Course Content
Introduction
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Course Outline
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Your First Day
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Machine Learning 101
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What is Machine Learning
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AI/Machine Learning/Data Science
00:00
Exercise : Machine Learning Playground
00:00
How Did We Get Here
00:00
Exercise: YouTube Recommendation Engine
00:00
Types of Machine Learning
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What Is Machine Learning? Round 2
00:00
Section Review
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Machine Learning and Data Science Framework
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Section Overview
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Introducing Our Framework
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6 Step Machine Learning Framework
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Types of Machine Learning Problems
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Types of Data
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Types of Evaluation
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Features In Data
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Modelling – Splitting Data
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Modelling – Picking the Model
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Modelling – Tuning
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Modelling – Comparison
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Experimentation
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Tools We Will Use
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The 2 Paths
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The 2 Paths
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Data Science Environment Setup
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Section Overview
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Introducing Our Tools
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What is Conda?
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Conda Environments
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Mac Environment Setup
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Mac Environment Setup 2
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Windows Environment Setup
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Windows Environment Setup 2
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Jupyter Notebook Walkthrough
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Jupyter Notebook Walkthrough 2
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Jupyter Notebook Walkthrough 3
00:00
Pandas : Data Analysis
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Section Overview
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Pandas Introduction
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Series, Data Frames and CSVs
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Describing Data with Pandas
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Selecting and Viewing Data with Pandas
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Selecting and Viewing Data with Pandas Part 2
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Manipulating Data
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Manipulating Data 2
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Manipulating Data 3
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How To Download The Course Assignments
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Numpy
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Section Overview
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NumPy Introduction
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NumPy DataTypes and Attributes
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Creating NumPy Arrays
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NumPy Random Seed
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Viewing Arrays and Matrices
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Manipulating Arrays
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Manipulating Arrays 2
00:00
Standard Deviation and Variance
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Reshape and Transpose
00:00
Dot Product vs Element Wise
00:00
Exercise: Nut Butter Store Sales
00:00
Comparison Operators
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Sorting Arrays
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Turn Images Into NumPy Arrays
00:00
Matplotlib : Plotting And Data Visualization
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Section Overview
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Matplotlib Introduction
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Importing And Using Matplotlib
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Anatomy Of A Matplotlib Figure
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Scatter Plot And Bar Plot
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Histograms And Subplots
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Subplots Option 2
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Quick Tip: Data Visualizations
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Plotting From Pandas DataFrames
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Plotting From Pandas DataFrames 2
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Plotting from Pandas DataFrames 3
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Plotting from Pandas DataFrames 4
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Plotting from Pandas DataFrames 5
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Plotting from Pandas DataFrames 6
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Plotting from Pandas DataFrames 7
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Customizing Your Plots
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Customizing Your Plots 2
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Saving And Sharing Your Plots
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Scikit – Learn : Creating Machine Learning models
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Section Overview
00:00
Scikit-learn Introduction
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Refresher: What Is Machine Learning?
