Complete A.I. & Machine Learning, Data Science Bootcamp
Categories:Machine LearningData Science & AI
$49.99$9.99Save 80%
What's included
Certificate of completion
Create an account to start learning
Course OutlineFree Preview
5:59
Join Our Online Classroom
4:01
Exercise Meet Your Classmates Instructor
Asking Questions Getting Help
Your First Day
3:48
What Is Machine Learning
6:52
AIMachine LearningData Science
4:51
ZTM Resources
4:23
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
4:45
Section Review
1:48
Monthly Coding Challenges Free Resources and Guides
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
4:51
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
4:00
Optional Elements of AI
The 2 Paths
3:27
Python Machine Learning Monthly
Endorsements On LinkedIN
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
16:18
Jupyter Notebook Walkthrough 3
8:10
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 Videos
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
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
Viewing Arrays and Matrices
9:35
Manipulating Arrays
11:32
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
Exercise Imposter Syndrome
2:56
Assignment NumPy Practice
Optional Extra NumPy resources
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
8:29
Plotting from Pandas DataFrames 6
8:28
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
Section Overview
2:29
Scikitlearn Introduction
6:41
Quick Note Upcoming Video
Refresher What Is Machine Learning
5:40
Quick Note Upcoming Videos
Scikitlearn Cheatsheet
6:13
Typical scikitlearn 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 NaNNone values
Getting Your Data Ready Handling Missing Values With Pandas
12:22
Extension Feature Scaling
Note Correction in the upcoming video splitting data
Getting Your Data Ready Handling Missing Values With Scikitlearn
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 predictproba
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 Scikitlearn 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
7:29
Saving And Loading A Model 2
6:20
Putting It All Together
20:19
Putting It All Together 2
11:34
ScikitLearn Practice
Milestone Projects
Section Overview
2:09
Project Overview
6:09
Project Environment Setup
10:59
Optional Windows Project Environment Setup
4:52
Step 14 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
13:37
Preparing Our Data For Machine Learning
8:51
Choosing The Right Models
10:15
Experimenting With Machine Learning Models
6:31
TuningImproving 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
Note Code change in upcoming video
Evaluating Our Model 2
5:55
Evaluating Our Model 3
8:49
Finding The Most Important Features
16:07
Reviewing The Project
9:13
Section Overview
1:07
Project Overview
4:24
Downloading the data for the next two projects
Project Environment Setup
10:52
Step 14 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 Whats 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 Introduction
3:24
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
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 20 Default Issue
3:39
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
6:38
Optional How machines learn and whats 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
15:20
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
15:02
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
Section Overview
2:19
Communicating Your Work
3:22
Communicating With Managers
2:58
Communicating With CoWorkers
3:42
Weekend Project Principle
6:32
Communicating With Outside World
3:29
Storytelling
3:06
Communicating and sharing your work Further reading
Endorsements On LinkedIn
Quick Note Upcoming Video
What If I Dont Have Enough Experience
15:03
Learning Guideline
Quick Note Upcoming Videos
JTS Learn to Learn
1:59
JTS Start With Why
2:43
Quick Note Upcoming Videos
CWD Git Github
17:40
CWD Git Github 2
16:52
Contributing To Open Source
14:08
Contributing To Open Source 2
9:40
Exercise Contribute To Open Source
Coding Challenges
What Is A Programming Language
6:24
Python Interpreter
7:04
How To Run Python Code
4:53
Latest Version Of Python
1:28
Our First Python Program
7:43
Python 2 vs Python 3
6:41
Exercise How Does Python Work
2:09
Learning Python
2:05
Python Data Types
4:46
How To Succeed
Numbers
11:09
Math Functions
4:29
DEVELOPER FUNDAMENTALS I
4:07
Operator Precedence
3:10
Exercise Operator Precedence
Optional bin and complex
4:02
Variables
13:12
Expressions vs Statements
1:36
Augmented Assignment Operator
2:49
Strings
5:29
String Concatenation
1:16
Type Conversion
3:03
Escape Sequences
4:23
Formatted Strings
8:24
String Indexes
8:57
Immutability
3:13
BuiltIn Functions Methods
10:03
Booleans
3:21
Exercise Type Conversion
8:22
DEVELOPER FUNDAMENTALS II
4:42
Exercise Password Checker
7:21
Lists
5:01
List Slicing
7:48
Matrix
4:11
List Methods
10:28
List Methods 2
4:24
List Methods 3
4:52
Common List Patterns
5:57
List Unpacking
2:41
None
1:51
Dictionaries
6:21
DEVELOPER FUNDAMENTALS III
2:40
Dictionary Keys
3:37
Dictionary Methods
4:37
Dictionary Methods 2
7:04
Tuples
4:46
Tuples 2
3:14
Sets
7:24
Sets 2
8:45
Breaking The Flow
2:35
Conditional Logic
13:18
Indentation In Python
4:38
Truthy vs Falsey
5:18
Ternary Operator
4:14
Short Circuiting
4:02
Logical Operators
6:56
Exercise Logical Operators
7:47
is vs
7:36
For Loops
7:01
Iterables
6:43
Exercise Tricky Counter
3:23
range
5:38
enumerate
4:37
While Loops
6:28
While Loops 2
5:49
break continue pass
4:15
Our First GUI
8:48
DEVELOPER FUNDAMENTALS IV
6:34
Exercise Find Duplicates
3:54
Functions
7:41
Parameters and Arguments
4:25
Default Parameters and Keyword Arguments
5:40
return
13:11
Exercise Tesla
Methods vs Functions
4:33
Docstrings
3:47
Clean Code
4:38
args and kwargs
7:56
Exercise Functions
4:18
Scope
3:38
Scope Rules
6:55
global Keyword
6:13
nonlocal Keyword
3:21
Why Do We Need Scope
3:38
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
Python Exam Testing Your Understanding
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 Resource Python Cheatsheet
Statistics and Mathematics
Become An Alumni
Thank You
2:44
Thank You Part 2
Special Bonus Lecture
$49.99$9.99Save 80%
What's included
Certificate of completion
Create an account to start learning