Complete Machine Learning and Data Science: Zero to Mastery 

Course Outline

Join Our Online Classroom!

Exercise: Meet The Community

Your First Day

What Is Machine Learning?

AI/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

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

Experimentation

Tools We Will Use

Optional: Elements of AI

The 2 Paths

Python + Machine Learning Monthly

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

Jupyter Notebook Walkthrough

Jupyter Notebook Walkthrough 2

Jupyter Notebook Walkthrough 3

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

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

Assignment: NumPy Practice

Optional: Extra NumPy resources

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

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

Getting Your Data Ready: Handling Missing Values With Pandas

Getting Your Data Ready: Handling Missing Values With Scikit-learn

Choosing The Right Model For Your Data

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_proba()

Making Predictions With Our Model (Regression)

Evaluating A Machine Learning Model (Score)

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)

Evaluating A Classification Model 4 (Confusion Matrix)

Evaluating A Classification Model 5 (Confusion Matrix)

Evaluating A Classification Model 6 (Classification Report)

Evaluating A Regression Model 1 (R2 Score)

Evaluating A Regression Model 2 (MAE)

Evaluating A Regression Model 3 (MSE)

Machine Learning Model Evaluation

Evaluating A Model With Cross Validation and Scoring Parameter

Evaluating A Model With Scikit-learn Functions

Improving A Machine Learning Model

Tuning Hyperparameters

Tuning Hyperparameters 2

Tuning Hyperparameters 3

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

Milestone Projects!

Section Overview

Project Overview

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

Evaluating Our Model

Evaluating Our Model 2

Evaluating Our Model 3

Finding The Most Important Features

Reviewing The Project

Section Overview

Project Overview

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

Custom Evaluation Function

Reducing Data

RandomizedSearchCV

Improving Hyperparameters

Preproccessing Our Data

Making Predictions

Feature Importance

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

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

Finishing Dog Vision: Where to next?

Section Overview

Videos uploaded by FEB 14th

Endorsements On LinkedIn

Quick Note: Upcoming Video

What If I Don't Have Enough Experience?

Learning Guideline

Quick Note: Upcoming Videos

JTS: Learn to Learn

JTS: Start With Why

Quick Note: Upcoming Videos

CWD: Git + Github

CWD: Git + Github 2

Contributing To Open Source

Contributing To Open Source 2

Coding Challenges

Exercise: Contribute To Open Source

What Is A Programming Language

Python Interpreter

How To Run Python Code

Our First Python Program

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: II

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

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

Statistics and Mathematics

Become An Alumni

Thank You

Bonus: Special Thank You Gift