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Artificial Intelligence & Data Science Diploma

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Artificial Intelligence & Data Science Diploma

دبلومة شاملة بتجمع بين AI وعلوم البيانات، بتركّز على الأساسيات النظرية والتطبيق العملي باستخدام أحدث الأدوات والتقنيات.

Module 1-Python for AI (1-Python for AI)

  • Download required tools
  • Data Types
  • Configuring Anaconda
  • Strings
  • Python basic Syntax
  • Containers
  • Conditions
  • Loops
  • Strings
  • Functions
  • Files Databases
  • Reusability

Module 1-Python for AI (2-Object-oriented programming (OOP)

  • Introduction to OOP and how to use them in AI
  • Handling Projects in Business
  • Complexity Analysis

Module 1-Python for AI (3-Python (Data Structure))

✨Project: Python problems - Build OOP Projects

  • Importance of Data Structures
  • Data Structures in AI
  • Data Structures usecases
  • Graphs

Module 2-Mathematics for AI (1-Introduction)

  • Introduction to Data Science
  • Data Pipeline
  • History of Data
  • Importance of data in business
  • Introduction to Data Science, Data Analysis and Data Engineering
  • DataOps Pipeline
  • History of Data
  • Importance of data in business
  • Data Tools

Module 2-Mathematics for AI (2-Statistics)

  • Data pipeline
  • Importance of statistics in Al
  • Important statistics rules in Al (mean, median, mode, variance, sti... etc.)
  • Sampling techniques
  • Statistical test
  • Hypothesis testing

Module 2-Mathematics for AI (3-Linear Algebra)

  • How to use linear algebra in Data Analysis
  • Basic Algebra rules
  • Matrix multiplication

Module 2-Mathematics for AI (4-Probability)

  • Why do we use probability?
  • Conditional probability
  • Unconditional Probability
  • Probability distribution

Module 2-Mathematics for AI (5-Calculus)

✨Project: Analyzing Real Data with Statistics for AI.

  • Calculus in Al
  • Differentiation
  • Weights optimization
  • Partial Clerative
  • Chain Rule

Module 3-Data Analysis (1-Introduction to Data Analysis)

  • History of Data
  • Importance of data in business
  • Important tools to use in data science

Module 3-Data Analysis (2- Data Extracting)

  • Web scrapping (beautiful soup, requests)
  • feature extraction
  • data filtering and data manipulation
  • Scrapping tools
  • Automatic scrapping

Module 3-Data Analysis (3-Data Science with python: Pandas)

  • Dealing with dataset
  • Extracting information from dataset
  • Data Handing and formatting
  • Creating Dataframes
  • Automatic Data Cleaning
  • Automatic Data Analysis

Module 3-Data Analysis (4-Data Visualization)

  • Matplotlib basics (charts, modeling, matplotlib with dataframes)
  • Seabom basics
  • Automatic Visualization
  • Sweetvir

Module 3-Data Analysis (5-Real life data)

  • How to transform data into vectors?
  • Work with real datasets
  • getting started with Kaggle

Module 3-Data Analysis (6-Image Processing)

  • openCV basics
  • face detection, recognition
  • object detection
  • Medlapipe

Module 3-Data Analysis (7-Audio Analysis)

✨Project: End-to-End Data Projects - Report Generation

  • Wave analysis >
  • Sampling
  • Quantization
  • Audio Visualization

Module 4-Data Science

✨Project: End-to-End Data Projects - Report Generation - Story Telling

  • Cloud-Based Platforms (Google Colab -Amazon SageMaker -IBM Watson - MS Azure ML)
  • SQL
  • Recommendation systems
  • Financial Analysis
  • PoweBI
  • Tableau
  • How to write your data schema
  • How to report your insights
  • Story telling
  • How to work with any data base in python and PowerBI

Module 5-Machine Learning (1-Introduction)

  • History of Machine Learning
  • Types of Machine Learning
  • Types of Problems AI can solve

Module 5-Machine Learning (2-Classification)

