Section 1 : Introduction

 		Lecture 1 Course Outline copy 5:59
Lecture 2 Join Our Online Classroom! Text
Lecture 3 Exercise Meet The Community Text
Lecture 4 Your First Day 3:48

Section 2 : Machine Learning 101

 		Lecture 5 What Is Machine Learning 6:52
Lecture 6 AIMachine LearningData Science 4:51
Lecture 7 Exercise Machine Learning Playground 6:16
Lecture 8 How Did We Get Here 6:3
Lecture 9 Exercise YouTube Recommendation Engine 4:25
Lecture 10 Types of Machine Learning 4:41
Lecture 11 Are You Getting It Yet Text
Lecture 12 What Is Machine Learning Round 2 4:45
Lecture 13 Section Review 1:48

Section 3 : Machine Learning and Data Science Framework

 		Lecture 14 Section Overview 3:9
Lecture 15 Introducing Our Framework 2:38
Lecture 16 Step Machine Learning Framework 4:59
Lecture 17 Types of Machine Learning Problems 10:32
Lecture 18 Types of Data 4:51
Lecture 19 Types of Evaluation 3:31
Lecture 20 Features In Data 5:22
Lecture 21 Modelling - Splitting Data
Lecture 22 Modelling - Picking the Model 4:35
Lecture 23 Modelling - Tuning 3:17
Lecture 24 Modelling - Comparison 9:32
Lecture 25 Overfitting and Underfitting Definitions Text
Lecture 26 Experimentation 3:35
Lecture 27 Tools We Will Use 4:0
Lecture 28 Optional Elements of AI Text

Section 4 : The 2 Paths

 		Lecture 29 The 2 Paths 3:27
Lecture 30 Python + Machine Learning Monthly Text
Lecture 31 Endorsements On LinkedIN Text

Section 5 : Data Science Environment Setup

 		Lecture 32 Section Overview 1:9
Lecture 33 Introducing Our Tools 3:29
Lecture 34 What is Conda 2:35
Lecture 35 Conda Environments 4:30
Lecture 36 Mac Environment Setup 17:27
Lecture 37 Mac Environment Setup 2 14:11
Lecture 38 Windows Environment Setup 5:17
Lecture 39 Windows Environment Setup 2 23:18
Lecture 40 Linux Environment Setup Text
Lecture 41 Sharing your Conda Environment Text
Lecture 42 Jupyter Notebook Walkthrough
Lecture 43 Jupyter Notebook Walkthrough 2 16:18
Lecture 44 Jupyter Notebook Walkthrough 3 8:10

Section 6 : Pandas Data Analysis

 		Lecture 45 Section Overview 2:27
Lecture 46 Downloading Workbooks and Assignments Text
Lecture 47 Pandas Introduction 4:29
Lecture 48 Series, Data Frames and CSVs 13:21
Lecture 49 Data from URLs Text
Lecture 50 Describing Data with Pandas 9:49
Lecture 51 Selecting and Viewing Data with Pandas 11:8
Lecture 52 Selecting and Viewing Data with Pandas Part 2 13:7
Lecture 53 Manipulating Data 13:57
Lecture 54 Manipulating Data 2 9:57
Lecture 55 Manipulating Data 3 10:12
Lecture 56 Assignment Pandas Practice Text
Lecture 57 How To Download The Course Assignments 7:43

Section 7 : NumPy

 		Lecture 58 Section Overview 2:41
Lecture 59 NumPy Introduction 5:18
Lecture 60 Quick Note Correction In Next Video Text
Lecture 61 NumPy DataTypes and Attributes 14:6
Lecture 62 Creating NumPy Arrays 9:22
Lecture 63 NumPy Random Seed 7:17
Lecture 64 Viewing Arrays and Matrices 9:35
Lecture 65 Manipulating Arrays 11:32
Lecture 66 Manipulating Arrays 2 9:44
Lecture 67 Standard Deviation and Variance 7:10
Lecture 68 Reshape and Transpose 7:27
Lecture 69 Dot Product vs Element Wise 11:45
Lecture 70 Exercise Nut Butter Store Sales 13:4
Lecture 71 Comparison Operators 3:34
Lecture 72 Sorting Arrays 6:20
Lecture 73 Turn Images Into NumPy Arrays 7:37
Lecture 74 Assignment NumPy Practice Text
Lecture 75 Optional Extra NumPy resources Text

