Machine Learning & Data Science Online Course

ML & Data Science Complete Bootcamp (2022 Edition)

Machine Learning 101

• What Is Machine Learning?
• Al/Machine Learning/Data Science
• Exercise: Machine Learning Playground
• How Did We Get Here?
• Exercise: YouTube Recommendation Engine
• Types of Machine Learning
• Are You Getting It Yet?
• What Is Machine Learning? Round 2
• Section Review
• Monthly Coding Challenges, Free Resources and Guides

Machine Learning and Data Science Framework

• Section Overview
• Introducing Our Framework
• 6 Step Machine Learning Framework
• Types of Machine Learning Problems
• Types of Data
• Types of Evaluation
• Features In Data
• Modelling - Splitting Data
• Modelling - Picking the Model
• Modelling - Tuning
• Modelling - Comparison
• Overfitting and Underfitting Definitions
• Experimentation
• Tools We Will Use
• Optional: Elements of Al

Data Science Environment Setup

• Section Overview
• Introducing Our Tools
• What is Conda?
• Conda Environments
• Mac Environment Setup
• Mac Environment Setup 2
• Windows Environment Setup
• Windows Environment Setup 2
• Linux Environment Setup
• Sharing your Conda Environment
• Jupyter Notebook Walkthrough
• Jupyter Notebook Walkthrough 2
• Jupyter Notebook Walkthrough 3

Pandas: Data Analysis

• Section Overview
• Downloading Workbooks and Assignments
• Pandas Introduction
• Series, Data Frames and CSVs
• Data from URLs
• Describing Data with Pandas
• Selecting and Viewing Data with Pandas
• Selecting and Viewing Data with Pandas Part 2
• Manipulating Data
• Manipulating Data 2
• Manipulating Data 3
• Assignment: Pandas Practice
• How To Download The Course Assignments

NumPy

• Section Overview
• NumPy Introduction
• Quick Note: Correction In Next Video
• NumPy DataTypes and Attributes
• Creating NumPy Arrays
• NumPy Random Seed
• Viewing Arrays and Matrices
• Manipulating Arrays
• Manipulating Arrays 2
• Standard Deviation and Variance
• Reshape and Transpose
• Dot Product vs Element Wise
• Exercise: Nut Butter Store Sales
• Comparison Operators
• Sorting Arrays
• Turn Images Into NumPy Arrays
• Exercise: Imposter Syndrome
• Assignment: NumPy Practice
• Optional: Extra NumPy resources

Matplotlib: Plotting and Data Visualization

• Section Overview
• Matplotlib Introduction
• Importing And Using Matplotlib
• Anatomy Of A Matplotlib Figure
• Scatter Plot And Bar Plot
• Histograms And Subplots
• Subplots Option 2
• Quick Tip: Data Visualizations
• Plotting From Pandas DataFrames
• Quick Note: Regular Expressions
• Plotting From Pandas DataFrames 2
• Plotting from Pandas DataFrames 3
• Plotting from Pandas DataFrames 4
• Plotting from Pandas DataFrames 5
• Plotting from Pandas DataFrames 6
• Plotting from Pandas DataFrames 7
• Customizing Your Plots
• Customizing Your Plots 2
• Saving And Sharing Your Plots
• Assignment: Matplotlib Practice

