AI Professional Program - Standard

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 Lecture Videos: Dive into the world of AI and data science with in-depth lecture videos delivered by an industry expert. These videos 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.

Instructor Office Hours and Exam Schedule

Description	Dates	Timing	Location
KHDA-Accredited AI Certification Exam	2 July 2023	Sunday: 3:00PM - 5:00PM	Dubai (GMT+4)

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

250 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 is the Founder and CEO of Innosoft Gulf, a leading AI and Blockchain Education Center in Dubai. With a focus on teaching, consulting, and research in the fields of Blockchain, Deep Learning, Machine Learning, and Big Data, Ahmed has trained hundreds of professionals, equipping them with the skills needed for success in these cutting-edge technologies.

With a background in Information and Decision Science from the University of Illinois at Chicago, Ahmed brings extensive industry experience, having worked with renowned organizations such as Sun Microsystems and the Chicago Board of Options Exchange (CBOE). He played a key role in the development of a Java EE-based workflow engine, which was featured in the best-selling book 'Java EE Patterns.'

Ahmed's passion for advancing AI and Big Data technologies extends beyond education. As the organizer of Innosoft Gulf – Big Data and Artificial Intelligence, one of Dubai's largest meet-up groups, he has fostered a thriving local community of professionals in these fields.

Currently pursuing graduate studies in AI at Stanford University, Ahmed remains actively involved in cutting-edge projects. His expertise in areas such as self-driving technology utilizing Fully Convolutional Neural Networks for Automated Traffic Lane Detection further enhances his ability to deliver impactful training and consultancy.

As a data scientist and founder of Innosoft Gulf, Ahmed brings a wealth of knowledge and real-world experience to this program, ensuring participants gain practical skills and insights to excel in the rapidly evolving AI and Big Data landscape.