Section 8 : Matplotlib Plotting and Data Visualization

 		Lecture 76 Section Overview 1:51
Lecture 77 Matplotlib Introduction 5:17
Lecture 78 Importing And Using Matplotlib 11:36
Lecture 79 Anatomy Of A Matplotlib Figure 9:19
Lecture 80 Scatter Plot And Bar Plot 10:9
Lecture 81 Histograms And Subplots 8:40
Lecture 82 Subplots Option 2 4:15
Lecture 83 Quick Tip Data Visualizations 1:48
Lecture 84 Plotting From Pandas DataFrames 5:58
Lecture 85 Quick Note Regular Expressions Text
Lecture 86 Plotting From Pandas DataFrames 2 10:33
Lecture 87 Plotting from Pandas DataFrames 3 8:33
Lecture 88 Plotting from Pandas DataFrames 4 6:36
Lecture 89 Plotting from Pandas DataFrames 5 8:29
Lecture 90 Plotting from Pandas DataFrames 6 8:29
Lecture 91 Plotting from Pandas DataFrames 7 11:20
Lecture 92 Customizing Your Plots 10:10
Lecture 93 Customizing Your Plots 2 9:41
Lecture 94 Saving And Sharing Your Plots 4:14
Lecture 95 Assignment Matplotlib Practice Text

Section 9 : Scikit-learn Creating Machine Learning Models

 		Lecture 96 Section Overview 2:30
Lecture 97 Scikit-learn Introduction 6:41
Lecture 98 Quick Note Upcoming Video Text
Lecture 99 Refresher What Is Machine Learning 5:40
Lecture 100 Quick Note Upcoming Videos Text
Lecture 101 Scikit-learn Cheatsheet 6:13
Lecture 102 Typical scikit-learn Workflow 23:14
Lecture 103 Optional Debugging Warnings In Jupyter 18:58
Lecture 104 Getting Your Data Ready Splitting Your Data 8:37
Lecture 105 Quick Tip Clean, Transform, Reduce 5:3
Lecture 106 Getting Your Data Ready Convert Data To Numbers 16:54
Lecture 107 Getting Your Data Ready Handling Missing Values With Pandas 12:22
Lecture 108 Extension Feature Scaling Text
Lecture 109 Note Correction in the upcoming video (splitting data) Text
Lecture 110 Getting Your Data Ready Handling Missing Values With Scikit-learn 17:29
Lecture 111 Choosing The Right Model For Your Data 14:54
Lecture 112 Choosing The Right Model For Your Data 2 (Regression) 8:41
Lecture 113 Quick Note Decision Trees Text
Lecture 114 Quick Tip How ML Algorithms Work 1:25
Lecture 115 Choosing The Right Model For Your Data 3 (Classification) 12:45
Lecture 116 Fitting A Model To The Data 6:45
Lecture 117 Making Predictions With Our Model 8:25
Lecture 118 predict() vs predict_proba() 8:33
Lecture 119 Making Predictions With Our Model (Regression) 6:50
Lecture 120 Evaluating A Machine Learning Model (Score) 8:58
Lecture 121 Evaluating A Machine Learning Model 2 (Cross Validation) 13:16
Lecture 122 Evaluating A Classification Model 1 (Accuracy) 4:46
Lecture 123 Evaluating A Classification Model 2 (ROC Curve) 9:4
Lecture 124 Evaluating A Classification Model 3 (ROC Curve) 7:44
Lecture 125 Reading Extension ROC Curve + AUC Text
Lecture 126 Evaluating A Classification Model 4 (Confusion Matrix) 11:1
Lecture 127 Evaluating A Classification Model 5 (Confusion Matrix) 8:7
Lecture 128 Evaluating A Classification Model 6 (Classification Report) 10:17
Lecture 129 Evaluating A Regression Model 1 (R2 Score) 9:13
Lecture 130 Evaluating A Regression Model 2 (MAE) 4:18
Lecture 131 Evaluating A Regression Model 3 (MSE) 6:34
Lecture 132 Machine Learning Model Evaluation Text
Lecture 133 Evaluating A Model With Cross Validation and Scoring Parameter 14:5
Lecture 134 Evaluating A Model With Scikit-learn Functions 12:15
Lecture 135 Improving A Machine Learning Model 11:17
Lecture 136 Tuning Hyperparameters 23:15
Lecture 137 Tuning Hyperparameters 2 14:23
Lecture 138 Tuning Hyperparameters 3 14:59
Lecture 139 Note Metric Comparison Improvement Text
Lecture 140 Quick Tip Correlation Analysis 2:28
Lecture 141 Saving And Loading A Model 7:29
Lecture 142 Saving And Loading A Model 2 6:20
Lecture 143 Putting It All Together 20:20
Lecture 144 Putting It All Together 2 2:9
Lecture 145 Scikit-Learn Practice Text

