Artificial Intelligence Course

Target Audience:

This program is targeting :

Industrial, Commercial, Institutional
Communication Engineers ,Software & Computer Engineers and computer science graduates

Language: English/Arabic

Course Description:

The course is designed to be a comprehensive training for Artificial Intelligence (AI). It consists of three parts; the first is concerned with Python Programming language which is considered the most common language used in AI. The second part covers the Machine Learning (ML) task of AI which applies the knowledge gained in the first part to solve ML problems such as regression, classification, and clustering. The third part covers Deep Learning (DL) which introduces the theory and application of Neural Networks in AI.

Course Learning Outcomes:

On successful completion of the course, the trainee will be able to:

Explain what constitutes “Artificial” Intelligence and how to identify systems with Artificial
Explain how Artificial Intelligence enables capabilities that are beyond conventional
Write programs using Python, and incorporate python libraries (Matplotlib, NumPy, SciPy,…) to carry out specific tasks,
Use python and its libraries to solve Machine Learning problems such as regression, classification, and clustering.
Explain what is Deep Learning and the analogy between Human Neural Network and Artificial Neural Networks (ANN).
Application of different types of ANN such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in Image Processing, Natural Language Processing, and other

Artificial Intelligence Content:

1 – Python Programming

	Topic
1	Introduction to Python
2	· Environment Setup · Python syntax · Data Types · Control Statements (if, for, while) · Functions · Error Handling
3	File Handling
4	NumPy Library · NumPy is a Python library used for working with arrays.
5	Matplotlib Library · Matplotlib is a low level graph plotting library in python that serves as a visualization utility.
6	SciPy Library · SciPy is a scientific computation library that uses NumPy underneath.

2 – Machine Learning

	Topic
1	Introduction to AI
2	Introduction to Machine Learning
3	Math Essential for Machine Learning · Linear Algebra · Vectors · Statistics · Probability
4	Data Preprocessing · Importing libraries · Data acquisition · Data cleaning · Handling missing data · Categorical data · Data splitting · Feature scaling · Feature Engineering
5	Regression problem · Linear Regression · Multi-linear regression · Polynomial regression · Regression Evaluation Metrics · K-nearest neighbor regression · Decision Tree regression

6

Classification problem

· Logistic Regression

· Naive Bayes

· Support vector machine (SVM)

· K-nearest neighbor classifier

· Decision tree classifier

· Ensemble learning

· Classification Evaluation Metrics

· Bagging and Boosting

7

Clustering Problems

· Dimensionality reduction

· K-means

· DBSCAN

· Hierarchical clustering

· Association Rules

8

Reinforcement learning

· Q-Learning

9

Model Selection and Evaluation

· Loss functions

· Gradient descent

· Bias-variance tradeoff

· Cross-validation

· Hyper-parameter tuning

3 – Deep Learning

	Topic
1	Python OOP and Classes
2	Python Advanced Topics
3	Introduction to Neural Networks
4	Deep Learning With Pytorch
5	Convolutional Neural Networks (CNN)
6	Dynamical Systems: Recurrent Neural Networks (RNN).
7	Recurrent Neural Networks
8	Long Short-Term Memory Network (LSTM)
9	Generative Adversarial Networks
10	Model Deployment
11	Updating a Model

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Jelecom Egypt

Jelecom is an Egyptian Company which has professional Engineers for Engineering Projects and it provides Technical Training and Solutions for both Engineers and Technicians in Industry & Academy