterns 2 00:16:48 Duration
Lecture 10 Finding Patterns 3 00:13:37 Duration
Lecture 11 Preparing Our Data For Machine Learning 00:08:52 Duration
Lecture 12 Choosing The Right Models 00:10:15 Duration
Lecture 13 Experimenting With Machine Learning Models 00:06:32 Duration
Lecture 14 TuningImproving Our Model 00:13:49 Duration
Lecture 15 Tuning Hyperparameters 00:11:28 Duration
Lecture 16 Tuning Hyperparameters 2 00:11:50 Duration
Lecture 17 Tuning Hyperparameters 3 00:07:07 Duration
Lecture 18 Quick Note Confusion Matrix Labels
Lecture 19 Evaluating Our Model 00:11:01 Duration
Lecture 20 Evaluating Our Model 2 00:05:56 Duration
Lecture 21 Evaluating Our Model 3 00:08:50 Duration
Lecture 22 Finding The Most Important Features 00:16:07 Duration
Lecture 23 Reviewing The Project 00:09:13 Duration

Section 12 : Milestone Project 2 Supervised Learning (Time Series Data)
Lecture 1 Section Overview 00:01:07 Duration
Lecture 2 Project Overview 00:04:24 Duration
Lecture 3 Project Environment Setup 00:10:52 Duration
Lecture 4 Step 1~4 Framework Setup 00:08:36 Duration
Lecture 5 Downloading the data for the next two projects
Lecture 6 Exploring Our Data 00:14:16 Duration
Lecture 7 Exploring Our Data 2 00:06:17 Duration
Lecture 8 Feature Engineering 00:15:24 Duration
Lecture 9 Turning Data Into Numbers 00:15:38 Duration
Lecture 10 Filling Missing Numerical Values 00:12:49 Duration
Lecture 11 Filling Missing Categorical Values 00:08:27 Duration
Lecture 12 Fitting A Machine Learning Model 00:07:16 Duration
Lecture 13 Splitting Data 00:10:01 Duration
Lecture 14 Challenge What's wrong with splitting data after filling it
Lecture 15 Custom Evaluation Function 00:11:13 Duration
Lecture 16 Reducing Data 00:10:36 Duration
Lecture 17 RandomizedSearchCV 00:09:32 Duration
Lecture 18 Improving Hyperparameters 00:08:11 Duration
Lecture 19 Preproccessing Our Data 00:13:16 Duration
Lecture 20 Making Predictions 00:09:18 Duration
Lecture 21 Feature Importance 00:13:50 Duration

Section 13 : Data Engineering
Lecture 1 Data Engineering Introduction 00:03:24 Duration
Lecture 2 Optional OLTP Databases 00:10:54 Duration
Lecture 3 What Is Data 00:06:42 Duration
Lecture 4 What Is A Data Engineer 00:04:21 Duration
Lecture 5 What Is A Data Engineer 2 00:05:36 Duration
Lecture 6 What Is A Data Engineer 3 00:05:04 Duration
Lecture 7 What Is A Data Engineer 4 00:03:23 Duration
Lecture 8 Types Of Databases 00:06:50 Duration
Lecture 9 Quick Note Upcoming Video
Lecture 10 Optional Learn SQL
Lecture 11 Hadoop, HDFS and MapReduce 00:04:23 Duration
Lecture 12 Apache Spark and Apache Flink 00:02:08 Duration
Lecture 13 Kafka and Stream Processing 00:04:33 Duration

