ARTIFICIAL INTELLIGENCE CERTIFICATION AUTHORITIES

Artificial Intelligence Course Features

ARTIFICIAL INTELLIGENCE LEAD MENTORS

ARTIFICIAL INTELLIGENCE COURSE FEE IN KOZHIKODE

Live Virtual

Instructor Led Live Online

154,000
92,055

  • IABAC® & DMC Certification
  • 9-Month | 780 Learning Hours
  • 100-Hour Live Online Training
  • 10 Capstone & 1 Client Project
  • 365 Days Flexi Pass + Cloud Lab
  • Internship + Job Assistance

Blended Learning

Self Learning + Live Mentoring

92,000
55,005

  • Self Learning + Live Mentoring
  • IABAC® & DMC Certification
  • 1 Year Access To Elearning
  • 10 Capstone & 1 Client Project
  • Job Assistance
  • 24*7 Learner assistance and support

Classroom

In - Person Classroom Training

154,000
105,355

  • IABAC® & DMC Certification
  • 9-Month | 780 Learning Hours
  • 100-Hour Classroom Sessions
  • 10 Capstone & 1 Client Project
  • Cloud Lab Access
  • Internship + Job Assistance

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UPCOMING ARTIFICIAL INTELLIGENCE ONLINE CLASSES IN KOZHIKODE

BEST ARTIFICIAL INTELLIGENCE CERTIFICATIONS

The entire training includes real-world projects and highly valuable case studies.

IABAC® certification provides global recognition of the relevant skills, thereby opening opportunities across the world.

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WHY DATAMITES INSTITUTE FOR ARTIFICIAL INTELLIGENCE COURSE

Why DataMites Infographic

SYLLABUS OF AI COURSE IN KOZHIKODE

MODULE 1 : DATA SCIENCE COURSE INTRODUCTION 

  • CDS Course Introduction
  • 3 Phase Learning
  • Learning Resources
  • Assessments & Certification Exams
  • DataMites Mobile App
  • Support Channels

MODULE 2 : DATA SCIENCE ESSENTIALS 

  • Introduction to Data Science
  • Evolution of Data Science
  • Data Science Terminologies
  • Data Science vs AI/Machine Learning
  • Data Science vs Analytics

MODULE3 : DATA SCIENCE DEMO 

  • Business Requirement: Use Case
  • Data Preparation
  • Machine learning Model building
  • Prediction with ML model
  • Delivering Business Value

MODULE 4 : ANALYTICS CLASSIFICATION 

  • Types of Analytics
  • Diagnostic Analytics
  • Predictive Analytics
  • Prescriptive Analytics

MODULE 5 : DATA SCIENCE AND RELATED FIELDS 

  • Introduction to AI
  • Introduction to Computer Vision
  • Introduction to Natural Language Processing
  • Introduction to Reinforcement Learning
  • Introduction to GAN
  • Introduction to  Generative Passive Models

MODULE 6 : DATA SCIENCE ROLES & WORKFLOW

  • Data Science Project workflow
  • Roles: Data Engineer, Data Scientist, ML Engineer and MLOps Engineer
  • Data Science Project stages

MODULE 7 : MACHINE LEARNING INTRODUCTION

  • What Is ML? ML Vs AI
  • ML Workflow, Popular ML Algorithms
  • Clustering, Classification And Regression
  • Supervised Vs Unsupervised

MODULE 8 : DATA SCIENCE INDUSTRY APPLICATIONS 

  • Data Science in Finance and Banking
  • Data Science in Retail
  • Data Science in Health Care
  • Data Science in Logistics and Supply Chain
  • Data Science in Technology Industry
  • Data Science in Manufacturing
  • Data Science in Agriculture

MODULE 1 : PYTHON BASICS 

  • Introduction of python
  • Installation of Python and IDE
  • Python objects
  • Python basic data types
  • Number & Booleans, strings
  • Arithmetic Operators
  • Comparison Operators
  • Assignment Operators
  • Operator’s precedence and associativity

