Machine Learning

Teacher

Overview

Age: 18+

Course Description

This course helps you to practically learn machine learning concepts from basics to advanced and develop the skills needed for an exciting career in Machine Learning.

Course Overview

This career building course involves you to build multiple projects and helps you develop a good understanding of Machine Learning concepts practically.

You will develop the following projects:

New House Price Prediction
Credit Card Fraud Detection
Movie Recommendation System
Hand Written Digits Recognition

Course Features

Lectures 94
Quizzes 0
Duration 100 hours
Skill level All levels
Language English
Students 1
Certificate No
Assessments Yes

Curriculum

Module 1: What is Machine Learning

8
- Lecture1.1
  
  Learn: What is Machine Learning
- Lecture1.2
  
  Learn: Supervised vs Unsupervised Learning
- Lecture1.3
  
  Learn: Machine Learning Terminology & Pipeline
- Lecture1.4
  
  Learn: Tools for Machine Learning
- Lecture1.5
  
  Do: Installation of Anaconda
- Lecture1.6
  
  Do: Setting up a Jupyter Notebook
- Lecture1.7
  
  Do: Explore Jupyter
- Lecture1.8
  
  Review: What is Machine Learning
Module 2: Your First Machine Learning Model - Boston Housing

7
- Lecture2.1
  
  Learn: Boston Housing – Problem Description
- Lecture2.2
  
  Learn: Types of Data & Its Implications
- Lecture2.3
  
  Learn: Preprocessing of Data
- Lecture2.4
  
  Do: Import BH Data
- Lecture2.5
  
  Do: Data Visualization of BH Data
- Lecture2.6
  
  Do: Correlation between Target & Attributes
- Lecture2.7
  
  Review: Your First Machine Learning Model!
Module 3: Mean Squared Error

5
- Lecture3.1
  
  Learn: Errors & Types
- Lecture3.2
  
  Learn: Mean Squared Error
- Lecture3.3
  
  Do: Define MSE Function
- Lecture3.4
  
  Do: Calculate MSE for Sample Data
- Lecture3.5
  
  Review: Mean Squared Error
Module 4: Data Splitting & Accuracy of Model - Boston Housing

3
- Lecture4.1
  
  Learn: Testing & Training Data – Cross Validation
- Lecture4.2
  
  Do: Split the dataset
- Lecture4.3
  
  Review: Data Splitting & Accuracy of Model
Module 5: Linear Regression - Boston Housing

