Artificial Intelligence and Deep learning techniques

Name: Artificial Intelligence and Deep learning techniques
Rating: 4.1 (138 reviews)

Searching Techniques, Knowledge and Reasoning Approaches, Deep Learning Techniques

Created bySaravanan T R

Last updated 6/2024

English

English [Auto],

What you'll learn

Understand the search technique procedures applied to real world problems
Represent various knowledge representation techniques to solve complex AI problems
Understand Deep neural network and layered learning approach with real world Scenario problems
Gain knowledge about various deep learning algorithm

Course content

3 sections • 14 lectures • 3h 23m total length

What is Artificial Intelligence9:21
Artificial intelligence enables machines to think by processing past data; human intelligence acts on current situations using experience. The lecture contrasts alarms and traffic signals with fixed timing.
Introduction to AI techniques20:38
Explore ai techniques—from machine learning, deep learning, natural language processing, computer vision, fuzzy logic, and cognitive computing—covering supervised, unsupervised, reinforcement, and semi-supervised learning, with regression, classification, and clustering.
Problem solving using AI10:06
Explore problem solving in AI by analyzing problem statements with their mathematical background, applying algorithms like minimax, and defining states, actions, goals, operators, and representations.
Problem Solving in AI Quiz
Basic Relationship of AI,ML.DL and Data Science18:10
Explore the relationship between artificial intelligence, machine learning, deep learning, and data science, and how labeled and unlabeled data drive supervised, unsupervised, semi-supervised, and reinforcement learning.
Introduction to Agent13:48
An agent perceives the environment with sensors, processes input, and acts through actuators in a perception cycle; robotic and software agents map input to output.
Types of Agent with Example26:07
The lecture categorizes agents into five types—table driven, simple reflex, model based reflex, goal based, and utility based—and explains their environments, sensors, actuators, and percept history.
Agent and Types Quiz

Knowledge and Reasoning basics15:44
Understand how knowledge representation organizes past data for intelligent behavior. Explore perception, learning methods, and deductive, inductive, common sense, abductive, monotonic and non-monotonic reasoning within the knowledge cycle.
Propositional logic examples9:22
Explore propositional logic as a boolean framework using truth tables, negation, conjunction, disjunction, implication, and if-and-only-if, with practical examples like it is raining and the road is wet.
Propositional logic Quiz
First Order Logic examples16:41
Explains first order logic as an extension of propositional logic, using predicates and universal and existential quantifiers to express relationships among objects via atomic and complex sentences beyond Boolean notions.
Predicate Logic Quiz

Introduction to Machine Learning11:17
Explore how machine learning enables systems to learn from data, using algorithms to train and test models, data cleaning, and apply supervised, unsupervised, regression, classification, clustering, and deep learning.
Type of Machine Learning with Examples18:44
Explore the main machine learning types—supervised, unsupervised, semi-supervised, and reinforcement learning—and their real-world uses. Understand regression and classification, labeled versus unlabeled data, and clustering algorithms like k-means and DBSCAN.
Type of Machine Learning Quiz
Artificial Intelligence vs Machine Learning vs Deep Learning vs Generative AI6:45
Compare artificial intelligence, machine learning, deep learning, and generative ai, and define their relationships from thinking machines to neural networks and large language models.
Artificial Intelligence vs Machine Learning vs Deep Learning vs Generative AI
Machine Learning Algorithm - Program Step by Step9:07
Walk through a step-by-step machine learning workflow using linear regression to predict student scores, including data import, visualization, train-test split, and mean squared error, mean absolute error, and R2 evaluation.
Deep Learning Techniques with Example17:32
Explore deep learning with neural networks, input, hidden, and output layers, activation functions like ReLU and sigmoid, backpropagation, and image classification applications.
Deep Learning Techniques Quiz

Requirements

You will learn every thing you need to know , Python knowledge needed for implementation

Description

The course Artificial Intelligence and Deep learning techniques provides the ,Knowledge on problem formulation and solving in AI and ML for real world situation,Impress interviewers by showing an understanding of the Knowledge and Reasoning Approaches with the learning techniques,exemplify the uninformed and informed search technique procedures for real world problems,Scenario based learning for all Artificial Intelligence and Deep learning techniques,Deep knowledge about Learning in Deep Learning and Neural Networks,Provide a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is applied to problems. Build your deep learning foundations and learn effective applications,Work on curated industry Projects in cliennt side as a industry Expert, Gain knowledge in problem formulation and building intelligent agents, Understand the search technique procedures applied to real world problems, Understand the types of logic and knowledge representation schemes, Acquire knowledge in planning and learning algorithms, Gain knowledge in AI Applications and advances in Artificial Intelligence, Build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applications, Train test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks, Build a goodl portfolio with entire Artificial Intelligence and Deep learning techniques

Who this course is for:

Beginner python developer to know about Artificial Intelligence and Deep learning techniques

Artificial Intelligence and Deep learning techniques

What you'll learn

Explore related topics

Course content

Introduction to Artificial Intelligence6 lectures • 1hr 38min

Knowledge and Reasoning Approaches3 lectures • 42min

Machine Learning and Deep Learning5 lectures • 1hr 3min

Requirements

Description

Who this course is for: