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Machine Learning I Data Science Course
Machine Learning I Data Science Course
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
Preview
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
Preview
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
Preview
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
Preview
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
Preview
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
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Lectures
372
Video
42:24:36 Hours
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Languages
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