Machine Learning Courses – Present & Future Trends

Machine learning is a field of Computer Science which gives Computers the ability to learn without being explicitly programmed. Machine learning tasks are typically classified into two broad categories, depending on whether there is a learning "signal" or "feedback" available to a learning system:

• Overseen Learning:  The computer is presented with example inputs and their desired outputs, given by a "mentor", and the goal is to learn a general rule that maps inputs to outputs.
• Semi-Overseen Learning: the computer is given only an incomplete training signal: a training set with some (often many) of the target outputs missing.Activated Learning - Computer can only obtain training labels for a limited set of instances (based on a budget), and also has to optimize its choice of objects to acquire labels for. When used interactively, these can be presented to the user for labeling.
• Reinforced learning: training data (in form of rewards and punishments) is given only as feedback to the programs actions in a dynamic environment, such as driving a vehicle or playing a game against an opponent.[5]:3
• Freehand learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unmonitored learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).

Another categorization of machine learning tasks arises when one considers the desired output of a machine-learned system :

• Learner must produce a model that assigns unseen inputs to one or more (multi-label classification) of these classes. This is typically tackled in a mentored way. Spam filtering is an example of classification, where the inputs are email (or other) messages and the classes are "spam" and "not spam".
• In regression, the outputs are continuous rather than discrete. In clustering, a set of inputs is to be divided into groups. Unlike in classification, the groups are not known beforehand, making this typically an unsupervised task.
• Density estimation finds the distribution of inputs in some space.
• Dimensionality reduction simplifies inputs by mapping them into a lower-dimensional space. Topic modeling is a related problem, where a program is given a list of human language documents and is tasked to find out which documents cover similar topics.

Machine learning problems, learning to learn learns its own inductive bias based on previous experience. Developmental learning, elaborated for robot learning & other categories exist.

Career Opportunities

Computer engineering is among the fastest-growing fields with unlimited opportunity in nearly every industry.  Post Graduate Diploma Course in Machine Learning provides the knowledge and tools to the design and implementation of information processing systems and their specific components.

Post Graduate Diploma students qualify for jobs in information technology, with focus on

• Automation and Robotics
• Software Development
• Dedicated and Embedded Systems design

Potential job titles include:

•     Knowledge Engineer
•     Data Scientist
•     Robotics Engineer
•     Computer Graphics Engineer
•     Project Manager
•     Applications Engineer
•     Design Engineer
•     Development Engineer

Engineers are ready to work in the industry as well as in the field of research, development and innovation.