Artificial Intelligence & Machine Learning Essential - AMLE

Course Prerequisite(s)

Please note that this course has the following prerequisites which must be completed before it can be accessed
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About Course

A thorough foundation in artificial intelligence and machine
learning, including important ideas and techniques, is
provided by Al and ML Essentials (AMLE). This entails
comprehending neural networks, supervised and
unsupervised learning, and real-world uses like computer
vision and natural language processing. Being proficient in
AMLE is essential for creating intelligent systems and using
datadriven insights in a variety of fields.

Course Content

1. Introduction to Al and ML

Understanding AI and ML
00:00
Definition and Scope of Artificial Intelligence (AI) and Machine Learning (ML
00:00
Historical developments and real-world application
00:00
Distinction between AI and ML
00:00
Basic Concepts in Machine Learning
00:00
Types of Machine Learning (Supervised, Unsupervised, Reinforcement Learning)
00:00
Feature engineering and data preprocessing
00:00
Model training, evaluation, and prediction
00:00
Introduction to Neural Networks
00:00
Basics of neural networks
00:00
Layers, neurons, and activation functions
00:00
Training neural networks with backpropagation
00:00

2. Data Preparation and Exploration in ML

3. Supervised Learning

4. Unsupervised Learning and Clustering

5. Introduction to Deep Learning and Practical Applications

6. Final Project

7. Final Exam

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Course Prerequisite(s)

About Course

Course Content

1. Introduction to Al and ML

Understanding AI and ML

Definition and Scope of Artificial Intelligence (AI) and Machine Learning (ML

Historical developments and real-world application

Distinction between AI and ML

Basic Concepts in Machine Learning

Types of Machine Learning (Supervised, Unsupervised, Reinforcement Learning)

Feature engineering and data preprocessing

Model training, evaluation, and prediction

Introduction to Neural Networks

Basics of neural networks

Layers, neurons, and activation functions

Training neural networks with backpropagation

2. Data Preparation and Exploration in ML

Data Collection and Cleaning

Gathering and cleaning data for ML projects

Handling missing values and outlier

Data quality and validation

Exploratory Data Analysis (EDA)

Visualizing data distributions and patterns

Correlation analysis

Feature selection and dimensionality reduction

Project: Data Preparation and EDA

Applying Data Preparation and EDA techniques to a real-world dataset

3. Supervised Learning

Regression

Understanding Regression Analysis

Linear and non-linear regression models

Evaluation metrics for regression

Classification

Basics of classification problems

Popular Classification Algorithms (e.g., Decision Trees, SVM)

Model evaluation and metrics for classification

Project: Building a Supervised Learning Mode

Implementing a supervised learning model on a provided dataset

4. Unsupervised Learning and Clustering

Clustering

Introduction to Unsupervised Learning

Types of Clustering Algorithms (e.g., K-Means, Hierarchical)

Applications of Clustering

Dimensionality Reduction

Principal Component Analysis (PCA)

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Use cases for dimensionality reduction

Project: Unsupervised Learning Project

Applying clustering and dimensionality reduction to a real-world dataset

5. Introduction to Deep Learning and Practical Applications

Introduction to Deep Learning

Deep neural networks and architectures

Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN)

Transfer learning

Practical Applications of AI and ML

Use cases and applications in various industries

Ethical considerations and responsible AI

Future Trends in AI and ML

Capstone Project: Real-world AI and ML Application

Developing a comprehensive AI or ML project, integrating various concepts learned throughout the course

6. Final Project

7. Final Exam

Student Ratings & Reviews

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