MODULE 2 : PYTHON CONTROL STATEMENTS 

  • IF Conditional statement
  • IF-ELSE
  • NESTED IF
  • Python Loops basics
  • WHILE Statement
  • FOR statements
  • BREAK and CONTINUE statements

MODULE 3 : PYTHON DATA STRUCTURES 

  • Basic data structure in python
  • String object basics and inbuilt methods
  • List: Object, methods, comprehensions
  • Tuple: Object, methods, comprehensions
  • Sets: Object, methods, comprehensions
  • Dictionary: Object, methods, comprehensions

MODULE 4 : PYTHON FUNCTIONS 

  • Functions basics
  • Function Parameter passing
  • Iterators
  • Generator functions
  • Lambda functions
  • Map, reduce, filter functions

MODULE 5 : PYTHON NUMPY PACKAGE 

  • NumPy Introduction
  • Array – Data Structure
  • Core Numpy functions
  • Matrix Operations

MODULE 6 : PYTHON PANDAS PACKAGE 

  • Pandas functions
  • Data Frame and Series – Data Structure
  • Data munging with Pandas
  • Imputation and outlier analysis

MODULE 1 : OVERVIEW OF STATISTICS 

  • Descriptive And Inferential Statistics
  • Basic Terms Of Statistics
  • Types Of Data

MODULE 2 : HARNESSING DATA 

  • Random Sampling
  • Sampling With Replacement And Without Replacement
  • Cochran's  Minimum Sample Size
  • Simple Random Sampling
  • Stratified Random Sampling
  • Cluster Random Sampling
  • Systematic Random Sampling
  • Biased Random Sampling Methods
  • Sampling Error
  • Methods Of Collecting Data

MODULE 3 : EXPLORATORY DATA ANALYSIS 

  • Exploratory Data Analysis Introduction
  • Measures Of Central Tendencies: Mean, Median And Mode
  • Measures Of Central Tendencies: Range, Variance And Standard Deviation
  • Data Distribution Plot: Histogram
  • Normal Distribution
  • Z Value / Standard Value
  • Empherical Rule  and Outliers
  • Central Limit Theorem
  • Normality Testing
  • Skewness & Kurtosis
  • Measures Of Distance: Euclidean, Manhattan And MinkowskiDistance

MODULE 4 : HYPOTHESIS TESTING 

  • Hypothesis Testing Introduction
  • P- Value, Confidence Interval
  • Parametric Hypothesis Testing Methods
  • Hypothesis Testing Errors : Type I And Type Ii
  • One Sample T-test
  • Two Sample Independent T-test
  • Two Sample Relation T-test
  • One Way Anova Test

MODULE 5 : CORRELATION AND REGRESSION 

  • Correlation Introduction
  • Direct/Positive Correlation
  • Indirect/Negative Correlation
  • Regression
  • Choosing Right Method

MODULE 1: MACHINE LEARNING INTRODUCTION 

  • What Is ML? ML Vs AI
  • ML Workflow, Popular ML Algorithms
  • Clustering, Classification And Regression
  • Supervised Vs Unsupervised

MODULE 2: PYTHON NUMPY & PANDAS PACKAGE 

  • NumPy & Pandas functions
  • Array – Data Structure
  • Core Numpy functions
  • Matrix Operations
  • Data Frame and Series – Data Structure
  • Data munging with Pandas
  • Imputation and outlier analysis

MODULE 3: VISUALIZATION WITH PYTHON 

  • Visualization Packages (Matplotlib)
  • Components Of A Plot, Sub-Plots
  • Basic Plots: Line, Bar, Pie, Scatter
  • Advanced Python Data Visualizations

MODULE 4: ML ALGO: LINEAR REGRESSSION 

  • Introduction to Linear Regression
  • How it works: Regression and Best Fit Line
  • Modeling and Evaluation in Python

MODULE 5: ML ALGO: KNN 

  • Introduction to KNN
  • How It Works: Nearest Neighbor Concept
  • Modeling and Evaluation in Python

MODULE 6: ML ALGO: LOGISTIC REGRESSION 

  • Introduction to Logistic Regression
  • How it works: Classification & Sigmoid Curve
  • Modeling and Evaluation in Python

