AI Professional Program - April 2024

InnosoftGulf

Dubai Training - Artificial Intelligence Professional Program - Basic Package

Description

This program offers a comprehensive learning experience in Python programming, data analysis, data visualization, and machine learning. Designed specifically for individuals new to the field of AI and data science, participants will gain a solid foundation in Python fundamentals, including data manipulation and control flow. The program covers essential topics such as data structures, file I/O, and object-oriented programming, providing a well-rounded understanding of these key concepts. Participants will also learn data analysis techniques using popular libraries like Numpy and Pandas. The program further introduces supervised and unsupervised learning, guiding participants through building predictive models and exploring classification algorithms. Deep learning concepts, including neural networks and image classification, are also covered. Through hands-on exercises and real-world applications, participants will develop the necessary skills to leverage AI and Big Data effectively. This program is designed to empower beginners to embark on a successful journey into the exciting fields of AI and data science.

When you enroll in this program, you will get:

Twenty Hours of Live Lectures: Dive into the world of AI and data science with in-depth live lecturse delivered by an industry expert. These lectures provide a thorough understanding of Python programming, data analysis, data visualization, and machine learning concepts.
Practical Assignments and Quizzes: Apply your knowledge and reinforce your learning through practical assignments and quizzes. These hands-on exercises and assessments ensure that you can actively engage with the material and measure your progress.
KHDA-Accredited AI Certification Exam Included:This performance-based assessment goes beyond multiple-choice questions, allowing candidates to showcase their skills by performing tasks on a live system and datasets. During the exam, participants will be provided with a real-world dataset and tasked with demonstrating their proficiency in data analysis, visualization, and machine learning. Successfully passing this exam will earn you the prestigious KHDA-Accredited AI certification, validating your expertise in AI and data science.
Weekly Office Hours: To support your learning journey, we offer weekly office hours every Friday from 6:00 PM to 7:00 PM. These sessions, led by the course instructor, provide a dedicated space for personalized guidance, detailed feedback, and addressing any specific questions or challenges you may encounter during the course. This is an excellent opportunity to deepen your understanding and enhance your overall learning experience.
Remote Access to Dedicated Lab Environment: Gain remote access to our dedicated lab environment, allowing you to conveniently work on projects and practical exercises from anywhere. This provides a flexible learning experience where you can apply your newly acquired skills in a real-world setting, enhancing your hands-on experience in AI and data science.

AI Professional Program Schedule and Details

Description	Dates	Timing	Location
AI Professional Program Sessions	3, 4, 10, 11, 17, 18, 24, 25 February 2024	Saturdays & Sundays: 6:00 PM - 8:30 PM	Dubai Knowledge Park, Block 6, Office 102
Weekly Office Hours	Every Friday in February 2024	6:00 PM - 7:00 PM	In-Person or Online
Certification Exam	Sunday, 2 March 2024	3:00 PM - 5:00 PM	In-Person or Online

Audience

Professionals or students who are interested in Python for Data Science and Machine Learning
Future Data Science Professionals and Engineers
Professionals interested in developing their skills in data analysis, data visualization, and machine learning

Prerequisites

Basic understanding of programming concepts
Familiarity with Python programming language (prior experience is beneficial but not mandatory)
Basic knowledge of mathematics and statistics
Interest in data analysis, data visualization, and machine learning

Registration Fees

1225 USD

How to Enroll?

Click on the "Enroll in AI-200" button above.
Create an account if you don't have one. You will receive a message from edu@innosoft.ai requesting to activate your email. (In case you don’t receive this notification, please check your spam folder.)
Sign in with your Userid and Password.
Enter your payment details.
Once the payment is made, you will be enrolled in the program.
You will gain access to all the lecture videos and course material.

Course Syllabus

Establishing a Python Environment: Walkthrough of setting up a conducive Python development environment.
Creating a Virtual Environment: Process and benefits of segregating projects with separate Python virtual environments.
Setting up Your Github Repository: Fundamentals of using Github for version control and sharing code.
Overview of Jupyter Notebook IDE: Introduction to Jupyter, a powerful, flexible open-source IDE ideal for Python scripting.
Data Types: Exploration of Python's built-in data types and their manipulation.
String Manipulation: Techniques for processing and handling Python strings.
Selection Statements: Understanding conditional logic with If, Else, and Elif statements.
For and While Loops: Grasp repetitive operations with Python's For and While loop constructs.

