• Introduction of Machine Learning
• What Today’s AI can Do?
• Traditional AI
• Categories of Machine Learning
• Supervised Learning
• Scikit-Learn Algorithm
• Unsupervised Learning
• Artificial Neural Network
• Deep Learning
• Skills for Machine Learning
• Implementing Machine Learning

Artificial Intelligence and Machine Learning courses in Ranchi

Before leading to the meaning of artificial intelligence let understand what is the meaning of Intelligence- 

Intelligence: The ability to learn and solve problems. This definition is taken from webster’s Dictionary. 

The most common answer that one expects is “to make computers intelligent so that they can act intelligently!”, but the question is how much intelligent? How can one judge intelligence? 

as intelligent as humans. If the computers can, somehow, solve real-world problems, by improving on their own from past experiences, they would be called “intelligent”. 
Thus, the AI systems are more generic(rather than specific), have the ability to “think” and are more flexible. 

Intelligence, as we know, is the ability to acquire and apply knowledge. Knowledge is the information acquired through experience. Experience is the knowledge gained through exposure(training). Summing the terms up, we get artificial intelligence as the “copy of something natural(i.e., human beings) ‘WHO’ is capable of acquiring and applying the information it has gained through exposure.” 

Intelligence is composed of:  

• Reasoning
• Learning
• Problem Solving
• Perception
• Linguistic Intelligence

Many tools are used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics. The AI field draws upon computer science, mathematics, psychology, linguistics, philosophy, neuroscience, artificial psychology, and many others. 

Need for Artificial Intelligence  

1. To create expert systems that exhibit intelligent behavior with the capability to learn, demonstrate, explain, and advise its users.
2. Helping machines find solutions to complex problems like humans do and applying them as algorithms in a computer-friendly manner.

Applications of AI include Natural Language Processing, Gaming, Speech Recognition, Vision Systems, Healthcare, Automotive, etc. 

An AI system is composed of an agent and its environment. An agent(e.g., human or robot) is anything that can perceive its environment through sensors and acts upon that environment through effectors. Intelligent agents must be able to set goals and achieve them. In classical planning problems, the agent can assume that it is the only system acting in the world, allowing the agent to be certain of the consequences of its actions. However, if the agent is not the only actor, then it requires that the agent can reason under uncertainty. This calls for an agent that cannot only assess its environment and make predictions but also evaluate its predictions and adapt based on its assessment. Natural language processing gives machines the ability to read and understand human language. Some straightforward applications of natural language processing include information retrieval, text mining, question answering, and machine translation. Machine perception is the ability to use input from sensors (such as cameras, microphones, sensors, etc.) to deduce aspects of the world. e.g., Computer Vision. Concepts such as game theory, decision theory, necessitate that an agent is able to detect and model human emotions. 

Many times, students get confused between Machine Learning and Artificial Intelligence, but Machine learning, a fundamental concept of AI research since the field’s inception, is the study of computer algorithms that improve automatically through experience. The mathematical analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as a computational learning theory. 

Stuart Shapiro divides AI research into three approaches, which he calls computational psychology, computational philosophy, and computer science. Computational psychology is used to make computer programs that mimic human behavior. Computational philosophy is used to develop an adaptive, free-flowing computer mind. Implementing computer science serves the goal of creating computers that can perform tasks that only people could previously accomplish. 

AI has developed a large number of tools to solve the most difficult problems in computer science, like:  

• Search and optimization
• Logic
• Probabilistic methods for uncertain reasoning
• Classifiers and statistical learning methods
• Neural networks
• Control theory
• Languages

High-profile examples of AI include autonomous vehicles (such as drones and self-driving cars), medical diagnosis, creating art (such as poetry), proving mathematical theorems, playing games (such as Chess or Go), search engines (such as Google search), virtual assistants (such as Siri), image recognition in photographs, spam filtering, prediction of judicial decisions[204] and targeted online advertisements. Other applications include Healthcare, Automotive, Finance, Video games, etc 

Are there limits to how intelligent machines – or human-machine hybrids – can be? A superintelligence, hyperintelligence, or superhuman intelligence is a hypothetical agent that would possess intelligence far surpassing that of the brightest and most gifted human mind. ‘‘Superintelligence’’ may also refer to the form or degree of intelligence possessed by such an agent. 

Our fee structure is most reasonable as per current market demands. We understand the importance of hard earned money and hence we have very cost effective fee structure for different role based trainings which is affordable compared to other institute in market.

We also have discount plans in case of more students from single college or group of students willing to enroll for specific course. In group of students we provide special discount making the total fee even more affordable.

• Live online Classes
• Jr Coding Classes
• Role Based Training
• Programming/Coding and Frameworks
• Full Stack Development
• Internship (Online/Offline) 
• Live Project Training

1) What do you understand by Machine learning?

Machine learning is the form of Artificial Intelligence that deals with system programming and automates data analysis to enable computers to learn and act through experiences without being explicitly programmed.

2) Differentiate between inductive learning and deductive learning?

In inductive learning, the model learns by examples from a set of observed instances to draw a generalized conclusion. On the other side, in deductive learning, the model first applies the conclusion, and then the conclusion is drawn.

• Inductive learning is the method of using observations to draw conclusions.
• Deductive learning is the method of using conclusions to form observations.

3) What is the difference between Data Mining and Machine Learning?

Data mining can be described as the process in which the structured data tries to abstract knowledge or interesting unknown patterns. During this process, machine learning algorithms are used.

Machine learning represents the study, design, and development of the algorithms which provide the ability to the processors to learn without being explicitly programmed.

4) What is the meaning of Overfitting in Machine learning?

Overfitting can be seen in machine learning when a statistical model describes random error or noise instead of the underlying relationship. Overfitting is usually observed when a model is excessively complex. It happens because of having too many parameters concerning the number of training data types. The model displays poor performance, which has been overfitted.

5) What are the different types of Algorithm methods in Machine Learning?

The different types of algorithm methods in machine earning are:

• Supervised Learning
• Semi-supervised Learning
• Unsupervised Learning
• Transduction
• Reinforcement Learning