Certification In Machine Learning

This course will give an opportunity to gain expertise in one of the most fascinating and fastest growing areas of Computer Science that covers fascinating and compelling topics related to human intelligence and its applications in industry, defense, healthcare, agriculture and many other areas.

Machine learning (ML) is the study of computer algorithms that can improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so.

Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.

Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

These are three types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.

Machine learning is an important component of the growing field of data science. Through the use of statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights within data mining projects.

These insights subsequently drive decision making within applications and businesses, ideally impacting key growth metrics. As big data continues to expand and grow, the market demand for data scientists will increase, requiring them to assist in the identification of the most relevant business questions and subsequently the data to answer them.

Machine learning, deep learning, and neural networks are all sub-fields of artificial intelligence. However, deep learning is actually a sub-field of machine learning, and neural networks is a sub-field of deep learning.

Course Curriculum:

• Unit 1: Machine Learning (ML) – Introduction & Concepts
• Unit 2: Environment Setup & Basics (Python)
• Unit 3: Types of Machine Learning (Python)
• Unit 4: Data Pre-Processing, Analysis & Visualization (Python)
• Unit 5: Training Data & Test Data (Python)
• Unit 6: Techniques of Machine Learning (Python)
• Unit 7: Machine Learning Algorithms (Python)
• Unit 8: Machine Learning Applications (Python)