  • What is Classification
  • Logistic Regression
  • Support vector machine
  • K Nearest Neighbors
  • Decision Tree
  • Random forest
  • Xgboost regression
  • Decision Tree Regression
  • LASS Oregression
  • SVM Regression
  • Neural Network regression

Module 5-Machine Learning (3-Regression)

  • Begging
  • Decision Tree Regression
  • LASSO Regression
  • Random Forest Regression
  • Ridge Regression
  • SVM Regression
  • Boosting
  • Stacking
  • Xgboost regression
  • Neural Network regression

Module 5-Machine Learning (4-Clustering)

  • Calculus in AI
  • Clustering in business
  • K-Means Clustering
  • Mean Shift Clustering

Module 5-Machine Learning (5-Save & Use your model)

✨Project: Implementation from Scratch for important models - Tips & tricks for Interviews - MOC Interview

  • Saving Your model
  • Uploading your model
  • Create an APt
  • Connecting to your model

Module 6-Deep learning (1-Introduction)

  • What is deep learning
  • Why and when to use it
  • Deep learning and Neuroscience
  • TensorFlow VS Pytorch Vs Onnx

Module 6-Deep learning (2-Deep Learning in Business)

  • Auto ML
  • Tpot
  • Clear ML
  • N2O
  • ML flow

Module 6-Deep learning (3- Artificial Neural Network (ANN))

  • ANN Intuition
  • Forward & Backward propagation
  • Error functions and optimization
  • Activation functions
  • Implementation from Scratch

Module 6-Deep learning (4-CNN)

  • Neural networks for Image classification
  • Pretrained models & Transfer Learning
  • Inception model

Module 6-Deep learning (5-RNN)

  • Time Series Analysis
  • Network Feedback

Module 6-Deep learning (6-GRU & LSTM)

  • Network memory
  • Implementation approaches

Module 6-Deep learning (7-Transformers)

  • Generative Architectures
  • Deep fake networks
  • IBM Watson
  • Applying Al in real life applications
  • Chatbots basics
  • Chatbots basics
  • wit.ai

Module 6-Deep learning (8-Computer Vision)

  • Advanced neural networks
  • Applying computer vision in medical fields
  • Applying computer vision in medical fields
  • Medical data types (CT scans, dicom files, etc..)
  • Working with 3D data types
  • image segmentation
  • 3D CNN
  • Object Detection
  • No code models
  • Data Augmentation
  • Data Annotation
  • Data Annotation

Module 6-Deep learning (9-Langchain)

  • Intro to Hugging Face
  • Hugging Face API
  • Deploying Hugging Face models

Module 6-Deep learning (10-Closing)

✨Project: Building a Simple Image Classification Model.

  • Al Automation
  • Cronjobs
  • Intro to Reinforcement learning
  • References
  • Questions and answers
  • Project selection

Module 7: Course Projects

  • Currency Classifier
  • Face rocognition/ detection Course Projects
  • Text classification
  • Market predection
  • Music Recommendation system
  • Movie Recommendation ststem
  • Cancer detection
  • Multi user Chatbot
  • image generator using GANs
  • Scrapping data from instegram We will be working with over 30 datasets from different types (Le images, string, numbers etc...)

Course Outcome & Expectations نتائج الدبلومة والتوقعات

  • أكثر من 10 مشاريع.
  • استخلاص رؤى من جميع أنواع البيانات.
Conclude Insights from all kinds of Data
  • إجراء جميع عمليات المعالجة المسبقة للبيانات اللازمة لأي مشكلة.
Perform all Data preprocessing required for a problem.
  • إنشاء مجموعات بيانات بناءً على المشكلة. وقواعد البيانات.
Generate Datasets based on the problem
  • اختيار المنهجية الأنسب لحل أي مشكلة تحسين.
Choose the best methodology to solve any optimization problem
  • إتقان الأدوات الرئيسية لعلم البيانات.
Mastering main tools of Data Science
  • ضبط النماذج وتخصيص هياكلها. Fine tune models and customize architectures
  • مقابلات تجريبية + تدريب مهني (Mock Interviews + Career Coaching)