Scikit-learn: Creating Machine Learning Models

• Section Overview
• Scikit-learn Introduction
• Quick Note: Upcoming Video
• Refresher: What Is Machine Learning?
• Quick Note: Upcoming Videos
• Scikit-learn Cheatsheet
• Typical scikit-learn Workflow
• Optional: Debugging Warnings In Jupyter
• Getting Your Data Ready: Splitting Your Data
• Quick Tip: Clean, Transform, Reduce
• Getting Your Data Ready: Convert Data To Numbers
• Note: Update to next video (OneHotEncoder can handle NaN/None values)
• Getting Your Data Ready: Handling Missing Values With Pandas
• Extension: Feature Scaling
• Note: Correction in the upcoming video (splitting data)
• Getting Your Data Ready: Handling Missing Values With Scikit-earn
• NEW: Choosing The Right Model For Your Data
• NEW: Choosing The Right Model For Your Data 2 (Regression)
• Quick Note: Decision Trees
• Quick Tip: How ML Algorithms Work
• Choosing The Right Model For Your Data 3 (Classification)
• Fitting A Model To The Data
• Making Predictions With Our Model predict vs predict _probal)
• NEW: Making Predictions With Our Model (Regression)
• NEW: Evaluating A Machine Learning Model (Score) Part 1
• NEW: Evaluating A Machine Learning Model (Score) Part 2
• Evaluating A Machine Learning Model 2 (Cross Validation)
• Evaluating A Classification Model 1 (Accuracy)
• Evaluating A Classification Model 2 (ROC Curve)
• Evaluating A Classification Model 3 (ROC Curve)
• Reading Extension: ROC Curve + AUC
• Evaluating A Classification Model 4 (Confusion Matrix)
• NEW: Evaluating A Classification Model 5 (Confusion Matrix)
• Evaluating A Classification Model 6 (Classification Report)
• NEW: Evaluating A Regression Model 1 (R2 Score)
• NEW: Evaluating A Regression Model 2 (MAE)
• NEW: Evaluating A Regression Model 3 (MSE)
• Machine Learning Model Evaluation
• NEW: Evaluating A Model With Cross Validation and Scoring Parameter
• NEW: Evaluating A Model With Scikit-learn Functions
• Improving A Machine Learning Model
• Tuning Hyperparameters
• Tuning Hyperparameters 2
• Tuning Hyperparameters 3
• Note: Metric Comparison Improvement
• Quick Tip: Correlation Analysis
• Saving And Loading A Model
• Saving And Loading A Model 2
• Putting It All Together
• Putting It All Together 2
• Scikit-Learn Practice

ML Classification

• Section Overview
• Proiect Overview
• Project Environment Setup
• Optional: Windows Project Environment Setup
• Step 1~4 Framework Setup
• Getting Our Tools Ready
• Exploring Our Data
• Finding Patterns
• Finding Patterns 2
• Finding Patterns 3
• Preparing Our Data For Machine Learning
• Choosing The Right Models
• Experimenting With Machine Learning Models
• Tuning/Improving Our Model
• Tuning Hyperparameters
• Tuning Hyperparameters 2
• Tuning Hyperparameters 3
• Quick Note: Confusion Matrix Labels
• Evaluating Our Model
• Evaluating Our Model 2
• Evaluating Our Model 3
• Finding The Most Important Features
• Reviewing The Project

ML Time Series Data

• Section Overview
• Project Overview
• Downloading the data for the next two projects
• Project Environment Setup
• Step 1~4 Framework Setup
• Exploring Our Data
• Exploring Our Data 2
• Feature Engineering
• Turning Data Into Numbers
• Filling Missing Numerical Values
• Filling Missing Categorical Values
• Fitting A Machine Learning Model
• Splitting Data
• Challenge: What's wrong with splitting data after filling it?
• Custom Evaluation Function
• Reducing Data
• Randomized Search CV
• Improving Hyperparameters
• Preprocessing Our Data
• Making Predictions
• Feature Importance

Data Engineering

• Data Engineering Introduction
• What Is Data?
• What Is A Data Engineer?
• What Is A Data Engineer 2?
• What Is A Data Engineer 3?
• What Is A Data Engineer 4?
• Types Of Databases
• Quick Note: Upcoming Video
• Optional: OLTP Databases
• Optional: Learn SQL
• Hadoop, HDFS and MapReduce
• Apache Spark and Apache Flink
• Kafka and Stream Processing

