Master AI & Deep Learning
Unlock the power of artificial intelligence and deep learning with our comprehensive training course designed for aspiring AI professionals.
This Course Includes
- 12 Sessions
- 40 Hours of Hands-on Training
- Real-World AI Projects
- Online Practical Labs
- Learn Python, TensorFlow, and PyTorch
- Practical Tasks, Lectures, and More
- Access to GPU Cloud Environments
Things You'll Learn
- Fundamentals of artificial intelligence
- Building and training deep neural networks
- Implementing machine learning algorithms
- Computer vision and natural language processing
- Optimizing models for production deployment
Course Content
Introduction to Artificial Intelligence
- Overview of AI: history, applications, and future trends.
- Key concepts: supervised, unsupervised, and reinforcement learning.
- Introduction to Python for AI development.
- Setting up your AI environment: Anaconda, Jupyter, and libraries.
- Hands-on: Building a simple linear regression model.
- Real-world examples: How Netflix and Google use AI.
- Understanding datasets: preprocessing and feature engineering.
- Ethics in AI: Bias, fairness, and transparency.
- Practical exercise: Cleaning a dataset for AI modeling.
Machine Learning Foundations
- Core algorithms: Decision trees, SVMs, and k-NN.
- Evaluation metrics: Accuracy, precision, recall, and F1-score.
- Overfitting vs. underfitting: Regularization techniques.
- Hands-on: Training a classifier with scikit-learn.
- Hyperparameter tuning with grid search and cross-validation.
- Ensemble methods: Random forests and gradient boosting.
- Real-world scenario: Predicting customer churn.
- Hands-on lab: Building a recommendation system.
- Scaling ML models with large datasets.
Deep Learning with Neural Networks
- Introduction to neural networks: Perceptrons and layers.
- Activation functions: ReLU, sigmoid, and tanh.
- Building models with TensorFlow and PyTorch.
- Backpropagation and gradient descent optimization.
- Hands-on: Creating a feedforward neural network.
- Convolutional Neural Networks (CNNs) for image data.
- Recurrent Neural Networks (RNNs) for sequential data.
- Hands-on lab: Classifying handwritten digits with a CNN.
- Optimizing deep learning models: Dropout and batch normalization.
Advanced AI Applications
- Computer vision: Object detection and image segmentation.
- Natural Language Processing (NLP): Tokenization and embeddings.
- Hands-on: Building a sentiment analysis model with NLP.
- Transfer learning with pre-trained models (e.g., BERT, ResNet).
- Generative AI: GANs and autoencoders.
- Hands-on lab: Generating images with a GAN.
- Reinforcement learning basics: Q-learning and policy gradients.
- Real-world project: Training an AI to play a simple game.
- Deploying AI models with Flask and cloud platforms.
AI in Production
- Model optimization: Quantization and pruning.
- Deploying models to production with Docker and Kubernetes.
- Monitoring AI systems: Drift detection and retraining.
- Hands-on: Deploying a deep learning model to AWS.
- Scaling AI workloads with GPU clusters.
- Security in AI: Adversarial attacks and defenses.
- Hands-on lab: Securing a model against data poisoning.
- Case study: How Tesla uses AI for autonomous driving.
- Final project: End-to-end AI solution development.
Why Choose This Course?
- Expert-led sessions by AI practitioners
- Hands-on projects with real-world datasets
- Flexible online learning with live support
- Comprehensive coverage of AI tools and frameworks
- Prepares you for AI certifications and careers