First Image Classifier - Pakistan Institute of Technical & Professional Education

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Course Content

Section 1. Introduction

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What is Machine Learning

Data Science Playground

First Image Classifier

Section 2. Data Science and Machine Learning

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Recommender System Using

Data Science vs Machine Learning vs Artificial Intelligence

Summarizing It All

Section 3. AI Project Life Cycle

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AI Project Framework

Step 1 Problem Definition

Step 3 Evaluation

Step 4 Features

Step 5 Modelling

Step 5 Data Validation

Step 6 Course Correction

Tools Needed for AI Project

Section 4. Python the Most Powerful Language

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What is Programming Language

Python Interpreter and First Code

Python 3 vs Python 2

Formula to Learn Coding

Data Types and Basic Arithmetic

Basic Arithmetic Part 2

Rule of Programming

Section 5. Python the Most Powerful Language Part 02

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Mathematical Operators and Order of Precedence

Variables and Their BIG No No

Statement vs Expression

Augmented Assignment Operator

String Data Type

String Concatenation

Type Conversion

String Formatting

Section 6. Python the Most Powerful Language Part 03

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Built-in Function and Methods

Boolean Data Type

Exercise (Skipped – No Video Link)

Data Structure and Lists

Lists Continued

Matrix from Lists

Section 7. Python the Most Powerful Language Part 04

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Creating Lists Programmatically

AND OR Keywords

If Else Statement

Intro to Process of Coding Conditionals

Sets Data Types

Tuple Data Types

Most Used Methods on Dictionaries

Dictionary Key is Unchangeable

Boolean Result of Different Values

Section 8. Python the Most Powerful Language Part 05

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Logical Operators

Identity Operator

For Loop and Iterables

Nested For Loop

Exercise For Loop (Skipped – No Video Link)

Continue Break Pass Keywords

Exercise Draw a Shape (Skipped – No Video Link)

Section 9. Python Part-2

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Scope of a Function

Exercise (Skipped – No Video Link)

Args and Kwargs

Good Programming Practices

Default Parameters

Parameter vs Argument

Why of Functions

Section 10. Python Part-3

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Sets and Dictionary Comprehension

List Comprehension Case 1, 2, and 3

Special Functions Reduce

Special Functions Zip

Special Functions Filter

Special Functions Map

Programming Best Practices 2

Global vs Nonlocal Keywords

Python Packages

Section 11. Environment Setup for Machine Learning Projects

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Who is Mr. Conda

Loading and Visualizing Data

Walkthrough of Jupyter Notebook 2

Walkthrough of Jupyter Notebook 1

Installing for MacOS and Linux

Starting Jupyter Notebook

Installing Tools

Installing Conda

Blueprint of Machine Learning Project

Setting Up Machine Learning Project

Tools for Data Science Environment

Section 12. Pandas for Data Analysis

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Add Remove Data

Data Selection 2

Describing Data

How to Import Data

Pandas and What We Will Cover

Manipulating Data

Section 13. NumPy

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What and Why of NumPy

Important Functions on Arrays

Creating NumPy Array

Accessing Elements

Array Manipulation

Section 14. NumPy Part 02

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Mean, Variance and STD

Dot Product vs Matrix Manipulation

Reshape and Transpose

Exercise (Skipped – No Video Link)

Comparison Operators

Section 15. Matplotlib

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Matplotlib Intro

Pandas Data Frame

Four Plots One Figure

Most Used Plots (Bar Plot)

One Figure Many Plots

Setting Up Features

Methods to Plot

First Plot with Matplotlib

Plotting from Pandas Data Frame

Section 16. Matplotlib Part 02

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Plotting from Pandas Data Frame

Bar Plot from Pandas Data Frame

Pyplot vs OO Methods

Life Cycle of OO Method

Life Cycle of OO Method Advanced

Customization Part 2

Customization Part 3

Naming Entire Figure

Section 17. Scikit-Learn

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What Actually ML Model Is

Intro to Sklearn

Step 1 Getting Data Ready (Split Data)

Step 2 Choosing ML Model

Step 3 Fit Model

Step 4 Evaluate Model

Step 5 Improve Model

Step 6 Save Model

Section 18. Scikit-Learn Part 02

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What We Are Going to Do

Using Map to Choose Model

Choosing Machine Learning Model

Getting Data (Missing Values Method 2)

Getting Data (Missing Values)

Getting Data (Second Method of Conversion)

Getting Data (Anatomy of Conversion)

Getting Data Ready Converting Part 1

Step 1 Getting Data Split Data

Step 2 How to Choose Better Model

Section 19. Scikit-Learn Part 03

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Choosing Model for Classification Problem

Step 4 Area Under the Curve Part 1

Step 4 Accuracy (Classification Model)

Step 4 What is Cross Validation

Step 4 Evaluating Machine Learning Model (Default Scoring)

Step 3 Running Prediction on Regression Problem

Step 3 Predict Proba Method

Running Prediction

Step 4 Area Under the Curve Part 2

Section 20. Scikit-Learn Part 04

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Step 4 Area Under the Curve Part 3 (Plotting)

Step 4 Evaluation Using Functions (Classification)

Step 4 Scoring Parameters for Regression

Step 4 Scoring Parameters for Classification

Step 4 Mean Square Error for Regression Problems

Step 4 Mean Absolute Error for Regression Problems

Step 4 R2 for Regression Problems

Step 4 Classification Report Fully Explained

Step 4 Classification Report (Important Concepts)

Step 4 Confusion Matrix Plot

Confusion Matrix Calculate

Step 4 Evaluation Using Functions (Regression)

Section 21. Scikit-Learn Part 05

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Step 5 Improving Model by Hyperparameters

Step 5 Improving Model by Hyperparameters Manually

Step 5 Hyperparameters Task 1

Step 5 Evaluation Metrics in One Function

Step 5 Hyperparameters Comparison

Tuning Hyperparameters Using RSCV

Tuning Hyperparameters Using RSCV Part 2

Tuning Hyperparameters Using GSCV

Results Comparison

Section 22. Scikit-Learn Part 06

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Save Load Model with Pickle Method 1

Save Load Model with Joblib Method 2

Building Entire Model Using Pipeline Part 1

Building Entire Model Using Pipeline Part 2

Building Entire Model Using Pipeline Part 3

Building Entire Model Using Pipeline Part 4

Section 23. Project 1 Part 01

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Milestone Project 1 Intro

Chest Pain Type and Target Relation Part 2

Chest Pain Type and Target Relation Part 1

Age Distribution

Scatter Plot to See Any Pattern

Be Careful with Plot Choice

Exploratory Data Analysis Part 2

Exploratory Data Analysis Part 1

Importing Tools and Libraries

Data Features Recognition

Creating Project Environment

Correlation Matrix Part 1

Section 24. Project 1 Part 02

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Plotting Correlation Matrix Part 2

Modelling Split the Data

Choosing the Right Model

Improving Model

Plotting the Improved Model Score

Hyperparameter Tuning Using GSCV

Hyperparameters for RandomForestClassifier

Running the Model with Hyperparameters Using GSCV

Score Comparison After Tuning

Section 25. Project 1 Part 03

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Hyperparameters Tuning Using Grid Search CV

What Have We Learnt

Area Under the Curve and Confusion Matrix

Plot the Classification Report

Let’s See If Cross Validation Layers Help Us

Visualizing Cross Validation Score

Features Improvement

Eaxm

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AI ( Artificial intelligence )

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