Section 14 : Neural Networks Deep Learning, Transfer Learning and TensorFlow 2
Lecture 1 Section Overview 00:02:06 Duration
Lecture 2 Deep Learning and Unstructured Data 00:13:36 Duration
Lecture 3 Setting Up With Google
Lecture 4 Setting Up Google Colab 00:07:17 Duration
Lecture 5 Google Colab Workspace 00:04:23 Duration
Lecture 6 Uploading Project Data 00:06:52 Duration
Lecture 7 Setting Up Our Data 00:04:41 Duration
Lecture 8 Setting Up Our Data 2 00:01:32 Duration
Lecture 9 Importing TensorFlow 2 00:12:44 Duration
Lecture 10 Optional TensorFlow 2 00:03:39 Duration
Lecture 11 Using A GPU 00:09:00 Duration
Lecture 12 Optional GPU and Google Colab 00:04:27 Duration
Lecture 13 Optional Reloading Colab Notebook 00:06:50 Duration
Lecture 14 Loading Our Data Labels 00:12:05 Duration
Lecture 15 Preparing The Images 00:12:32 Duration
Lecture 16 Turning Data Labels Into Numbers 00:12:12 Duration
Lecture 17 Creating Our Own Validation Set 00:09:18 Duration
Lecture 18 Preprocess Images 00:10:26 Duration
Lecture 19 Preprocess Images 2 00:11:00 Duration
Lecture 20 Turning Data Into Batches 00:09:37 Duration
Lecture 21 Turning Data Into Batches 2 00:17:55 Duration
Lecture 22 Visualizing Our Data 00:12:42 Duration
Lecture 23 Preparing Our Inputs and Outputs 00:06:38 Duration
Lecture 24 Optional How machines learn and what's going on behind the scenes
Lecture 25 Building A Deep Learning Model 00:11:42 Duration
Lecture 26 Building A Deep Learning Model 2 00:10:53 Duration
Lecture 27 Building A Deep Learning Model 3 00:09:06 Duration
Lecture 28 Building A Deep Learning Model 4 00:09:12 Duration
Lecture 29 Summarizing Our Model 00:04:52 Duration
Lecture 30 Evaluating Our Model 00:09:27 Duration
Lecture 31 Preventing Overfitting 00:04:20 Duration
Lecture 32 Training Your Deep Neural Network 00:19:10 Duration
Lecture 33 Evaluating Performance With TensorBoard 00:07:31 Duration
Lecture 34 Make And Transform Predictions 00:15:05 Duration
Lecture 35 Transform Predictions To Text 00:15:20 Duration
Lecture 36 Visualizing Model Predictions
Lecture 37 Visualizing And Evaluate Model Predictions 2 00:15:52 Duration
Lecture 38 Visualizing And Evaluate Model Predictions 3 00:10:40 Duration
Lecture 39 Saving And Loading A Trained Model 00:13:34 Duration
Lecture 40 Training Model On Full Dataset 00:15:02 Duration
Lecture 41 Making Predictions On Test Images 00:16:55 Duration
Lecture 42 Submitting Model to Kaggle
Lecture 43 Making Predictions On Our Images 00:15:15 Duration
Lecture 44 Finishing Dog Vision Where to next

Section 15 : Storytelling + Communication How To Present Your Work
Lecture 1 Section Overview 00:02:19 Duration
Lecture 2 Communicating Your Work 00:03:22 Duration
Lecture 3 Communicating With Managers 00:02:58 Duration
Lecture 4 Communicating With Co-Workers 00:03:43 Duration
Lecture 5 Weekend Project Principle 00:06:32 Duration
Lecture 6 Communicating With Outside World 00:03:29 Duration
Lecture 7 Storytelling 00:03:06 Duration
Lecture 8 Communicating and sharing your work Further reading

Section 16 : Career Advice + Extra Bits
Lecture 1 Endorsements On LinkedIn
Lecture 2 Quick Note Upcoming Video
Lecture 3 What If I Don't Have Enough Experience 00:15:03 Duration
Lecture 4 Learning Guideline
Lecture 5 Quick Note Upcoming Videos
Lecture 6 JTS Learn to Learn 00:02:00 Duration
Lecture 7 JTS Start With Why 00:02:44 Duration
Lecture 8 Quick Note Upcoming Videos
Lecture 9 CWD Git + Github 00:17:40 Duration
Lecture 10 CWD Git + Github 2 00:16:53 Duration
Lecture 11 Contributing To Open Source 00:14:44 Duration
Lecture 12 Contributing To Open Source 2 00:09:43 Duration
Lecture 13 Coding Challenges
Lecture 14 Exercise Contribute To Open Source

Section 17 : Learn Python
Lecture 1 What Is A Programming Language 00:06:24 Duration
Lecture 2 Python Interpreter 00:07:04 Duration
Lecture 3 How To Run Python Code 00:04:53 Duration
Lecture 4 Our First Python Program 00:07:44 Duration
Lecture 5 Latest Version Of Python 00:01:58 Duration
Lecture 6 Python 2 vs Python 3 00:06:41 Duration
Lecture 7 Exercise How Does Python Work 00:02:10 Duration
Lecture 8 Learning Python 00:02:05 Duration
Lecture 9 Python Data Types 00:04:46 Duration
Lecture 10 How To Succeed
Lecture 11 Numbers 00:11:09 Duration
Lecture 12 Math Functions 00:04:29 Duration
Lecture 13 DEVELOPER FUNDAMENTALS I 00:04:07 Duration
Lecture 14 Operator Precedence 00:03:10 Duration
Lecture 15 Exercise Operator Precedence
Lecture 16 Optional bin() and complex 00:04:02 Duration
Lecture 17 Variables 00:13:13 Duration
Lecture 18 Expressions vs Statements 00:01:37 Duration
Lecture 19 Augmented Assignment Operator 00:02:49 Duration
Lecture 20 Strings 00:05:30 Duration
Lecture 21 String Concatenation 00:01:16 Duration
Lecture 22 Type Conversion 00:03:03 Duration
Lecture 23 Escape Sequences 00:04:24 Duration
Lecture 24 Formatted Strings 00:08:24 Duration
Lecture 25 String Indexes 00:08:57 Duration
Lecture 26 Immutability 00:03:14 Duration
Lecture 27 Built-In Functions + Methods 00:10:04 Duration
Lecture 28 Booleans 00:03:22 Duration
Lecture 29 Exercise Type Conversion 00:08:23 Duration
Lecture 30 DEVELOPER FUNDAMENTALS II 00:04:42 Duration
Lecture 31 Exercise Password Checker 00:07:21 Duration
Lecture 32 Lists 00:05:01 Duration
Lecture 33 List Slicing 00:07:48 Duration
Lecture 34 Matrix 00:04:11 Duration
Lecture 35 List Methods 00:10:28 Duration
Lecture 36 List Methods 2 00:04:24 Duration
Lecture 37 List Methods 3 00:04:52 Duration
Lecture 38 Common List Patterns 00:05:57 Duration
Lecture 39 List Unpacking 00:02:41 Duration
Lecture 40 None 00:01:51 Duration
Lecture 41 Dictionaries 00:06:21 Duration
Lecture 42 DEVELOPER FUNDAMENTALS III 00:02:40 Duration
Lecture 43 Dictionary Keys 00:03:37 Duration
Lecture 44 Dictionary Methods 00:04:37 Duration
Lecture 45 Dictionary Methods 2 00:07:04 Duration
Lecture 46 Tuples 00:04:47 Duration
Lecture 47 Tuples 2 00:03:15 Duration
Lecture 48 Sets 00:07:24 Duration
Lecture 49 Sets 2 00:08:45 Duration