MODULE 7: PRINCIPLE COMPONENT ANALYSIS (PCA) 

  • Building Blocks Of PCA
  • How it works: Finding Principal Components
  • Modeling PCA in Python

MODULE 8: ML ALGO: K MEANS CLUSTERING 

  • Understanding Clustering (Unsupervised)
  • K Means Algorithm
  • How it works : K Means theory
  • Modeling in Python

MODULE 1: MACHINE LEARNING INTRODUCTION 

  • What Is ML? ML Vs AI
  • ML Workflow, Popular ML Algorithms
  • Clustering, Classification And Regression
  • Supervised Vs Unsupervised

MODULE 2: ML ALGO: LINEAR REGRESSION 

  • Introduction to Linear Regression
  • How it works: Regression and Best Fit Line
  • Modeling and Evaluation in Python

MODULE 3: ML ALGO: LOGISTIC REGRESSION 

  • Introduction to Logistic Regression
  • How it works: Classification & Sigmoid Curve
  • Modeling and Evaluation in Python

MODULE 4: ML ALGO: KNN 

  • Introduction to KNN
  • How It Works: Nearest Neighbor Concept
  • Modeling and Evaluation in Python

MODULE 5: ML ALGO: K MEANS CLUSTERING 

  • Understanding Clustering (Unsupervised)
  • K Means Algorithm
  • How it works: K Means theory
  • Modeling in Python

MODULE 6: PRINCIPLE COMPONENT ANALYSIS (PCA) 

  • Building Blocks Of PCA
  • How it works: Finding Principal Components
  • Modeling PCA in Python

MODULE 7: ML ALGO: DECISION TREE 

  • Random Forest Ensemble technique
  • How it works: Bagging Theory
  • Modeling and Evaluation in Python

MODULE 8 : ML ALGO: NAÏVE BAYES 

  • Introduction to Naive Bayes
  • How it works: Bayes' Theorem
  • Naive Bayes For Text Classification
  • Modeling and Evaluation in Python

MODULE 9: GRADIENT BOOSTING, XGBOOST 

  • Introduction to Boosting and XGBoost
  • How it works: weak learners' concept
  • Modeling and Evaluation of in Python

MODULE 10: ML ALGO: SUPPORT VECTOR MACHINE  (SVM) 

  • Introduction to SVM
  • How It Works: SVM Concept, Kernel Trick
  • Modeling and Evaluation of SVM in Python

MODULE 11: ARTIFICIAL NEURAL NETWORK (ANN) 

  • Introduction to ANN
  • How It Works: Back prop, Gradient Descent
  • Modeling and Evaluation of ANN in Python

MODULE 12: ADVANCED ML CONCEPTS 

  • Adv Metrics (Roc_Auc, R2, Precision, Recall)
  • K-Fold Cross validation
  • Grid And Randomized Search CV In Sklearn
  • Imbalanced Data Set : Smote Technique
  • Feature Selection Techniques

MODULE 1: TIME SERIES FORECASTING - ARIMA 

  • What is Time Series?
  • Trend, Seasonality, cyclical and random
  • Autoregressive Model (AR)
  • Moving Average Model (MA)
  • Stationarity of Time Series
  • ARIMA Model
  • Autocorrelation and AIC 

MODULE 2: FEATURE ENGINEERING 

  • Introduction to Features Engineering
  • Transforming Predictors
  • Feature Selection methods
  • Backward elimination technique
  • Feature importance from ML modeling

MODULE 3: SENTIMENT ANALYSIS 

  • Introduction to Sentiment Analysis
  • Python packages: TextBlob, NLTK
  • Case study: Twitter Live Sentiment Analysis

MODULE 4: REGULAR EXPRESSIONS WITH PYTHON 

  • Regex Introduction
  • Regex codes
  • Text extraction with Python Regex

MODULE 5: ML MODEL DEPLOYMENT WITH FLASK 

  • Introduction to Flask
  • URL and App routing
  • Flask application – ML Model deployment

