The fundamental principles of Machine Learning
No prior machine learning experience necessary.
What this course is about:
In this course, you will learn the fundamentals of Machine Learning and reinforce these concepts by working with real data and building meaningful predictive models. Afterwards, you will have a portfolio where you can showcase your projects, and the necessary foundation in Machine Learning to continue in a direction that best fits your interests and skillset.
If you are aspiring programmer, whether beginner or jedi, this course is for new learners on the pursuit to master machine learning concepts. This course discusses the FUNDAMENTAL principles of machine learning. No prior machine learning experience necessary. Beyond doing your part as a hard working student, you walk away with skills that translate to the programming marketplace that can either cultivate your future programming career or give you a bump in your pre existing career by showing employers your new skillset. The decision on how hard you work lies with you. In this one week course, you will become involved in a decision-making process surrounding the usage of machine learning, how it can help achieve business and project goals, which machine learning techniques to use, potential pitfalls, and how to interpret the results. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems.
Who this program is for:
This course is for people who 1) want to get a sense of what Deep Learning is all about and 2) who’d like to use the high-level libraries Keras and TensorFlow to write code that implement the basics of Deep Learning on their own laptops or in the cloud via Jupyter Notebooks.
Typical studying day starts at 10pm with a previous day recap and completing previous exercises. Lecture on new topics takes about two hours and starts at 11.00pm. After lecture, students start working on new exercises with instructor guidance. Around 3pm students present and discuss their work with instructors, learn alternative solutions, and best practices from instructors and invited professional programmers.
You will learn:
- Regression: Linear Regression, Polynomial Regression, Backward Elimination of Regressors
- Classification: Naive Bayes, Logistic Regression, Support Vector Machines
- Resampling, Bootstrapping, and Cross Validation
- Regularization: Lasso and Ridge Regression
- Dimension Reduction Trees: Decision Trees, Bagging, Boosting, Random Forest
- Unsupervised Learning: K-Means Clustering,
- Neural Networks: Intro To Artificial Neural Networks and Deep Learning
Prerequisites & Preparation:
- Python Programming 101