00:00
Scikit – Learn Cheatsheet
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Typical scikit-learn Workflow
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Optional: Debugging Warnings In Jupyter
00:00
Getting Your Data Ready: Splitting Your Data
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Quick Tip: Clean, Transform, Reduce
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Getting Your Data Ready: Convert Data To Numbers
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Getting Your Data Ready: Handling Missing Values With Pandas
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Getting Your Data Ready: Handling Missing Values With Scikit-learn
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NEW: Choosing The Right Model For Your Data
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NEW: Choosing The Right Model For Your Data 2 (Regression)
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Quick Tip: How ML Algorithms Work
01:25
Choosing The Right Model For Your Data 3 (Classification)
00:00
Fitting A Model To The Data
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Making Predictions With Our Model
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predict() vs predict_proba()
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NEW: Making Predictions With Our Model (Regression)
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NEW: Evaluating A Machine Learning Model (Score) Part 1
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NEW: Evaluating A Machine Learning Model (Score) Part 2
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Evaluating A Machine Learning Model 2 (Cross Validation)
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Evaluating A Classification Model 1 (Accuracy)
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Evaluating A Classification Model 2 (ROC Curve)
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Evaluating A Classification Model 3 (ROC Curve)
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Evaluating A Classification Model 4 (Confusion Matrix)
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NEW: Evaluating A Classification Model 5 (Confusion Matrix)
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Evaluating A Classification Model 6 (Classification Report)
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NEW: Evaluating A Regression Model 1 (R2 Score)
00:00
NEW: Evaluating A Regression Model 2 (MAE)
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NEW: Evaluating A Regression Model 3 (MSE)
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NEW: Evaluating A Model With Cross Validation and Scoring Parameter
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NEW: Evaluating A Model With Scikit-learn Functions
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Improving A Machine Learning Model
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Tuning Hyperparameters
00:00
Tuning Hyperparameters 2
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Tuning Hyperparameters 3
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Quick Tip: Correlation Analysis
00:00
Saving And Loading A Model
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Saving And Loading A Model 2
00:00
Putting It All Together
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Putting It All Together 2
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Milestone Project 1: Supervised Learning (Classifications)
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Section Overview
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Project Overview
00:00
Project Environment Setup
00:00
Step 1~4 Framework Setup
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Getting Our Tools Ready
00:00
Exploring Our Data
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Finding Patterns
00:00
Finding Patterns 2
00:00
Finding Patterns 3
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Preparing Our Data For Machine Learning
00:00
Choosing The Right Models
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Experimenting With Machine Learning Models
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Tuining/Improving our Model
00:00
Tuning Hyperparameters
00:00
Tuning Hyperparameters 2
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Tuning Hyperparameters 3
00:00
Evaluating Our Model
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Evaluating Our Model 2
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Evaluating Our Model 3
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Finding The Most Important Features
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Reviewing The Project
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Milestone Project 2 : Supervised Learning (Time Series Data)
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Section Overview
00:00
Project Overview
00:00
Project Environment Setup
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Step 1~4 Framework Setup
00:00
Exploring Our Data
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Exploring Our Data 2
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Feature Engineering
00:00
Turning Data Into Numbers
00:00
Filling Missing Numerical Values
00:00
Filling Missing Categorical Values
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Fitting A Machine Learning Model
00:00
Splitting Data
00:00
Custom Evaluation Function
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Reducing Data
00:00
RandomizedSearchCV
00:00
Improving Hyperparameters
00:00
Preproccessing Our Data
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Making Predictions
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Feature Importance
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Data Engineering
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Data Engineering Introduction
00:00
What Is Data?
00:00
What Is A Data Engineer?
00:00
What is Data Engineer 2?
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What Is A Data Engineer 3?
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What Is A Data Engineer 4?
00:00
Types Of Databases
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Optional: OLTP Databases
00:00
Hadoop, HDFS and MapReduce
00:00
Apache Spark and Apache Flink
00:00
Kafka and Stream Processing
00:00
Neural Networks : Deep Learning, Transfer Learning and TensorFlow 2
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Section Overview
00:00
Deep Learning and Unstructured Data
00:00
Setting Up Google Colab
00:00
Google Colab Workspace
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Uploading Project Data
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Setting Up Our Data
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Setting Up Our Data 2
00:00
Importing TensorFlow 2
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Optional: TensorFlow 2.0 Default Issue
00:00
Using A GPU
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Optional: GPU and Google Colab
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Optional: Reloading Colab Notebook
00:00
Loading Our Data Labels
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Preparing The Images
00:00
Turning Data Labels Into Numbers
00:00
Creating Our Own Validation Set
00:00
Preprocess Images
00:00
Preprocess Images 2
00:00
Turning Data Into Batches
00:00
Turning Data Into Batches 2
00:00
Visualizing Our Data
00:00
Preparing Our Inputs and Outputs
00:00
Building A Deep Learning Model
00:00
Building A Deep Learning Model 2
00:00
Building A Deep Learning Model 3
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Building A Deep Learning Model 4
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Summarizing Our Model
00:00
Evaluating Our Model
00:00
Preventing Overfitting
00:00
Training Your Deep Neural Network
00:00
Evaluating Performance With TensorBoard
00:00
Make And Transform Predictions
00:00
Transform Predictions To Text
00:00
Visualizing Model Predictions
00:00
Visualizing And Evaluate Model Predictions 2
00:00
Visualizing And Evaluate Model Predictions 3
00:00
Saving And Loading A Trained Model
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Training Model On Full Dataset
00:00
Making Predictions On Test Images
00:00
Submitting Model to Kaggle
00:00
Making Predictions On Your Images
00:00
Storytelling+Communication : How to Present Your Work
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Section Overview
00:00
Communicating Your Work
00:00
Communicating With Managers
00:00
Communicating With Co-Workers
00:00
Weekend Project Principle
00:00
Communicating With Outside World
00:00
Storytelling
00:00
Career Advice + Extra Bits
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What If I Don’t Have Enough Experience?