Section 10 : Supervised Learning Classification + Regression

 		Lecture 146 Milestone Projects! Text

Section 11 : Milestone Project 1 Supervised Learning (Classification)

 		Lecture 147 Section Overview 11:34
Lecture 148 Project Overview 6:10
Lecture 149 Project Environment Setup 10:59
Lecture 150 Optional Windows Project Environment Setup 4:52
Lecture 151 Step 1~4 Framework Setup 12:6
Lecture 152 Getting Our Tools Ready 9:4
Lecture 153 Exploring Our Data 8:34
Lecture 154 Finding Patterns 10:3
Lecture 155 Finding Patterns 2 16:48
Lecture 156 Finding Patterns 3 13:37
Lecture 157 Preparing Our Data For Machine Learning 8:52
Lecture 158 Choosing The Right Models 10:15
Lecture 159 Experimenting With Machine Learning Models 6:32
Lecture 160 TuningImproving Our Model 13:49
Lecture 161 Tuning Hyperparameters 11:28
Lecture 162 Tuning Hyperparameters 2 11:50
Lecture 163 Tuning Hyperparameters 3 7:7
Lecture 164 Quick Note Confusion Matrix Labels Text
Lecture 165 Evaluating Our Model 11:1
Lecture 166 Evaluating Our Model 2 5:56
Lecture 167 Evaluating Our Model 3 8:50
Lecture 168 Finding The Most Important Features 16:7
Lecture 169 Reviewing The Project 9:13

Section 12 : Milestone Project 2 Supervised Learning (Time Series Data)

 		Lecture 170 Section Overview 1:7
Lecture 171 Project Overview 4:24
Lecture 172 Project Environment Setup 10:52
Lecture 173 Step 1~4 Framework Setup 8:36
Lecture 174 Downloading the data for the next two projects Text
Lecture 175 Exploring Our Data 14:16
Lecture 176 Exploring Our Data 2 6:17
Lecture 177 Feature Engineering 15:24
Lecture 178 Turning Data Into Numbers 15:38
Lecture 179 Filling Missing Numerical Values 12:49
Lecture 180 Filling Missing Categorical Values 8:27
Lecture 181 Fitting A Machine Learning Model 7:16
Lecture 182 Splitting Data 10:1
Lecture 183 Challenge What's wrong with splitting data after filling it Text
Lecture 184 Custom Evaluation Function 11:13
Lecture 185 Reducing Data 10:36
Lecture 186 RandomizedSearchCV 9:32
Lecture 187 Improving Hyperparameters 8:11
Lecture 188 Preproccessing Our Data 13:16
Lecture 189 Making Predictions 9:18
Lecture 190 Feature Importance 13:50

Section 13 : Data Engineering

 		Lecture 191 Data Engineering Introduction 3:24
Lecture 192 Optional OLTP Databases 10:54
Lecture 193 What Is Data 6:42
Lecture 194 What Is A Data Engineer 4:21
Lecture 195 What Is A Data Engineer 2 5:36
Lecture 196 What Is A Data Engineer 3 5:4
Lecture 197 What Is A Data Engineer 4 3:23
Lecture 198 Types Of Databases 6:50
Lecture 199 Quick Note Upcoming Video Text
Lecture 200 Optional Learn SQL Text
Lecture 201 Hadoop, HDFS and MapReduce 4:23
Lecture 202 Apache Spark and Apache Flink 2:8
Lecture 203 Kafka and Stream Processing 4:33

