Upskilling is the latest trend in the workplace, and it's easy to say why. Earning additional qualifications will improve your skills and make you more competitive in the job market. For a successful career, it makes sense to expand your skills as much as possible. Specialists with machine learning skills are in high demand. With AI being used in every industry, learning machine language is a way to take your career to the next level. Whether you're a new employee, a novice looking to change careers, or a seasoned professional looking to stay up to date, continuing education in machine language is the next best thing you can do. is a measure.
In the first course of the Machine Learning Specialization, you will:
• Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.
• Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression
The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications.
This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.
This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012.
It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)
By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.
This course is an overview of how machine learning models work and how they are used. This starts with basic statistical modeling or machine learning and will progress to building powerful models soon.
Become an expert in Machine Learning and AI with our free course to learn Machine Learning algorithms. Gain in-depth knowledge on supervised learning algorithms and unsupervised learning algorithms, k-means clustering, PCA, reinforcement learning, and Q-learning. Discover how machine learning algorithms work and how you can apply them in data analysis and automation. By the end of the course, you’ll gain the skills required for a machine learning engineer.
Common career opportunities you can pursue after completing such free machine learning courses are Machine learning engineer, Data scientist, Artificial Intelligence engineer, NLP scientist, etc.
A self-study guide for aspiring machine learning practitioners. Machine Learning Crash Course features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises.
Some of the questions answered in this course:
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