Section 18 : Learn Python Part 2
Lecture 1 Breaking The Flow 00:02:35 Duration
Lecture 2 Conditional Logic 00:13:18 Duration
Lecture 3 Indentation In Python 00:04:39 Duration
Lecture 4 Truthy vs Falsey 00:05:18 Duration
Lecture 5 Ternary Operator 00:04:14 Duration
Lecture 6 Short Circuiting 00:04:02 Duration
Lecture 7 Logical Operators 00:06:56 Duration
Lecture 8 Exercise Logical Operators 00:07:48 Duration
Lecture 9 is vs == 00:07:36 Duration
Lecture 10 For Loops 00:07:01 Duration
Lecture 11 Iterables 00:06:44 Duration
Lecture 12 Exercise Tricky Counter 00:03:23 Duration
Lecture 13 range() 00:04:37 Duration
Lecture 14 enumerate() 00:05:39 Duration
Lecture 15 While Loops 00:06:28 Duration
Lecture 16 While Loops 2 00:05:50 Duration
Lecture 17 break, continue, pass 00:04:16 Duration
Lecture 18 Our First GUI 00:08:49 Duration
Lecture 19 DEVELOPER FUNDAMENTALS IV 00:06:34 Duration
Lecture 20 Exercise Find Duplicates 00:03:55 Duration
Lecture 21 Functions 00:07:41 Duration
Lecture 22 Parameters and Arguments 00:04:25 Duration
Lecture 23 Default Parameters and Keyword Arguments 00:05:41 Duration
Lecture 24 return 00:13:11 Duration
Lecture 25 Exercise Tesla
Lecture 26 Methods vs Functions 00:04:33 Duration
Lecture 27 Docstrings 00:03:47 Duration
Lecture 28 Clean Code 00:04:38 Duration
Lecture 29 args and kwargs 00:07:57 Duration
Lecture 30 Exercise Functions 00:04:18 Duration
Lecture 31 Scope 00:03:38 Duration
Lecture 32 Scope Rules 00:06:55 Duration
Lecture 33 global Keyword 00:06:13 Duration
Lecture 34 nonlocal Keyword
Lecture 35 Why Do We Need Scope 00:03:39 Duration
Lecture 36 Pure Functions 00:09:23 Duration
Lecture 37 map() 00:06:31 Duration
Lecture 38 filter() 00:04:23 Duration
Lecture 39 zip() 00:03:28 Duration
Lecture 40 reduce() 00:07:32 Duration
Lecture 41 List Comprehensions 00:08:37 Duration
Lecture 42 Set Comprehensions 00:06:27 Duration
Lecture 43 Exercise Comprehensions 00:04:37 Duration
Lecture 44 Python Exam Testing Your Understanding
Lecture 45 Modules in Python 00:10:55 Duration
Lecture 46 Quick Note Upcoming Videos
Lecture 47 Optional PyCharm 00:08:19 Duration
Lecture 48 Packages in Python 00:10:45 Duration
Lecture 49 Different Ways To Import 00:07:04 Duration
Lecture 50 Next Steps

Section 19 : Bonus Learn Advanced Statistics and Mathematics for FREE!
Lecture 1 Statistics and Mathematics

Section 20 : Where To Go From Here
Lecture 1 Become An Alumni
Lecture 2 Thank You 00:02:44 Duration

Section 21 : BONUS SECTION
Lecture 1 Bonus Lecture