Section 14 : Neural Networks Deep Learning, Transfer Learning and TensorFlow 2

 		Lecture 204 Section Overview 2:6
Lecture 205 Deep Learning and Unstructured Data 13:36
Lecture 206 Setting Up With Google Text
Lecture 207 Setting Up Google Colab 7:17
Lecture 208 Google Colab Workspace 4:23
Lecture 209 Uploading Project Data 6:52
Lecture 210 Setting Up Our Data 4:41
Lecture 211 Setting Up Our Data 2 1:32
Lecture 212 Importing TensorFlow 2 12:44
Lecture 213 Optional TensorFlow 2 3:39
Lecture 214 Using A GPU 9:0
Lecture 215 Optional GPU and Google Colab 4:27
Lecture 216 Optional Reloading Colab Notebook 6:50
Lecture 217 Loading Our Data Labels 12:5
Lecture 218 Preparing The Images 12:32
Lecture 219 Turning Data Labels Into Numbers 12:12
Lecture 220 Creating Our Own Validation Set 9:18
Lecture 221 Preprocess Images 10:26
Lecture 222 Preprocess Images 2 11:0
Lecture 223 Turning Data Into Batches 9:37
Lecture 224 Turning Data Into Batches 2 17:55
Lecture 225 Visualizing Our Data 12:42
Lecture 226 Preparing Our Inputs and Outputs 6:38
Lecture 227 Optional How machines learn and what's going on behind the scenes Text
Lecture 228 Building A Deep Learning Model 11:42
Lecture 229 Building A Deep Learning Model 2 10:53
Lecture 230 Building A Deep Learning Model 3 9:6
Lecture 231 Building A Deep Learning Model 4 9:12
Lecture 232 Summarizing Our Model 4:52
Lecture 233 Evaluating Our Model 9:27
Lecture 234 Preventing Overfitting 4:20
Lecture 235 Training Your Deep Neural Network 19:10
Lecture 236 Evaluating Performance With TensorBoard 7:31
Lecture 237 Make And Transform Predictions 15:5
Lecture 238 Transform Predictions To Text 15:20
Lecture 239 Visualizing Model Predictions
Lecture 240 Visualizing And Evaluate Model Predictions 2 15:52
Lecture 241 Visualizing And Evaluate Model Predictions 3 10:40
Lecture 242 Saving And Loading A Trained Model 13:34
Lecture 243 Training Model On Full Dataset 15:2
Lecture 244 Making Predictions On Test Images 16:55
Lecture 245 Submitting Model to Kaggle
Lecture 246 Making Predictions On Our Images 15:15
Lecture 247 Finishing Dog Vision Where to next Text

Section 15 : Storytelling + Communication How To Present Your Work

 		Lecture 248 Section Overview 2:19
Lecture 249 Communicating Your Work 3:22
Lecture 250 Communicating With Managers 2:58
Lecture 251 Communicating With Co-Workers 3:43
Lecture 252 Weekend Project Principle 6:32
Lecture 253 Communicating With Outside World 3:29
Lecture 254 Storytelling 3:6
Lecture 255 Communicating and sharing your work Further reading Text

Section 16 : Career Advice + Extra Bits

 		Lecture 256 Endorsements On LinkedIn Text
Lecture 257 Quick Note Upcoming Video Text
Lecture 258 What If I Don't Have Enough Experience 15:3
Lecture 259 Learning Guideline Text
Lecture 260 Quick Note Upcoming Videos Text
Lecture 261 JTS Learn to Learn 2:0
Lecture 262 JTS Start With Why 2:44
Lecture 263 Quick Note Upcoming Videos Text
Lecture 264 CWD Git + Github 17:40
Lecture 265 CWD Git + Github 2 16:53
Lecture 266 Contributing To Open Source 14:44
Lecture 267 Contributing To Open Source 2 9:43
Lecture 268 Coding Challenges Text
Lecture 269 Exercise Contribute To Open Source Text

