Development > Data Science
58 h
£34.99 £12.99
Become an AI Engineer: The 2025 AI & Machine Learning Masterclass
What you will learn:
- Master Python programming for AI and ML.
- Become proficient in data preprocessing for AI model optimization.
- Develop a strong understanding of statistical methods relevant to AI.
- Build and fine-tune various machine learning models.
- Gain expertise in deep learning techniques, including CNNs, RNNs, and Transformers.
- Learn and apply transfer learning to solve specific AI problems.
- Master techniques to effectively deploy ML models using APIs.
- Utilize Docker for containerizing and deploying models across environments.
- Become proficient in monitoring, maintaining, and retraining AI models.
- Understand the complete ML lifecycle, from conceptualization to deployment and ongoing maintenance.
- Explore emerging fields in AI like LangChain and AI agents.
- Learn the basics of MLOps for efficient AI model deployment and management.
Description
Launch your AI career with our intensive 16-week bootcamp! This comprehensive program isn't just theory; it's hands-on AI engineering from the ground up. You'll conquer Python programming, master essential data science techniques, and dive deep into machine learning algorithms, including cutting-edge deep learning models (CNNs, RNNs, Transformers). We'll cover everything from fundamental concepts and statistical methods to industry-standard frameworks like TensorFlow, PyTorch, and Hugging Face. Throughout the bootcamp, you'll tackle real-world projects, building your portfolio and practical skills while gaining confidence in tackling industry challenges.
Start with the basics of Python programming and data manipulation, then progress through essential mathematical foundations including Linear Algebra, Calculus, and Probability & Statistics. Master supervised and unsupervised machine learning, exploring algorithms like linear regression, logistic regression, decision trees, and ensemble methods. Delve into advanced topics such as neural networks, convolutional neural networks (CNNs) for image processing, recurrent neural networks (RNNs) for sequence modeling, and the transformative power of transformers and attention mechanisms. You'll learn to leverage powerful frameworks like TensorFlow and PyTorch to build, train, and optimize AI models. We'll show you how to deploy these models effectively. Furthermore, you'll explore the emerging fields of AI agents, LangChain, and MLOps, equipping you for success in the cutting-edge AI industry.
This bootcamp is designed for both AI beginners and those seeking to upskill. Regardless of your background, you'll emerge with the knowledge and practical experience to navigate the complexities of artificial intelligence. Become a sought-after AI Engineer, researcher, or innovator – enroll today!
Bootcamp Highlights:
- 16 weeks of intensive, project-based learning
- Master Python programming and data science techniques
- Hands-on experience with TensorFlow, PyTorch, and Hugging Face
- Build a portfolio of real-world AI projects
- Expert instructors guiding your journey
- Career readiness and industry expertise
Don't wait – transform your future. Enroll now!
Curriculum
Week 1: Python Programming Basics for AI
This introductory week covers essential Python programming concepts for AI development. You'll begin with the fundamentals of Python setup and syntax, progressing through control flow, functions, modules, and data structures (lists, tuples, dictionaries, sets). String manipulation and file handling techniques are also addressed, concluding with practical coding exercises to solidify your understanding.
Week 2: Data Science Essentials for AI
This week introduces the core libraries used in data science. You will learn to utilize NumPy for numerical computation, Pandas for data manipulation and cleaning, and Matplotlib/Seaborn for data visualization. The week culminates in an exploratory data analysis (EDA) project, applying the learned techniques to a real-world dataset.
Week 3: Mathematics for Machine Learning and AI
This week lays the mathematical foundation necessary for machine learning. You will cover linear algebra fundamentals and advanced concepts, calculus (derivatives and integrals), probability theory, and statistical fundamentals. A mini project involving linear regression from scratch provides practical application of these concepts.
Week 4: Probability and Statistics for Machine Learning
Building on the previous week, this section delves deeper into probability distributions, statistical inference, hypothesis testing, correlation, and regression analysis. A real-world data analysis project allows for applying statistical methods to draw meaningful insights.
Week 5: Introduction to Machine Learning
This week introduces the core concepts of machine learning. You'll learn about supervised learning, regression models, classification techniques, model evaluation, and the k-Nearest Neighbors algorithm. A mini-project allows you to build your first machine learning model.
Week 6: Feature Engineering and Model Evaluation
This week focuses on techniques to enhance model performance. You'll learn about feature engineering, data scaling, encoding categorical variables, feature selection, and various model evaluation techniques including cross-validation and hyperparameter tuning.
Week 7: Advanced Machine Learning Algorithms
This week introduces ensemble learning techniques, exploring bagging, random forests, boosting, XGBoost, LightGBM, CatBoost, and methods for handling imbalanced datasets. You’ll conclude with a project comparing different models on a real dataset.
