$ 29.90$ 89.9067% Off

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$ 29.90$ 89.9067% Off

Enrollment will begin soon

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Complete A.I. Machine Learning and Data Science 121-127

Name: Complete A.I. Machine Learning and Data Science 121-127
Price: 29.9 USD
Availability: InStock
Rating: 4.3 (85 reviews)

This online self-paced course provides a comprehensive introduction to Artificial Intelligence, Machine Learning, and Data Science concepts with practical applications. Learners will develop analytical, programming, and AI model-building skills required for modern data-driven industries.

982 Enrolled

English

4.3 (85)

Offered By: iqrasity

20 Modules

Gain insight into a topic and learn the fundamentals.

Beginner

No prior experience required

Self-paced

Flexible deadline. Learn at your own pace.

Enrollment will begin soon

Course Content

20 sections•218 resources• total length

Module 1. Introduction

0 resources

Module 2. Machine Learning 101 (8 Videos)

8 resources

What Is Machine Learning

AIMachine LearningData Science

Exercise Machine Learning Playground

How Did We Get Here

Exercise YouTube Recommendation Engine

Types of Machine Learning

What Is Machine Learning Round 2

Section Review

Module 3. Machine Learning and Data Science Framework (13 Videos)

13 resources

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

Module 4. The 2 Paths

1 resources

The 2 Paths

Module 5. Data Science Environment Setup (10 Videos)

10 resources

Section Overview

Introducing Our Tools

What is Conda

Conda Environments

Mac Environment Setup

Mac Environment Setup 2

Windows Environment Setup

Windows Environment Setup 2

Jupyter Notebook Walkthrough

Jupyter Notebook Walkthrough 3

Module 6. Pandas Data Analysis (10 Videos)

10 resources

Section Overview

Pandas Introduction

Series Data Frames and CSVs

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

How To Download The Course Assignments

Module 7. NumPy (13 videos)

13 resources

Section Overview

NumPy Introduction

NumPy DataTypes and Attributes

Creating NumPy Arrays

NumPy Random Seed6. NumPy Random Seed

Viewing Arrays and Matrices

Manipulating Arrays

Manipulating Arrays 2

Standard Deviation and Variance

Exercise Nut Butter Store Sales

Comparison Operators

Sorting Arrays

Turn Images Into NumPy Arrays

Module 8. Matplotlib Plotting and Data Visualization (18 videos)

18 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

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 2

Customizing Your Plots

Saving And Sharing Your Plots

Module 9. Scikit-learn Creating Machine Learning Models (42 Videos)

42 resources

Section Overview

Scikit-learn Introduction

Refresher What Is Machine Learning

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

NEW Choosing The Right Model For Your Data

NEW Choosing The Right Model For Your Data 2 (Regression)

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()

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)

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)

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

Quick Tip Correlation Analysis

Saving And Loading A Model

Saving And Loading A Model 2

Putting It All Together

Putting It All Together 2

Module 10. Supervised Learning Classification Regression

0 resources

Module 11. Milestone Project 1 Supervised Learning (Classification) (22 Videos)

22 resources

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

TuningImproving 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

Exercise Imposter Syndrome

Module 12. Milestone Project 2 Supervised Learning (Time Series Data) (18 Videos)

18 resources

Section Overview

Project Overview

Project Environment Setup

Step 1~4 Framework Setup

Exploring Our Data

Exploring Our Data 2

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

Module 13. Data Engineering (11 Videos)

11 resources

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

Optional OLTP Databases

Hadoop, HDFS and MapReduce

Apache Spark and Apache Flink

Kafka and Stream Processing

Module 14. Neural Networks Deep Learning Transfer Learning and TensorFlow 2 (42 Videos)

42 resources

Section Overview

Deep Learning and Unstructured Data

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 Reloading Colab Notebook

Loading Our Data Labels

Optional GPU and Google Colab

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

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

Module 15. Storytelling Communication How To Present Your Projects (7 Videos)

7 resources

Section Overview

Communicating Your Work

Communicating With Managers

Communicating With Co-Workers

Weekend Project Principle

Communicating With Outside World

Storytelling

Module 16. Career Advice Extra Bits (3 Videos)

3 resources

What If I Don't Have Enough Experience

JTS Learn to Learn

JTS Start With Why

Module 17. Learn Python

0 resources

Module 18. Learn Python Part 2

0 resources

Module 19. Bonus Learn Advanced Statistics and Mathematics

0 resources

Module 20. Way Forward

0 resources

Complete A.I. Machine Learning and Data Science 121-127

20 Modules

Beginner

Self-paced

Introduction & Applications of Artificial Intelligence (AI)

Applications of AI in Healthcare Profession

Fundamentals of Generative Arificial Intelligence (AI)

Generative For Cybersecurity Blue Team

AI Coding with GitHub Copilot

Building AI Agents with AWS

Cybersecurity Champion Be A Change Leader With AI

Defending Against AI Driven Phishing And Social Engineering

Generative AI For Penetration Testing Red Team

Engineering AI RAG for LLM

Introduction To ChatGPT A Practical Guide

Class 7

Class 8

Nimaaz

Class 6: Shapes

Class 6: Algebra

Class 6: Science

Class 6: Moon

Complete A.I. Machine Learning and Data Science 121-127

AI Mastery LLMs Explained With Math(Transformers, Attention Mechanisms & More)

About this course

What you'll learn

Course Content

Skills you'll gain

Pre-requisite

Details to know

Shareable certificate

Assessments

Taught in English

Why iqrasity

Digital Certificate

Career Paths You Can Pursue

Instructor

iqrasity