Neural Networks: Deep Learning, Transfer Learning and TensorFlow 2

• Section Overview
• Deep Learning and Unstructured Data
• Setting Up With Google
• Setting Up Google Colab
• Google Colab Workspace
• Uploading Project Data
• Setting Up Our Data
• Setting Up Our Data 2
• Importing TensorFlow 2
• Optional: TensorFlow 2.0 Default Issue
• Using A GPU
• Optional: GPU and Google Colab
• Optional: Reloading Colab Notebook
• Loading Our Data Labels
• Preparing The Images
• Turning Data Labels Into Numbers
• Creating Our Own Validation Set
• Preprocess Images
• Preprocess Images 2
• Turning Data Into Batches
• Turning Data Into Batches 2
• Visualizing Our Data
• Preparing Our Inputs and Outputs
• Optional: How machines learn and what's going on behind the
• scenes?
• Building A Deep Learning Model
• Building A Deep Learning Model 2
• Building A Deep Learning Model 3
• Building A Deep Learning Model 4
• Summarizing Our Model
• Evaluating Our Model
• Preventing Overfitting
• Training Your Deep Neural Network
• Evaluating Performance With TensorBoard
• Make And Transform Predictions
• Transform Predictions To Text
• Visualizing Model Predictions
• Visualizing And Evaluate Model Predictions 2
• Visualizing And Evaluate Model Predictions 3
• Saving And Loading A Trained Model
• Training Model On Full Dataset
• Making Predictions On Test Images
• Submitting Model to Kaggle
• Making Predictions On Our Images

Storytelling + Communication: How To Present Your Work

• Section Overview
• Communicating Your Work
• Communicating With Managers
• Communicating With Co-Workers
• Weekend Project Principle
• Communicating With Outside World
• Storytelling
• Communicating and sharing your work: Further reading

Learn Python

• What Is A Programming Language
• Python Interpreter
• How To Run Python Code
• Our First Python Program
• Latest Version Of Python
• Python 2 vs Python 3
• Exercise: How Does Python Work?
• Learning Python
• Python Data Types
• How To Succeed
• Numbers
• Math Functions
• DEVELOPER FUNDAMENTALS: I
• Operator Precedence
• Exercise: Operator Precedence
• Optional: bin() and complex
• Variables
• Expressions vs Statements
• Augmented Assignment Operator
• Strings
• String Concatenation
• Type Conversion
• Escape Sequences
• Formatted Strings
• String Indexes
• Immutability
• Built-In Functions + Methods
• Booleans
• Exercise: Type Conversion
• DEVELOPER FUNDAMENTALS: I
• Exercise: Password Checker
• Lists
• List Slicing
• Matrix
• List Methods
• List Methods 2
• List Methods 3
• Common List Patterns
• List Unpacking
• None
• Dictionaries
• DEVELOPER FUNDAMENTALS: III
• Dictionary Keys
• Dictionary Methods
• Dictionary Methods 2
• Tuples
• Tuples 2
• Sets
• Sets 2

Learn Python Part 2

• Breaking The Flow
• Conditional Logic
• Indentation In Python
• Truthy vs Falsey
• Ternary Operator
• Short Circuiting
• Logical Operators
• Exercise: Logical Operators
• is vs ==
• For Loops
• Iterables
• Exercise: Tricky Counter
• range()
• enumerate ()
• While Loops
• While Loops 2
• break, continue, pass
• Our First GUI
• DEVELOPER FUNDAMENTALS: IV
• Exercise: Find Duplicates
• Functions
• Parameters and Arguments
• Default Parameters and Keyword Arguments
• return
• Exercise: Tesla
• Methods vs Functions
• Docstrings
• Clean Code
• *args and **kwargs
• Exercise: Functions
• Scope
• Scope Rules
• global Keyword
• nonlocal Keyword
• Why Do We Need Scope?
• Pure Functions
• map()
• filter()
• zip()
• reduce/)
• List Comprehensions
• Set Comprehensions
• Exercise: Comprehensions
• Python Exam: Testing Your Understanding
• Modules in Python
• Quick Note: Upcoming Videos
• Optional: PyCharm
• Packages in Python
• Different Ways To Import
• Next Steps
• Bonus Resource: Python Cheatsheet