MODULE 6: ADVANCED DATA ANALYSIS WITH MS EXCEL 

  • MS Excel core Functions • Pivot Table
  • Advanced Functions (VLOOKUP, INDIRECT..)
  • Linear Regression with EXCEL
  • Goal Seek Analysis
  • Data Table
  • Solving Data Equation with EXCEL
  • Monte Carlo Simulation with MS EXCEL

MODULE 7: AWS CLOUD FOR DATA SCIENCE

  • Introduction of cloud
  • Difference between GCC, Azure,AWS
  • AWS Service ( EC2 and S3 service)
  • AWS Service (AMI), AWS Service (RDS)
  • AWS Service (IAM), AWS (Athena service)
  • AWS (EMR), AWS, AWS (Redshift)
  • ML Modeling with AWS Sage Maker 

MODULE 8: AZURE FOR DATA SCIENCE 

  • Introduction to AZURE ML studio
  • Data Pipeline and ML modeling with Azure
  • MODULE 1: DATABASE INTRODUCTION 

    • DATABASE Overview
    • Key concepts of database management
    • CRUD Operations
    • Relational Database Management System
    • RDBMS vs No-SQL (Document DB)

    MODULE 2: SQL BASICS 

    • Introduction to Databases
    • Introduction to SQL
    • SQL Commands
    • MY SQL  workbench installation
    • Comments • import and export dataset

    MODULE 3: DATA TYPES AND CONSTRAINTS 

    • Numeric, Character, date time data type
    • Primary key, Foreign key, Not null
    • Unique, Check, default, Auto increment

    MODULE 4: DATABASES AND TABLES (MySQL) 

    • Create database
    • Delete database
    • Show and use databases
    • Create table, Rename table
    • Delete table, Delete  table records
    • Create new table from existing data types
    • Insert into, Update records
    • Alter table

    MODULE 5: SQL JOINS 

    • Inner join
    • Outer join
    • Left join
    • Right join
    • Cross join
    • Self join

    MODULE 6: SQL COMMANDS AND CLAUSES 

    • Select, Select distinct
    • Aliases, Where clause
    • Relational operators, Logical
    • Between, Order by, In
    • Like, Limit, null/not null, group by
    • Having, Sub queries

    MODULE 7: DOCUMENT DB/NO-SQL DB 

    • Introduction of Document DB
    • Document DB vs SQL DB
    • Popular Document DBs
    • MongoDB basics
    • Data format and Key methods
    • MongoDB data management

MODULE 1: GIT  INTRODUCTION 

  • Purpose of Version Control
  • Popular Version control tools
  • Git Distribution Version Control
  • Terminologies
  • Git Workflow
  • Git Architecture

MODULE 2: GIT REPOSITORY and GitHub 

  • Git Repo Introduction
  • Create New Repo with Init command
  • Copying existing repo
  • Git user and remote node
  • Git Status and rebase
  • Review Repo History
  • GitHub Cloud Remote Repo

MODULE 3: COMMITS, PULL, FETCH AND PUSH 

  • Code commits
  • Pull, Fetch and conflicts resolution
  • Pushing to Remote Repo

MODULE 4: TAGGING, BRANCHING AND MERGING 

  • Organize code with branches
  • Checkout branch
  • Merge branches

MODULE 5: UNDOING CHANGES 

  • Editing Commits
  • Commit command Amend flag
  • Git reset and revert

MODULE 6: GIT WITH GITHUB AND BITBUCKET 

  • Creating GitHub Account
  • Local and Remote Repo
  • Collaborating with other developers
  • Bitbucket Git account

MODULE 1: BIG DATA INTRODUCTION 

  • Big Data Overview
  • Five Vs of Big Data
  • What is Big Data and Hadoop
  • Introduction to Hadoop
  • Components of Hadoop Ecosystem
  • Big Data Analytics Introduction

MODULE 2: HDFS AND MAP REDUCE 

  • HDFS – Big Data Storage
  • Distributed Processing with Map Reduce
  • Mapping and reducing  stages concepts
  • Key Terms: Output Format, Partitioners, Combiners, Shuffle, and Sort
  • Hands-on Map Reduce task

