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Machine Learning with Python
Learn about Data Science and Machine Learning with Python! Including Numpy, Pandas, Matplotlib, Scikit-Learn and more!
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What you get in this Machine Learning with Python?
Course Completion Certificates
Mobile App Access
Machine Learning with Python
- You will learn how to use data science and machine learning with Python.
- You will create data pipeline workflows to analyze, visualize, and gain insights from data.
- You will build a portfolio of data science projects with real world data.
- You will be able to analyze your own data sets and gain insights through data science.
- Master critical data science skills.
- Understand Machine Learning from top to bottom.
- Replicate real-world situations and data reports.
- Learn NumPy for numerical processing with Python.
- Conduct feature engineering on real world case studies.
- Learn Pandas for data manipulation with Python.
- Create supervised machine learning algorithms to predict classes.
- Learn Matplotlib to create fully customized data visualizations with Python.
MODULE 1: Essentials Training
Courses No. of Hours Certificates Details Machine Learning with Python 2023 5h 17m ✔ Machine Learning with Python Case Study - Covid19 Mask Detector 2h 05m ✔ Machine Learning Python Case Study - Diabetes Prediction 1h 02m ✔
MODULE 2: Projects based Learning
Courses No. of Hours Certificates Details Projects and Case Studies on Machine Learning with Python 4h 5m ✔
About Machine Learning with Python
Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making.
Machine learning is a subfield of computer science stemming from research into artificial intelligence. It has strong ties to statistics and mathematical optimization, which deliver methods, theory and application domains to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit, rule-based algorithms is infeasible. Example applications include spam filtering, optical character recognition (OCR), search engines and computer vision. Machine learning is sometimes conflated with data mining,] although that focuses more on exploratory data analysis. Machine learning and pattern recognition “can be viewed as two facets of the same field.
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. 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.
Machine learning has proven to be a fruitful area of research, spawning a number of different problems and algorithms for their solution. This algorithm vary in their goals,in the available training data, and in the learning strategies. The ability to learn must be part of any system that would claim to possess general intelligence.
- No prior knowledge of machine learning required
- Basic knowledge of Python
- Anyone who wants to learn about data and analytics
- Data Engineers
- Software Engineers
- IT operations
- Technical managers