Artificial Intelligence using Python
This course provides a comprehensive introduction to Artificial Intelligence (AI) using Python, covering key AI concepts such as machine learning, deep learning, natural language processing (NLP), and computer vision.
- Course fee
- Contact us
- Duration
- 5 days
- Group rate
- Ask an advisor
Course overview
This course provides a comprehensive introduction to Artificial Intelligence (AI) using Python, covering key AI concepts such as machine learning, deep learning, natural language processing (NLP), and computer vision. Participants will learn how to build AI models from scratch, optimize performance, and deploy AI-powered applications. Hands-on programming with Python libraries like TensorFlow, Keras, Scikit-Learn, and OpenCV will be a core part of the training.
Training methodology
Hands-on Coding Labs – Writing AI models in Python
Live Demonstrations – Implementing AI solutions in real-world scenarios
Case Studies & Industry Applications – Understanding AI in various sectors
Collaborative Learning – Group projects and brainstorming sessions
Capstone Project – Developing an end-to-end AI-powered application
Target competencies
- Python Programming for AI Development
- Fundamentals of Machine Learning & Deep Learning
- Building AI Models with TensorFlow, Keras & Scikit-Learn
- Data Preprocessing & Feature Engineering
- Natural Language Processing & Computer Vision
- AI Model Optimization & Hyperparameter Tuning
- Deploying AI Models in Production Environments
Course objectives
By the end of the course, participants will be able to:
- Understand AI concepts, machine learning, and deep learning fundamentals
- Write Python scripts for AI model development
- Build classification, regression, and clustering models using Scikit-Learn
- Implement neural networks with TensorFlow & Keras
- Apply AI techniques to NLP, image recognition, and reinforcement learning
- Optimize AI models for performance and scalability
- Deploy AI applications in real-world environments
Course outline
- What is Artificial Intelligence? – History, Trends, and Applications
- AI vs. Machine Learning vs. Deep Learning
- Setting up Python Environment for AI – Jupyter Notebook, Anaconda, Colab
- Overview of Python Libraries for AI (NumPy, Pandas, Matplotlib, Scikit-Learn, TensorFlow, Keras, OpenCV)
- Introduction to Supervised & Unsupervised Learning
- Loading & Cleaning Datasets – Handling Missing & Noisy Data
- Data Normalization, Standardization & Feature Scaling
- Exploratory Data Analysis (EDA) using Matplotlib & Seaborn
- Feature Engineering – Encoding Categorical Data, Feature Selection, PCA
- Splitting Data for Training & Testing AI Models
- Linear & Logistic Regression Models
- Decision Trees & Random Forest Classifiers
- K-Nearest Neighbors (KNN) & Support Vector Machines (SVM)
- K-Means Clustering & Hierarchical Clustering
- Performance Evaluation – Confusion Matrix, Precision, Recall, F1-Score
- Introduction to Neural Networks & Backpropagation
- Building Deep Learning Models using TensorFlow & Keras
- Activation Functions (ReLU, Sigmoid, Softmax, Tanh)
- Optimizing Models – Gradient Descent, Dropout, Batch Normalization
- Hyperparameter Tuning using Grid Search & Random Search
- Text Preprocessing – Tokenization, Stopwords, Stemming, Lemmatization
- Implementing TF-IDF & Word Embeddings (Word2Vec, GloVe, BERT)
- Sentiment Analysis using NLTK & Scikit-Learn
- Building a Chatbot with NLP & Deep Learning
- Text Classification & Named Entity Recognition (NER)
- Introduction to Computer Vision & Image Processing
- Image Preprocessing – Resizing, Normalization, Augmentation
- Convolutional Neural Networks (CNNs) for Image Recognition
- Object Detection using YOLO & OpenCV
- Face Recognition with Deep Learning Models
- Basics of Reinforcement Learning – Rewards & Actions
- Implementing Q-Learning & Deep Q-Networks (DQN)
- Applications of Reinforcement Learning – Robotics, Game AI
- AI Model Optimization Techniques – Regularization, Transfer Learning
- Automated Hyperparameter Tuning with Bayesian Optimization
- Exporting & Saving AI Models – Pickle, Joblib, ONNX
- Deploying AI Models using Flask, FastAPI & TensorFlow Serving
- Hosting AI Models on AWS, Azure, Google Cloud
- Building AI-Powered Web & Mobile Applications
- Bias & Fairness in AI Models
- AI Ethics, Privacy, and Security Concerns
- Explainable AI (XAI) & Model Interpretability
- Future Trends & Career Opportunities in AI
Morning and evening classes are available. CompuBase Training Center, Abu Dhabi.