11
- Lecture5.1
  
  Learn: Linear Regression & its Types
- Lecture5.2
  
  Learn: Gradient Descent – Learning Rates
- Lecture5.3
  
  Learn: Programming logic – Update Function
- Lecture5.4
  
  Do: Update Function
- Lecture5.5
  
  Learn: Programming logic – Descent Function
- Lecture5.6
  
  Do: Descent Function
- Lecture5.7
  
  Learn: Programming logic – Model Training Visualization
- Lecture5.8
  
  Do: Model Training Visualization
- Lecture5.9
  
  Learn: Programming logic – Error Visualization
- Lecture5.10
  
  Do: Error Visualization
- Lecture5.11
  
  Review: Linear Regression
Module 6: Machine Learning Prediction - Boston Housing

5
- Lecture6.1
  
  Learn: Prediction & Validation
- Lecture6.2
  
  Learn: Programming logic – Prediction of Prices
- Lecture6.3
  
  Do: Prediction of Prices
- Lecture6.4
  
  Do: Train Model with Different Attributes & Predict
- Lecture6.5
  
  Review: Machine Learning Prediction
Module 7: Credit Card Fraud Detection

4
- Lecture7.1
  
  Learn: Credit Card Fraud Detection – Problem Description
- Lecture7.2
  
  Learn: Data Preprocessing – Programming Logic
- Lecture7.3
  
  Do: Import & Visualize Data
- Lecture7.4
  
  Do: Preprocess Data
Module 8: SelectKBest Features

5
- Lecture8.1
  
  Learn: What is SelectkBest
- Lecture8.2
  
  Learn: SelectKBest – Programming Logic
- Lecture8.3
  
  Do: SelectKBest Features
- Lecture8.4
  
  Do: Discard Bad Features
- Lecture8.5
  
  Do: Visualize Features
Module 9: Train Model

4
- Lecture9.1
  
  Learn: What is Gaussian Naive Bayes Algorithm
- Lecture9.2
  
  Learn: What is Cross Validation
- Lecture9.3
  
  Learn: Gaussian Naive Bayes – Programming Logic
- Lecture9.4
  
  Do: Split Dataset
Module 10: Fraud Prediction

3
- Lecture10.1
  
  Learn: What is Confusion Matrix
- Lecture10.2
  
  Learn: Confusion Matrix – Programming Logic
- Lecture10.3
  
  Do: Evaluation & Visualization
Module 11: Movie Recommendation System

3
- Lecture11.1
  
  Learn: Movie Recommendation – Problem Description
- Lecture11.2
  
  Learn: MRS – Problem Setup
- Lecture11.3
  
  Do: Download & Import Data
Module 12: MRS - Data Preprocessing

7
- Lecture12.1
  
  Learn: MRS – Data Visualization – Programming Logic
- Lecture12.2
  
  Learn: MRS – Data Processing – Programming Logic
- Lecture12.3
  
  Do: MRS – Data Vizualization – Movies
- Lecture12.4
  
  Do: MRS – Data Processing – Movies
- Lecture12.5
  
  Do: MRS – Data Vizualization & Processing – Users
- Lecture12.6
  
  Do: MRS – Create pivot table and sparse matrix
- Lecture12.7
  
  Do: MRS – Map Data – Movies to Movie ID
Module 13: Train Model

3
- Lecture13.1
  
  Learn: K Nearest Neighbours
- Lecture13.2
  
  Learn: MRS – Train the Model
- Lecture13.3
  
  Review: K Nearest Neighbours
Module 14: MRS - Evaluate Model & Predict

3
- Lecture14.1
  
  Learn: MRS – Search Movie Title – Programming Logic
- Lecture14.2
  
  Do: MRS – Search Movie Title
- Lecture14.3
  
  Do: MRS – Recommend Movies
Module 15: Recognize Handwritten Digits

5
- Lecture15.1
  
  Learn: What is Deep Learning?
- Lecture15.2
  
  Learn: Recognize Handwritten Digits – Problem Description
- Lecture15.3
  
  Learn: MNIST Dataset – Description
- Lecture15.4
  
  Learn: Recognize Handwritten Digits – Problem Setup
- Lecture15.5
  
  Do: Import MNIST Dataset
Module 16: MNIST Dataset - Preprocessing the Data

4
- Lecture16.1
  
  Learn: Basic Properties of an Image
- Lecture16.2
  
  Do:MNIST Data – Properties of the Dataset
- Lecture16.3
  
  Do: MNIST Data – Visualize Samples of the Data
- Lecture16.4
  
  Do: MNIST Data – Scaling the Data
Module 17: Neural Networks in Deep Learning

8
- Lecture17.1
  
  Learn: What is a Neural Network
- Lecture17.2
  
  Learn: Representation of Neural Networks
- Lecture17.3
  
  Learn: Output of a Neural Network
- Lecture17.4
  
  Learn: Activation Functions in Neural Networks
- Lecture17.5
  
  Learn: TensorFlow & Keras – Basic Concepts
- Lecture17.6
  
  Learn: Recognize Handwritten Digits – Deep Learning Model – Programming Logic
- Lecture17.7
  
  Do: Recognize Handwritten Digits – Setup Neural Network
- Lecture17.8
  
  Do: Recognize Handwritten Digits – Train the Model
Module 18: Recognize Handwritten Digits - Evaluate the Model & Predict the Digits

6
- Lecture18.1
  
  Learn: Recognize Handwritten Digits – Model Evaluation – Programming Logic
- Lecture18.2
  
  Do: Recognize Handwritten Digits – Vizualize model training
- Lecture18.3
  
  Do: Recognize Handwritten Digits – Evaluate Accuracy & Make Predictions
- Lecture18.4
  
  Do: Recognize Handwritten Digits – Function to Visualize Results
- Lecture18.5
  
  Do: Recognize Handwritten Digits – Visualize Prediction of Random Samples
- Lecture18.6
  
  Do: Recognize Handwritten Digits – Visualize Prediction of Incorrect Samples

Embedded Systems

Students

1,199.00 د.إ

STEM Clouds

Blockchain and Bitcoin: Fundamentals

Students

799.00 د.إ

STEM Clouds

The Young Coder

This course will coming soon

Coming soon

STEM Clouds

Code your mBot

Students

99.00 د.إ

Language Learning for Kids in Augmented Reality

STEM Clouds

Language Learning for Kids in Augmented Reality

Students

199.00 د.إ

799.00 د.إ

Overview
Curriculum

Overview

Course Description

Course Overview

Course Features

Curriculum

Module 1: What is Machine Learning

Module 2: Your First Machine Learning Model - Boston Housing

Module 3: Mean Squared Error

Module 4: Data Splitting & Accuracy of Model - Boston Housing

Module 5: Linear Regression - Boston Housing

Module 6: Machine Learning Prediction - Boston Housing

Module 7: Credit Card Fraud Detection

Module 8: SelectKBest Features

Module 9: Train Model

Module 10: Fraud Prediction

Module 11: Movie Recommendation System

Module 12: MRS - Data Preprocessing

Module 13: Train Model

Module 14: MRS - Evaluate Model & Predict

Module 15: Recognize Handwritten Digits

Module 16: MNIST Dataset - Preprocessing the Data

Module 17: Neural Networks in Deep Learning

Module 18: Recognize Handwritten Digits - Evaluate the Model & Predict the Digits

You May Like

Embedded Systems

Blockchain and Bitcoin: Fundamentals

The Young Coder

Code your mBot

Language Learning for Kids in Augmented Reality

What We Offer

Quick Links

Follow Us

Register Today

Machine Learning

Overview

Course Description

Course Overview

Course Features

Curriculum

Module 1: What is Machine Learning

Module 2: Your First Machine Learning Model - Boston Housing

Module 3: Mean Squared Error

Module 4: Data Splitting & Accuracy of Model - Boston Housing

Module 5: Linear Regression - Boston Housing

Module 6: Machine Learning Prediction - Boston Housing

Module 7: Credit Card Fraud Detection

Module 8: SelectKBest Features

Module 9: Train Model

Module 10: Fraud Prediction

Module 11: Movie Recommendation System

Module 12: MRS - Data Preprocessing

Module 13: Train Model

Module 14: MRS - Evaluate Model & Predict

Module 15: Recognize Handwritten Digits

Module 16: MNIST Dataset - Preprocessing the Data

Module 17: Neural Networks in Deep Learning

Module 18: Recognize Handwritten Digits - Evaluate the Model & Predict the Digits

You May Like

Embedded Systems

Blockchain and Bitcoin: Fundamentals

The Young Coder

Code your mBot

Language Learning for Kids in Augmented Reality

What We Offer

Quick Links

Follow Us

Login with your site account

Register a new account

Register Today

Login with your site account

Modal title