Courses to prefer while choosing Machine Learning as your career

By ridhigrg |Email | Apr 15, 2020 | 1620 Views

Machine Learning with Python: A Practical Introduction
Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends. This Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning.
About this course
This Machine Learning with Python course dives into the basics of machine learning using Python, an approachable and well-known programming language. You'll learn about supervised vs. unsupervised learning, look into how statistical modeling relates to machine learning, and do a comparison of each.

We'll explore many popular algorithms including Classification, Regression, Clustering, and Dimensional Reduction and popular models such as Train/Test Split, Root Mean Squared Error (RMSE), and Random Forests. Along the way, youâ??ll look at real-life examples of machine learning and see how it affects society in ways you may not have guessed!

Most importantly, you will transform your theoretical knowledge into practical skills using hands-on labs. Get ready to do more learning than your machine!

We'll explore many popular algorithms including Classification, Regression, Clustering, and Dimensional Reduction and popular models such as strain/Test Split, Root Mean Squared Error and Random Forests.

Most importantly, you will transform your theoretical knowledge into practical skills using hands-on labs. Get ready to do more learning than your machine!

What you'll learn
  • The difference between the two main types of machine learning methods: supervised and unsupervised
  • Supervised learning algorithms, including classification and regression
  • Unsupervised learning algorithms, including Clustering and Dimensionality Reduction
  • How statistical modeling relates to machine learning and how to compare them
  • Real-life examples of the different ways machine learning affects society

Data Science: Machine Learning
Build a movie recommendation system and learn the science behind one of the most popular and successful data science techniques.

Harvard University
Perhaps the most popular data science methodologies come from machine learning. What distinguishes machine learning from other computer guided decision processes is that it builds prediction algorithms using data. Some of the most popular products that use machine learning include the handwriting readers implemented by the postal service, speech recognition, movie recommendation systems, and spam detectors.

In this course, part of our Professional Certificate Program in Data Science, you will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system.

You will learn about training data, and how to use a set of data to discover potentially predictive relationships. As you build the movie recommendation system, you will learn how to train algorithms using training data so you can predict the outcome for future datasets. You will also learn about overtraining and techniques to avoid it such as cross-validation. All of these skills are fundamental to machine learning.

What you'll learn
  • The basics of machine learning
  • How to perform cross-validation to avoid overtraining
  • Several popular machine learning algorithms
  • How to build a recommendation system
  • What is regularization and why it is useful?

Quantum Machine Learning
Quantum computers are becoming available, which begs the question: what are we going to use them for? Machine learning is a good candidate. In this course, we will introduce several quantum machine learning algorithms and implement them in Python.
University of Toronto
About this course
The pace of development in quantum computing mirrors the rapid advances made in machine learning and artificial intelligence. It is natural to ask whether quantum technologies could boost learning algorithms: this field of inquiry is called quantum-enhanced machine learning. The goal of this course is to show what benefits current and future quantum technologies can provide to machine learning, focusing on algorithms that are challenging with classical digital computers. We put a strong emphasis on implementing the protocols, using open source frameworks in Python. Prominent researchers in the field will give guest lectures to provide extra depth to each major topic. These guest lecturers include Alán Aspuru-Guzik, Seth Lloyd, Roger Melko, and Maria Schuld.

In particular, we will address the following objectives:

1) Understand the basics of quantum states as a generalization of classical probability distributions, their evolution in closed and open systems, and measurements as a form of sampling. Describe elementary classical and quantum many-body systems.

2) Contrast quantum computing paradigms and implementations. Recognize the limitations of current and near-future quantum technologies and the kind of tasks where they outperform or are expected to outperform classical computers. Explain variational circuits.

3) Describe and implement classical-quantum hybrid learning algorithms. Encode classical information in quantum systems. Perform discrete optimization in ensembles and unsupervised machine learning with different quantum computing paradigms. Sample quantum states for probabilistic models. Experiment with unusual kernel functions on quantum computers

4) Demonstrate coherent quantum machine learning protocols and estimate their resources requirements. Summarize quantum Fourier transformation, quantum phase estimation, and quantum matrix, and implement these algorithms. General linear algebra subroutines by quantum algorithms. Gaussian processes on a quantum computer.

What you'll learn
  • By the end of this course, students will be able to:
  • Distinguish between quantum computing paradigms relevant for machine learning
  • Assess expectations for quantum devices on various time scales
  • Identify opportunities in machine learning for using quantum resources

Source: HOB