Learning Path

Machine Learning Skill Track

A machine learning (ML) engineer is an expert on using data to training models. The models are then used to automate processes like image classification, speech recognition, and market forecasting.

9 Courses
131 Hours in Total
14 Case Studies
Lifetime access to course Content

Market Opportunity

Machine learning enabled solutions are being significantly adopted by organizations worldwide to enhance customer experience, ROI, and to gain a competitive edge in business operations. Moreover, in the coming years, applications of machine learning in various industry verticals is expected to rise exponentially.

Career Opportunity

An entry-level machine learning salary in the United States ranges broadly, but the average is approximately $97,090. However, if you consider potential bonuses and profit-sharing, that number can rapidly rise to $130,000 or more. (source: Springboard)

Highly Demanded

What You Will Learn

  • Master Machine Learning on Python & R
  • Make accurate predictions
  • Handle specific topics like Reinforcement Learning, NLP and Deep Learning
  • Apply the most advanced Data Visualization techniques with Seaborn and Matplotlib
  • Build an army of powerful Machine Learning models and know how to combine them to solve any problem
  • Understand the underlying theory and mathematics behind Artificial Neural Networks
  • Train Artificial Neural Networks (ANNs) using back propagation and gradient descent methods
  • Apply ANNs to predict house prices given parameters such as area, number of rooms, etc
  • Develop a fraud detection classifier using Machine Learning Techniques
  • Master Python and Scikit-Learn for Data Science and Machine Learning
Machine Learning A-Z™: Python & R in Data Science [2023]
43 hours
Machine Learning Practical: 6 Real-World Applications
9 hours
Machine Learning Regression Masterclass in Python
10 hours
Machine Learning Classification Bootcamp in Python
12 hours
Machine Learning Practical Workout | 8 Real-World Projects
14 hours
Mathematical Foundations of Machine Learning
14 hours
Docker Masterclass for Machine Learning and Data Science
4 hours
No-Code Machine Learning: Practical Guide to Modern ML Tools
6 hours
Python & Machine Learning for Financial Analysis
23 hours

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