Fundamentals to Become a Machine Learning Engineer
Offered By: LinkedIn Learning
Course Description
Overview
In a rapidly expanding industry, machine learning engineers hold a vital role in designing and creating AI algorithms to build machine learning solutions. In this learning path, you'll explore the basics of AI and machine learning and then move into a hands-on approach by building machine learning applications and training your own neural networks. An existing knowledge of Python is recommended.
- Explore the tools needed to be a machine learning engineer.
- Build your own deep learning applications and chatbots.
- Create, build, and train a neural network using Python.
Syllabus
Courses under this program:
Course 1: Artificial Intelligence Foundations: Thinking Machines
-Learn the key concepts behind artificial intelligence (AI), including strong and weak AI, approaches such as machine learning, and practical uses for new AI-enhanced technologies.
Course 2: Artificial Intelligence Foundations: Machine Learning
-Learn about the machine learning lifecycle and the steps required to build systems in this hands-on course.
Course 3: Machine Learning with Python: Foundations
-Learn the basics of machine learning and how you can create a machine learning model with Python.
Course 4: Deep Learning: Getting Started
-Learn the basics of deep learning and get up and running with this technology.
Course 5: Deep Learning: Model Optimization and Tuning
-Learn about various optimization and tuning options available for deep learning models and use them to improve models.
Course 6: Reinforcement Learning Foundations
-Learn the basics of reinforcement learning (RL), including the terminology, the kinds of problems you can solve with RL, and the different methods for solving those problems.
Course 7: Training Neural Networks in Python
-Take a deep dive into the inner workings of neural networks by learning how to create one from scratch in Python.
Course 1: Artificial Intelligence Foundations: Thinking Machines
-Learn the key concepts behind artificial intelligence (AI), including strong and weak AI, approaches such as machine learning, and practical uses for new AI-enhanced technologies.
Course 2: Artificial Intelligence Foundations: Machine Learning
-Learn about the machine learning lifecycle and the steps required to build systems in this hands-on course.
Course 3: Machine Learning with Python: Foundations
-Learn the basics of machine learning and how you can create a machine learning model with Python.
Course 4: Deep Learning: Getting Started
-Learn the basics of deep learning and get up and running with this technology.
Course 5: Deep Learning: Model Optimization and Tuning
-Learn about various optimization and tuning options available for deep learning models and use them to improve models.
Course 6: Reinforcement Learning Foundations
-Learn the basics of reinforcement learning (RL), including the terminology, the kinds of problems you can solve with RL, and the different methods for solving those problems.
Course 7: Training Neural Networks in Python
-Take a deep dive into the inner workings of neural networks by learning how to create one from scratch in Python.
Courses
-
Learn the key concepts behind artificial intelligence (AI), including strong and weak AI, approaches such as machine learning, and practical uses for new AI-enhanced technologies.
-
Learn about the machine learning lifecycle and the steps required to build systems in this hands-on course.
-
Take a deep dive into the inner workings of neural networks by learning how to create one from scratch in Python.
-
Learn the basics of deep learning and get up and running with this technology.
-
Learn the basics of machine learning and how you can create a machine learning model with Python.
-
Learn the basics of reinforcement learning (RL), including the terminology, the kinds of problems you can solve with RL, and the different methods for solving those problems.
-
Learn about various optimization and tuning options available for deep learning models and use them to improve models.
Taught by
Doug Rose, Kesha Williams, Frederick Nwanganga, Kumaran Ponnambalam, Khaulat Abdulhakeem and Eduardo Corpeño
Related Courses
Computational NeuroscienceUniversity of Washington via Coursera Reinforcement Learning
Brown University via Udacity Reinforcement Learning
Indian Institute of Technology Madras via Swayam FA17: Machine Learning
Georgia Institute of Technology via edX Introduction to Reinforcement Learning
Higher School of Economics via Coursera