Storing Data in Lists: Working with Python's versatile List data structure.
Working with read-only Data using Tuples: Understanding immutable sequence type - Tuples.
Creating maps with Dictionaries: Manipulating Python's built-in hashmap data structure - Dictionary.
Removing Duplicates using Sets: Understanding and applying Python's set data structure.
File Input and Output: Basics of reading from and writing to files in Python.

Code reuse with Methods: Achieving code reusability with Python methods.
Encapsulation using Classes and Objects: Understanding encapsulation concept, creating and manipulating classes and objects.
Inheritance and Composition: Applying inheritance and composition to model real-world concepts.
Creating your own reusable modules and libraries: Building and managing personal Python modules and libraries.

Advantages of using Numpy Arrays: Insights into Numpy's powerful N-dimensional array object.
Numpy Arrays Indexing: Techniques to access and manipulate elements in Numpy arrays.
Numpy Operations: Performing mathematical operations on Numpy arrays.
Pandas Dataframes: Working with Pandas dataframes, a two-dimensional labeled data structure.
Data Preprocessing: Handling missing values and categorical features in datasets.
Categorizing data with Groupby: Understanding and applying the Groupby operation in Pandas.
Merging and Joining Dataframes: Combining different Pandas dataframes using merge and join operations.
Loading Data into Dataframes: Importing data from various sources into Pandas dataframes.

Data Visualization with Matplotlib: Basics of creating static, animated, and interactive visualizations in Python using Matplotlib.
Data Visualization with Seaborn: Understanding Seaborn, a Python data visualization library based on Matplotlib.
Scatter, Join, Distribution and Regression Plots: Creating various types of plots to analyze data.
Interactive Data Visualization with Plotly: Introduction to Plotly, a Python graphing library that makes interactive, publication-quality graphs.
Geographical Data Visualization with Plotly: Exploring geographic data with Plotly.

Introduction to Machine Learning: Understanding the fundamentals of machine learning.
Supervised, Unsupervised and Reinforcement Learning: Distinguishing between different types of machine learning paradigms.
Supervised Learning (Classification, Regression): Basics of predictive models in machine learning: Classification and Regression.
Linear Regression: Building a predictive model using Linear Regression.
Building a Predictive Model for a Real Estate Firm: Applying machine learning to solve real-world business problems.

Building a Classification Model with Logistic Regression: Creating a binary classification model using Logistic Regression.
Evaluating Classification Models (Accuracy, Precision, Recall, F1-Score): Assessing the performance of classification models using common metrics.
Bias-Variance Tradeoff: Understanding the critical balance in machine learning models to improve generalization.
Classification – K-Nearest Neighbors (KNN): Introduction to instance-based learning algorithm K-Nearest Neighbors for classification problems.
Tuning a KNN Model: Exploring strategies to optimize the performance of a KNN model.
Unsupervised Learning – K-Means Clustering: Fundamentals of K-Means, a popular centroid-based clustering algorithm.

Neural Network Representation: Understanding the structure and components of neural networks.
Forward Propagation: Grasp the feedforward process in a neural network, where information flows from the input layer to the output.
Activation Functions: Exploration of various activation functions in neural networks and their role.
Cost Functions: Concept of cost functions in optimization of neural networks.
Back-Propagation with Gradient Descent: Understanding the learning mechanism in neural networks through backpropagation and gradient descent.
Image Classification with Deep Learning: Application of deep learning techniques for the task of image classification.

Instructor

Ahmed El Koutbia

Ahmed El Koutbia is a seasoned Data Scientist and Algorithmic Trading Developer. His expertise encompasses developing advanced trading and risk management systems for financial markets, as well as AI-driven projects such as self-driving car technology. Ahmed studied AI at Stanford University, contributing significantly to a project on traffic lane detection using computer vision. His professional journey also includes teaching advanced courses in AI, blockchain, and data science, positioning him as a well-rounded expert in both theoretical and practical aspects of technology and AI.