Week 8: Model Tuning and Optimization
This week provides strategies for improving model accuracy. You'll learn about hyperparameter tuning (grid search, random search, Bayesian optimization), regularization techniques, and automated tuning using GridSearchCV and RandomizedSearchCV. The week concludes with a project focused on building and tuning a final model.
Week 9: Neural Networks and Deep Learning Fundamentals
The course delves into the fundamentals of neural networks and deep learning. This includes forward propagation, activation functions, loss functions, backpropagation, gradient descent, and building neural networks with TensorFlow/Keras and PyTorch. A project involves image classification on CIFAR-10.
Week 10: Convolutional Neural Networks (CNNs)
This week concentrates on CNNs, covering convolutional layers, filters, pooling layers, building CNN architectures with Keras/TensorFlow and PyTorch, regularization, and data augmentation. A project focuses on image classification using Fashion MNIST or CIFAR-10.
Week 11: Recurrent Neural Networks (RNNs) and Sequence Modeling
This week explores RNNs and sequence modeling, covering RNN architecture, backpropagation through time (BPTT), LSTM networks, GRUs, text preprocessing, word embeddings, sequence-to-sequence models, and applications. A project focuses on text generation or sentiment analysis.
Week 12: Transformers and Attention Mechanisms
This week covers attention mechanisms, transformer architecture, self-attention, multi-head attention, positional encoding, feed-forward networks, and pre-trained transformers (BERT, GPT). You'll work with advanced transformers and complete a project such as text summarization or translation.
Week 13: Transfer Learning and Fine-Tuning
This week explores techniques to adapt pre-trained models to new tasks. You'll cover transfer learning in computer vision and NLP, fine-tuning techniques, domain adaptation, and challenges. A project involves fine-tuning for a custom task.
Machine Learning Algorithms and Implementations
This section provides detailed implementations of various machine learning algorithms in Python, including linear regression, logistic regression, support vector machines, decision trees, random forests, k-means clustering, principal component analysis (PCA), and more. It serves as a practical coding supplement to the theoretical concepts.
Introduction to Machine Learning and TensorFlow
This section provides a foundational introduction to Machine Learning, then dives into TensorFlow, covering its features and benefits, installation, and setting up your development environment.
Basics of TensorFlow
This section covers the fundamental aspects of TensorFlow, including tensors, tensor operations, constants, variables, placeholders, the computational graph, sessions, and building simple neural networks.
Intermediate TensorFlow
This section builds on the basics, introducing the Keras API, building complex models, training, evaluation, CNNs, RNNs, and transfer learning with pre-trained CNNs.
Advanced TensorFlow
This section focuses on advanced TensorFlow techniques including saving/loading models, TensorFlow Serving, TensorFlow Lite, distributed computing, TFX, and building end-to-end ML pipelines.
Practical Applications and Projects
This section provides numerous practical applications of TensorFlow, including projects on image classification, natural language processing, recommender systems, object detection, sentiment analysis, image recognition, time series prediction, and chatbot development.
Further Learning and Resources in TensorFlow
This section offers suggestions for continued learning, exploring topics such as GANs, reinforcement learning, TensorFlow Quantum, and various resources including documentation, tutorials, online courses, books, and communities.
Introduction to Learning PyTorch from Basics to Advanced
This section provides a comprehensive guide to PyTorch, starting with the basics and progressively covering advanced topics such as working with tensors, autograd, building neural networks, handling complex data, model deployment, debugging, and distributed training.
LangChain for Beginners
This section introduces LangChain and guides you through several projects using it, including question answering, sentiment analysis, document summarization, keyword extraction, and chatbot development.
AI Agents for Dummies
This detailed section covers AI agents comprehensively, starting with fundamental concepts and types, then delving into technologies, frameworks (AutoGPT, IBM Bee, LangGraph, CrewAI), applications across various fields, future trends, ethical implications, and broader impacts.
AI Agents: A Comprehensive Overview
This section focuses on hands-on practice with various AI agent frameworks (AutoGen, IBM Bee, LangGraph, CrewAI, AutoGPT), allowing you to build and experiment with these tools.
Creating and Publishing GPTs to ChatGPT Store
This section covers the process of creating and publishing your own GPTs to the ChatGPT Store, which is a useful skill for monetizing your skills.
Introduction and Hands-on MLOps
This section provides an introduction to MLOps, covering key concepts, practical applications, and hands-on experience in setting up a basic MLOps project structure and building an end-to-end pipeline. It also explores cloud platforms and Kubernetes for ML workloads.
Miscellaneous Projects on AI for Daily Practice
This section provides numerous smaller projects, spanning different AI techniques and applications, for daily practice and skill reinforcement. Topics include calculators, image classifiers, chatbots, spam detectors, and various prediction models.