MODULE 3: PYSPARK FOUNDATION 

  • PySpark Introduction
  • Spark Configuration
  • Resilient distributed datasets (RDD)
  • Working with RDDs in PySpark
  • Aggregating Data with Pair RDDs

MODULE 4: SPARK SQL and HADOOP HIVE 

  • Introducing Spark SQL
  • Spark SQL vs Hadoop Hive
  • Working with Spark SQL Query Language

MODULE 5: MACHINE LEARNING WITH SPARK ML 

  • Introduction to MLlib Various ML algorithms supported by MLib
  • ML model with Spark ML
  • Linear regression
  • logistic regression
  • Random forest

MODULE 6: KAFKA and Spark 

  • Kafka architecture
  • Kafka workflow
  • Configuring Kafka cluster
  • Operations

MODULE 1: BUSINESS INTELLIGENCE INTRODUCTION 

  • What Is Business Intelligence (BI)?
  • What Bi Is The Core Of Business Decisions?
  • BI Evolution
  • Business Intelligence Vs Business Analytics
  • Data Driven Decisions With Bi Tools
  • The Crisp-Dm Methodology

MODULE 2: BI WITH TABLEAU: INTRODUCTION 

  • The Tableau Interface
  • Tableau Workbook, Sheets And Dashboards
  • Filter Shelf, Rows And Columns
  • Dimensions And Measures
  • Distributing And Publishing

MODULE 3 : TABLEAU: CONNECTING TO DATA SOURCE 

  • Connecting To Data File , Database Servers
  • Managing Fields
  • Managing Extracts
  • Saving And Publishing Data Sources
  • Data Prep With Text And Excel Files
  • Join Types With Union
  • Cross-Database Joins
  • Data Blending
  • Connecting To Pdfs

MODULE 4 : TABLEAU : BUSINESS INSIGHTS 

  • Getting Started With Visual Analytics
  • Drill Down And Hierarchies
  • Sorting & Grouping
  • Creating And Working Sets
  • Using The Filter Shelf
  • Interactive Filters
  • Parameters
  • The Formatting Pane
  • Trend Lines & Reference Lines
  • Forecasting
  • Clustering

MODULE 5 : DASHBOARDS, STORIES AND PAGES 

  • Dashboards And Stories Introduction
  • Building A Dashboard
  • Dashboard Objects
  • Dashboard Formatting
  • Dashboard Interactivity Using Actions
  • Story Points
  • Animation With Pages

MODULE 6 : BI WITH POWER-BI 

  • Power BI basics
  • Basics Visualizations
  • Business Insights with Power BI

MODULE 1: ARTIFICIAL INTELLIGENCE OVERVIEW 

  • Evolution Of Human Intelligence
  • What Is Artificial Intelligence?
  • History Of Artificial Intelligence
  • Why Artificial Intelligence Now?
  • Ai Terminologies
  • Areas Of Artificial Intelligence
  • Ai Vs Data Science Vs Machine Learning

MODULE 2: DEEP LEARNING INTRODUCTION 

  • Deep Neural Network
  • Machine Learning vs Deep Learning
  • Feature Learning in Deep Networks
  • Applications of Deep Learning Networks

MODULE 3: TENSORFLOW FOUNDATION 

  • TensorFlow Installation and setup
  • TensorFlow Structure and  Modules
  • Hands-On: ML modeling with TensorFlow

MODULE 4: COMPUTER VISION INTRODUCTION 

  • Image Basics
  • Convolution Neural Network (CNN)
  • Image Classification with CNN
  • Hands-On: Cat vs Dogs Classification with CNN Network

MODULE 5: NATURAL LANGUAGE PROCESSING (NLP) 

  • NLP Introduction
  • Bag of Words Models
  • Word Embedding
  • Language Modeling
  • Hands-On: BERT  Algorithm

