Artificial Intelligence and Machine Learning Training
Course Duration: 6 Months
Delivery Mode:Classroom / Online / Blended
Artificial Intelligence and Machine Learning Training – Program Overview
The AI & ML Training Program is a comprehensive 6-month (132-hour) immersive course that builds foundational and advanced understanding of machine learning algorithms, neural networks, natural language processing, and production-ready AI deployment. Learners explore supervised and unsupervised learning, deep learning architectures, and model optimization through hands-on coding exercises, real-world datasets, and practical implementation projects.
The program covers critical areas including Python programming for AI, data preparation and visualization, CNN and RNN architectures, transformer models, and cloud-based deployment using Docker and AWS. With emphasis on industry-standard frameworks like TensorFlow, Keras, and Scikit-learn, participants gain the technical expertise and deployment proficiency needed to build scalable AI solutions that address real-world challenges across healthcare, finance, retail, and emerging technology sectors.
Artificial Intelligence and Machine Learning Training in Dubai -Who is it for?
- Academic Background (Preferred) : Bachelor’s degree preferred (STEM) for advanced AI/ML certifications (Computer Science, Data Science, Information Technology, Electronics, or related fields) with proficiency in English and basic Mathematics.
- Work Experience (for Advanced Program) 1–2 years in IT, software development, data analysis, or system administration roles is beneficial for advanced AI/ML specialization tracks.
- Programming Knowledge Not mandatory for entry-level modules; however, basic familiarity with Python, R, or scripting languages (Bash, PowerShell) is recommended for practical exercises.
- Computer Networking Skills Basic awareness of networking concepts, IP addressing, DNS, firewalls, and data transfer protocols is an advantage but not mandatory.d
- Language Proficiency Proficiency in English and/or Arabic (spoken and written) is mandatory, as all course materials, discussions, and assessments will be conducted in both languages.
Skills You’ll Develop with Our Artificial Intelligence and Machine Learning Course in Dubai
Gain in-demand technical and analytical skills that prepare you to excel in the fast-evolving fields of Artificial Intelligence and Machine Learning.
Machine Learning & Predictive Modeling
Learn to build, train, and evaluate intelligent models using supervised and unsupervised algorithms for real-world problem-solving.
Deep Learning & Neural Networks
Gain hands-on experience designing and training neural networks, CNNs, and RNNs to power image, speech, and text-based applications.
Natural Language Processing (NLP)
Master techniques to process and analyze human language, enabling sentiment analysis, text classification, and chatbot development.
AI Deployment & Cloud Integration
Develop the skills to deploy AI models using Flask, Docker, and AWS, making your solutions scalable and industry ready.
Software, Tools, Languages & Frameworks
AI & Machine Learning Training - Course Curriculum
Module 1: Introduction to AI
- What is AI? Evolution and Types (Narrow AI, General AI, Super AI)
- Key areas: ML, NLP, Computer Vision, Robotics, genAI
- Real-world applications of AI across domains
- Limitations and challenges of AI
- Ethics and Bias in AI
Module 2: Python for AI
- Python Basics: Variables, Data Types, Operators, Loops, Conditionals, Functions
- Data Structures in Python: Lists, Tuples, Dictionaries, Sets
- Object-Oriented Programming (OOP): Classes, Objects, Inheritance, Encapsulation
- NumPy: Arrays, Broadcasting, Vectorization
- Pandas: DataFrames, Series, Data Manipulation, Merging & Grouping
- Matplotlib and Seaborn: Plotting, customization, heatmaps, graphs
Module 3:Data Preparation and Visualization
- Data Collection Techniques: APIs, Web scraping, public datasets
- Data Cleaning Techniques: Handling missing values, duplicates, outliers
- Feature Engineering: Encoding, Scaling, Binning, Transformation
- Data Visualization for Insights: Histogram, Boxplots, Pair plots
- EDA (Exploratory Data Analysis) using Python libraries
- Analysis of real time data sets like titanic dataset, cancer data set
Module 4: Supervised Machine Learning
- What is supervised machine learning
- Regression VS classification
- Algorithms: Linear/Logistic Regression, KNN, SVM, Decision Trees, Random Forests, XGBoost
- Concepts: Labelled data, loss functions, overfitting, underfitting
- Model Evaluation: Confusion Matrix, Accuracy, Precision, Recall, F1 Score, ROC-AUC, r2 Score, Mean squared Error
- Cross-validation, Grid Search, Hyperparameter tuning
- Real time prediction of House price, Breast cancer data set
Module 5: Unsupervised Machine Learning
- Concepts: No labels data type, clustering, dimensionality reduction
- Algorithms: K-Means, Hierarchical Clustering, PCA
- Use Cases: Customer Segmentation, Anomaly Detection
Module 6: Deep Learning and Neural Networks
- Introduction to Neural Networks: Perceptron, Neuron
- Types of Neural neural network – ANN, CNN, RNN
- Activation Functions, Loss Functions
- Forward and Backpropagation, layers, Epoch, batch size
- Using TensorFlow and Keras: Model building, compiling, training, evaluating
- CNN for image data,
- RNN and LSTM for sequences
- Model Regularization & Optimization: Dropout, Early Stopping, Learning Rate Scheduling
- Real time model building on MNIST image dataset using ANN, and Cifar10 dataset using CNN
Module 7: Natural Language Processing
- Text Preprocessing: Tokenization, Stopword removal, Stemming, Lemmatization
- Text Representation: BoW, TF-IDF, Word Embeddings (Word2Vec)
- Sentiment Analysis and Text Classification
- Language Models: RNNs, Transformer basics, BERT introduction
- Chatbot Building Basics
- Twitter real data sentiment analysis
Module 8: AI Project Deployment & Cloud Integration
- Model Deployment Basics: Flask and FastAPI
- Creating REST APIs for ML
- Containerization with Docker: Dockerfiles, Dockerizing ML apps
- Model Hosting Platforms: Streamlit, Heroku, Render, Hugging Face Spaces
- Intro to Cloud (AWS): AWS Sagemaker
Module 9: Capstone Project
- Problem Definition and Domain Selection
- Data Collection & Cleaning
- Model Building (ML/DL/NLP)
- Evaluation & Deployment
- Deliverables: Web App, Code, Documentation, Presentation
- Sample projects
- Stock market data prediction
- Weather prediction
- Customer churn prediction
- Student performance prediction