Section 17 : Learn Python

 		Lecture 270 What Is A Programming Language 6:24
Lecture 271 Python Interpreter 7:4
Lecture 272 How To Run Python Code 4:53
Lecture 273 Our First Python Program 7:44
Lecture 274 Latest Version Of Python 1:58
Lecture 275 Python 2 vs Python 3 6:41
Lecture 276 Exercise How Does Python Work 2:10
Lecture 277 Learning Python 2:5
Lecture 278 Python Data Types 4:46
Lecture 279 How To Succeed Text
Lecture 280 Numbers 11:9
Lecture 281 Math Functions 4:29
Lecture 282 DEVELOPER FUNDAMENTALS I 4:7
Lecture 283 Operator Precedence 3:10
Lecture 284 Exercise Operator Precedence Pdf
Lecture 285 Optional bin() and complex 4:2
Lecture 286 Variables 13:13
Lecture 287 Expressions vs Statements 1:37
Lecture 288 Augmented Assignment Operator 2:49
Lecture 289 Strings 5:30
Lecture 290 String Concatenation 1:16
Lecture 291 Type Conversion 3:3
Lecture 292 Escape Sequences 4:24
Lecture 293 Formatted Strings 8:24
Lecture 294 String Indexes 8:57
Lecture 295 Immutability 3:14
Lecture 296 Built-In Functions + Methods 10:4
Lecture 297 Booleans 3:22
Lecture 298 Exercise Type Conversion 8:23
Lecture 299 DEVELOPER FUNDAMENTALS II 4:42
Lecture 300 Exercise Password Checker 7:21
Lecture 301 Lists 5:1
Lecture 302 List Slicing 7:48
Lecture 303 Matrix 4:11
Lecture 304 List Methods 10:28
Lecture 305 List Methods 2 4:24
Lecture 306 List Methods 3 4:52
Lecture 307 Common List Patterns 5:57
Lecture 308 List Unpacking 2:41
Lecture 309 None 1:51
Lecture 310 Dictionaries 6:21
Lecture 311 DEVELOPER FUNDAMENTALS III 2:40
Lecture 312 Dictionary Keys 3:37
Lecture 313 Dictionary Methods 4:37
Lecture 314 Dictionary Methods 2 7:4
Lecture 315 Tuples 4:47
Lecture 316 Tuples 2 3:15
Lecture 317 Sets 7:24
Lecture 318 Sets 2 8:45

Section 18 : Learn Python Part 2

 		Lecture 319 Breaking The Flow 2:35
Lecture 320 Conditional Logic 13:18
Lecture 321 Indentation In Python 4:39
Lecture 322 Truthy vs Falsey 5:18
Lecture 323 Ternary Operator 4:14
Lecture 324 Short Circuiting 4:2
Lecture 325 Logical Operators 6:56
Lecture 326 Exercise Logical Operators 7:48
Lecture 327 is vs == 7:36
Lecture 328 For Loops 7:1
Lecture 329 Iterables 6:44
Lecture 330 Exercise Tricky Counter 3:23
Lecture 331 range() 4:37
Lecture 332 enumerate() 5:39
Lecture 333 While Loops 6:28
Lecture 334 While Loops 2 5:50
Lecture 335 break, continue, pass 4:16
Lecture 336 Our First GUI 8:49
Lecture 337 DEVELOPER FUNDAMENTALS IV 6:34
Lecture 338 Exercise Find Duplicates 3:55
Lecture 339 Functions 7:41
Lecture 340 Parameters and Arguments 4:25
Lecture 341 Default Parameters and Keyword Arguments 5:41
Lecture 342 return 13:11
Lecture 343 Exercise Tesla Text
Lecture 344 Methods vs Functions 4:33
Lecture 345 Docstrings 3:47
Lecture 346 Clean Code 4:38
Lecture 347 args and kwargs 7:57
Lecture 348 Exercise Functions 4:18
Lecture 349 Scope 3:38
Lecture 350 Scope Rules 6:55
Lecture 351 global Keyword 6:13
Lecture 352 nonlocal Keyword
Lecture 353 Why Do We Need Scope 3:39
Lecture 354 Pure Functions 9:23
Lecture 355 map() 6:31
Lecture 356 filter() 4:23
Lecture 357 zip() 3:28
Lecture 358 reduce() 7:32
Lecture 359 List Comprehensions 8:37
Lecture 360 Set Comprehensions 6:27
Lecture 361 Exercise Comprehensions 4:37
Lecture 362 Python Exam Testing Your Understanding Text
Lecture 363 Modules in Python 10:55
Lecture 364 Quick Note Upcoming Videos Text
Lecture 365 Optional PyCharm 8:19
Lecture 366 Packages in Python 10:45
Lecture 367 Different Ways To Import 7:4
Lecture 368 Next Steps Text

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

 		Lecture 369 Statistics and Mathematics Text

Section 20 : Where To Go From Here

 		Lecture 370 Become An Alumni Text
Lecture 371 Thank You 2:44

Section 21 : BONUS SECTION

 		Lecture 372 Bonus Lecture Text