Machine learning and AI
What you'll learn
- Learn what machine learning is
- Learn the basic principles of machine learning
- Learn what artificial neurons are
- Learn the difference between machine learning and traditional programming
- Apply supervised learning techniques using Python libraries like scikit-learn
- Implement unsupervised learning algorithms such as clustering
- Build reinforcement learning agents and train them in simulated environments
- Understand and evaluate machine learning models using real-world datasets
- Visualize and interpret model results to gain actionable insights
- Compare the strengths and weaknesses of SL, UL, and RL in different scenarios
- Develop machine learning pipelines from data preprocessing to model deployment
- Use Python to experiment with hyperparameter tuning and model optimization
Requirements
- Some knowledge is linear algebra is preferred, but not absolutely necessary
- You must know object oriented programming, preferably in Python
Description
This machine learning course provides a comprehensive introduction to the core concepts underpinning modern artificial intelligence. We begin with a foundational understanding of linear algebra, exploring vectors, matrices, and their crucial role in representing and manipulating data within machine learning models.
Building on this mathematical base, we delve into the optimization process, focusing on gradient descent. This essential algorithm allows us to iteratively refine model parameters, minimizing errors and maximizing accuracy. We examine how gradient descent functions in practice, including the efficiency gains achieved through mini-batch processing, which divides large datasets into manageable subsets for faster training.
The course then transitions to the fundamental building blocks of neural networks: artificial neurons. We explore how these simplified models mimic biological neurons, processing inputs through weighted sums and activation functions. We discuss the concept of activation thresholds and synaptic strengths, drawing parallels to biological processes.
Finally, we assemble these individual neurons into interconnected neural networks. We examine how these networks learn complex patterns through backpropagation and weight adjustments, enabling them to perform tasks like image recognition and data classification. Throughout the course, we emphasize practical application, ensuring students grasp both the theoretical underpinnings and the real-world implications of machine learning. Have a nice learning time.
Who this course is for:
- People who want to approach machine learning.
- People who want to pursue a career in machine learning
Instructor
- 4.7 Instructor Rating
- 303 Reviews
- 1,828 Students
- 6 Courses
Sono Ph.D. in Fisica e Fisico dell'Ordine dei Chimici e Fisici nonché Innovation Manager iscritto all'elenco MiMIT. Lavoro da anni come data scientist e sviluppatore software. La mia esperienza con l'analisi dei dati è iniziata nell'ambito della fisica delle particelle all'interno di collaborazioni internazionali col CERN ed è proseguita nei più svariati ambiti dell'industria.