Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine Learning focuses on the development of computer programs that can access data and use it learn for themselves. At the outset of a machine learning project, a dataset is usually split into two or three subsets.
- Introduction to Machine Learning
- Walking with Python or R
- Machine Learning Techniques
- Supervised Learning
- Supervised Learning-Regression
- Supervised Learning- Classification
- Unsupervised Learning
- Unsupervised Learning- Clustering
- Unsupervised Learning- Recommendation
- Unsupervised Learning- Deep Learning
- Spark Core and MLLib
- Machine learning will automates analytic model building
- It can easily consume unlimited amounts of data with timely analysis and assessment
- Machine learning algorithms tend to operate at expedited levels
- Machine learning use this data analysis technique to predict admission rates
- Machine learning is proactive and specifically designed for action and reaction industries
- Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression, but who want to learn more about it and explore all the different fields of Machine Learning.
- Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets and who want to start a career in Data Science.
- Any data analysts who want to level up in Machine Learning.
- Any people who are not satisfied with their job and who want to become a Data Scientist.
- Any people who want to create added value to their business by using powerful Machine Learning tools
- Exam Format: Multiple Choice
- Number of Questions: 40
- Exam Pass Mark: 26 out of 40 (65%)
- Electronic Devices Permitted: No
- Open Book: No
Note – This certificate does not expire or require renewal.