MODULE 6: AI ETHICAL ISSUES AND CONCERNS 

  • Issues And Concerns Around Ai
  • Ai And Ethical Concerns
  • Ai And Bias
  • Ai: Ethics, Bias, And Trust

MODULE 1: NEURAL NETWORKS 

  • Structure of neural networks
  • Neural network - core concepts
  • Feed forward algorithm
  • Backpropagation
  • Building neural network from scratch using Numpy

MODULE 2: IMPLEMENTING DEEP NEURAL NETWORKS 

  • Introduction to neural networks with tf2.X
  • Simple deep learning model in Keras (tf2.X)
  • Building neural network model in TF2.0 for MNIST dataset

MODULE 3: DEEP COMPUTER VISION - CNN 

  • Convolutional neural networks (CNNs)
  • Introduction
  • CNNs with Keras
  • Transfer learning in CNN
  • Style transfer
  • Flowers dataset with tf2.X
  • Examining x-ray with CNN model

MODULE 4 : RECURRENT NEURAL NETWORK 

  • RNN introduction
  • Sequences with RNNs
  • Long short-term memory networks
  • LSTM RNNs and GRU
  • Examples of RNN applications

MODULE 5: NATURAL LANGUAGE PROCESSING (NLP) 

  • Natural language processing
  • Introduction
  • NLP with RNNs
  • Creating model
  • Transformers and BERT
  • State of art NLP and projects

MODULE 6: REINFORCEMENT LEARNING 

  • Markov decision process
  • Fundamental equations in RL
  • Model-based method
  • Dynamic programming model free methods

MODULE 7: DEEP REINFORCEMENT LEARNING 

  • Architectures of deep Q learning
  • Deep Q learning
  • Policy gradient methods

MODULE 8: GENERATIVE ADVERSARIAL NETWORK (GAN) 

  • Gan introduction
  • Core concepts of GAN
  • Building GAN model with TensorFlow 2.X
  • GAN applications

MODULE 9: DEPLOYING DL MODELS IN THE CLOUD (AWS) 

  • Amazon web services (AWS)
  • AWS SageMaker Overview
  • Sage Makers from Data pipeline to deployments
  • Deploying deep learning models WS Sage maker

OFFERED ARTIFICIAL INTELLIGENCE COURSES IN KOZHIKODE

ARTIFICIAL INTELLIGENCE TRAINING REVIEWS

ABOUT ARTIFICIAL INTELLIGENCE TRAINING IN KOZHIKODE

The widespread adoption of AI technologies across sectors is driving the exponential growth of the AI market, which is expected to reach $190.61 billion by 2025. In healthcare, AI enables accurate diagnostics and personalized treatments. Finance benefits from AI's fraud detection and market prediction capabilities. Retailers leverage AI for personalized recommendations and inventory management. Manufacturers utilize AI-driven automation and predictive maintenance. This remarkable market growth reflects the invaluable role AI plays in transforming diverse industries.

DataMites offers an extensive Artificial Intelligence Course in Kozhikode with a duration of 11 months, consisting of 780 learning hours. The course includes 100 hours of live online training, allowing participants to interact with instructors and fellow learners. It features 10 capstone projects and one client project, providing practical experience in AI implementation. With a 365-day Flexi Pass, learners have the flexibility to access course materials and practice in the cloud lab. Additionally, DataMites offers on demand Artificial Intelligence offline courses in Kozhikode to cater to individual learning preferences.

DataMites provides the following Artificial Intelligence Training in Kozhikode:

  • Artificial Intelligence Engineer

  • Artificial Intelligence Expert

  • Certified NLP Expert

  • Artificial Intelligence Foundation

  • Artificial Intelligence for Managers

There are the reasons to choose DataMites for Artificial Intelligence Training in Kozhikode

  • The first is the expertise of Ashok Veda and the experienced faculty who deliver the courses.

  • The curriculum is comprehensive, covering essential AI concepts and practical applications. 

  • DataMites offers globally recognized certifications from organizations such as IABAC, NASSCOM FutureSkills Prime, and JainX, enhancing learners' credentials. 

