CS50´s Introduction to Computer Science from Harvard University!

I have already completed my computer science course in which I learned and worked with C, Python, SQL, HTML, CSS, JavaScript and Flask. 

In the Machine Learning Specialization by Stanford University and DeepLearning.AI, I gained a comprehensive understanding of key machine learning concepts and techniques. The specialization covered supervised learning, including linear regression, logistic regression, decision trees, and neural networks, which equipped me with the ability to solve classification and regression problems. I also explored unsupervised learning methods like clustering and anomaly detection, helping me to uncover hidden patterns in unlabeled data.

In addition to these core areas, I learned how to build and deploy recommendation systems using collaborative filtering and content-based approaches, a skill highly applicable in personalization algorithms. The specialization also introduced reinforcement learning, where I studied how agents learn optimal behaviors by interacting with environments to maximize cumulative rewards, a concept used in areas like robotics and gaming.

Throughout the course, I developed hands-on experience with Python and various machine learning frameworks, gaining practical skills to apply these techniques to real-world problems. I now have a strong foundation in machine learning model building, evaluation, and optimization, enabling me to create scalable and efficient AI solutions.

In this course, I have learned how to build machine learning models in Python using popular libraries such as NumPy and scikit-learn. I've gained hands-on experience in constructing and training supervised machine learning models, specifically for prediction and binary classification tasks. This includes developing models like linear regression and logistic regression, which are foundational techniques in the field of machine learning.

In the "Advanced Learning Algorithms" course by Stanford Online and DeepLearning.AI on Coursera, I deepened my understanding of advanced machine learning techniques and modern neural network architectures. One of the key takeaways was learning to build and train neural networks using TensorFlow for multi-class classification. This hands-on experience helped me tackle more complex problems in AI development.

The course also emphasized best practices for machine learning development, ensuring that models generalize well to real-world data and tasks. I now understand how to implement strategies like regularization, adaptive learning rate methods, and proper validation techniques to improve model performance and avoid overfitting.

In addition, I worked with decision trees and tree ensemble methods, including random forests and boosted trees. These are powerful tools that allow for better model interpretability and performance, especially when dealing with structured data.

I also gained insights into optimization techniques and explored unsupervised learning methods like clustering and autoencoders. The course provided me with the skills to fine-tune and deploy machine learning models effectively, preparing me to tackle real-world challenges and contribute to impactful AI projects.

By completing this course, I’ve enhanced my technical abilities in advanced algorithms and feel well-equipped to pursue further projects in AI, applying these techniques to create efficient and scalable models. 

In the Unsupervised Learning, Recommenders, Reinforcement Learning course by Stanford Online and DeepLearning.AI on Coursera, I expanded my knowledge in key areas of machine learning beyond traditional supervised learning techniques. One of the primary takeaways was learning to work with unsupervised learning algorithms such as clustering and dimensionality reduction. These methods enabled me to discover patterns in data without the need for labeled examples.

I also gained valuable experience in building recommendation systems, a widely-used application in industries like e-commerce and entertainment. I learned how to implement collaborative filtering and content-based approaches, improving my ability to design systems that provide personalized suggestions based on user behavior.

Furthermore, the course introduced me to reinforcement learning, where I explored the principles of agents making sequential decisions in environments to maximize rewards. This area of AI is foundational for applications like robotics, game playing, and self-driving cars.

By completing this course, I’ve enhanced my technical abilities in unsupervised and reinforcement learning, equipping me with the skills needed to develop more autonomous and adaptive AI systems. I now feel confident in applying these advanced techniques to real-world challenges and creating impactful machine learning solutions.

I've deepened my understanding of AI technology, explored machine learning and data science, and learned about the workflow of AI projects. I also gained insights into how AI is transforming industries and society. This course has equipped me with the knowledge to navigate AI's impact and contribute to its responsible use in the future.  

In this course I reviewed and learned more about different things, such as functions, getting help, booleans, conditionals, lists, loops, list comprehensions, strings, dictionaries and external libraries. 

Completed the self-paced training course of MATLAB Onramp from MathWorks.

With this online porsion I learnt about the fundamentals, fabrications process flow, and data analysis of a variety of devices that can be made using toos and instruments inside MIT.nano´s cleanroom. 

Intro to machine learning working with real estate databases for predictions.  

Learned about what Generative AI is, how it is used, and how it differs from traditional machine learning methods.