00:00
JTS: Learn to Learn
00:00
JTS: Start With Why
00:00
CWD: Git + Github
00:00
CWD: Git + Github 2
00:00
Contributing To Open Source
00:00
Contributing To Open Source 2
00:00
Learn Python
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What Is A Programming Language
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Python Interpreter
00:00
How To Run Python Code
00:00
Our First Python Program
00:00
Python 2 vs Python 3
00:00
Exercise: How Does Python Work?
00:00
Learning Python
00:00
Python Data Types
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Numbers
00:00
Math Functions
00:00
DEVELOPER FUNDAMENTALS: I
00:00
Operator Precedence
00:00
Optional: bin() and complex
00:00
Variables
00:00
Expressions vs Statements
00:00
Augmented Assignment Operator
00:00
Strings
00:00
String Concatenation
00:00
Type Conversion
00:00
Escape Sequences
00:00
Formatted Strings
00:00
String Indexes
00:00
Immutability
00:00
Built-In Functions + Methods
00:00
Booleans
00:00
Exercise: Type Conversion
00:00
DEVELOPER FUNDAMENTALS: II
00:00
Exercise: Password Checker
00:00
Lists
00:00
List Slicing
00:00
Matrix
00:00
List Methods
00:00
List Methods 2
00:00
List Methods 3
00:00
Common List Patterns
00:00
List Unpacking
00:00
None
00:00
Dictionaries
00:00
DEVELOPER FUNDAMENTALS: III
00:00
Dictionary Keys
00:00
Dictionary Methods
00:00
Dictionary Methods 2
00:00
Tuples
00:00
Tuples 2
00:00
Sets
00:00
Sets 2
00:00
Learn Python Part 2
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Breaking The Flow
00:00
Conditional Logic
00:00
Indentation In Python
00:00
Truthy vs Falsey
00:00
Ternary Operator
00:00
Short Circuiting
04:14
Logical Operators
00:00
Exercise: Logical Operators
00:00
is vs ==
00:00
For Loops
00:00
Iterables
00:00
Exercise: Tricky Counter
00:00
range()
00:00
enumerate()
00:00
While Loops
00:00
While Loops 2
00:00
break, continue, pass
04:16
Our First GUI
00:00
DEVELOPER FUNDAMENTALS: IV
00:00
Exercise: Find Duplicates
00:00
Functions
00:00
Parameters and Arguments
00:00
Default Parameters and Keyword Arguments
00:00
return
00:00
Methods vs Functions
04:33
Docstrings
00:00
Clean Code
00:00
*args and **kwargs
00:00
Exercise: Functions
00:00
Scope
00:00
Scope Rules
00:00
global Keyword
00:00
nonlocal Keyword
00:00
Why Do We Need Scope?
00:00
Pure Functions
00:00
map()
00:00
filter()
00:00
zip()
00:00
reduce()
00:00
List Comprehensions
00:00
Set Comprehensions
00:00
Exercise: Comprehensions
00:00
Modules in Python
00:00
Optional: PyCharm
00:00
Packages in Python
00:00
Different Ways To Import
00:00
Assignment: Complete A.I. & Machine Learning
00:00
Where to go From Here ?
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Thank You
00:00
AI and ML
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