  • The training is flexible including options for online artificial intelligence training in Kozhikode and ON DEMAND artificial intelligence offline classes in Kozhikode, allowing individuals to learn at their own pace.

  • Participants engage in projects with real-world data, gaining hands-on experience. There are artificial intelligence internship opportunities available, along with artificial intelligence courses with placement assistance and job references. 

  • Hardcopy learning materials and books are provided to support the learning process. Learners can also become part of the DataMites Exclusive Learning Community, fostering networking and collaboration. 

  • The courses are priced affordably, and scholarships are available to deserving candidates.

Kozhikode, also known as Calicut, is a vibrant city located on the Malabar Coast of Kerala, India. With its rich history, cultural heritage, and educational institutions, Kozhikode provides a conducive environment for pursuing Artificial Intelligence Certification. The city boasts a growing tech ecosystem, attracting IT companies and startups. Kozhikode offers opportunities for networking and industry collaborations, contributing to the city's emergence as an AI hub. The blend of modern amenities and a traditional setting makes Kozhikode an ideal location for individuals seeking AI training and career opportunities.

Along with artificial intelligence courses, DataMites also provides machine learning, deep learning, python training, IoT, data engineer, mlops, tableau, data mining, python for data science, data analytics and data science courses in kozhikode.

ABOUT ARTIFICIAL INTELLIGENCE COURSE IN KOZHIKODE

AI refers to the field of computer science that focuses on creating intelligent machines capable of performing tasks that typically require human intelligence, such as problem-solving, decision-making, language understanding, and visual perception.

Artificial intelligence (AI) provides several benefits, including the automation of repetitive tasks, improved efficiency in data analysis, enhanced decision-making capabilities, and driving advancements across industries. However, there are drawbacks to consider, such as potential job displacement, ethical concerns related to privacy and bias, and the risk of errors or vulnerabilities in AI systems.

AI can be seen in virtual assistants like Siri and Alexa, as well as in personalized recommendations on streaming platforms and e-commerce websites that analyze user preferences to suggest relevant content or products.

Artificial intelligence is a broader concept that focuses on creating intelligent systems, while machine learning is a subset of AI that involves algorithms enabling systems to learn from data and make predictions without explicit programming.

Prominent companies known for hiring professionals in artificial intelligence roles include Google, Microsoft, Amazon, Facebook, IBM, Apple, Tesla, and others. However, opportunities can also be found in various industries like healthcare, finance, and manufacturing, where companies actively seek AI talent.

Typically, a bachelor's or master's degree in computer science, AI, machine learning, or related fields is necessary for a career in artificial intelligence. Strong programming skills in languages like Python or Java, knowledge of algorithms, statistics, and understanding of machine learning and deep learning concepts are beneficial.

Starting an artificial intelligence career with no prior experience can be achieved through self-study, online courses, building an AI project portfolio, participating in competitions, gaining practical experience through internships or freelancing, networking with professionals, and joining AI communities for mentorship and guidance.

To pursue a career in artificial intelligence, a strong educational background in fields like computer science, mathematics, or related disciplines is essential. Bachelor's or master's degrees in computer science, AI, machine learning, or data science are often preferred. Proficiency in programming languages, algorithms, statistics, and machine learning concepts is also important.

The AI Expert Course is an advanced-level program that delves into advanced AI algorithms, cutting-edge research, emerging trends, and complex applications. It often includes specialized modules or tracks in areas like deep learning, computer vision, natural language processing, or reinforcement learning. The course aims to enhance participants' expertise and prepare them for challenging AI projects and roles.

Transitioning into an artificial intelligence career from a different field requires gaining relevant skills and knowledge through self-study, online courses, or specialized AI training programs. Building a strong AI portfolio, participating in projects or competitions, networking with professionals, and seeking internships or entry-level positions in AI can help bridge the gap and gain practical experience.

To prepare for AI job interviews and technical assessments, individuals should focus on developing a strong understanding of AI concepts, mastering programming languages and frameworks, practicing AI implementation, solving coding problems, staying updated with the latest advancements, and improving communication skills.

The future prospects of AI in the job market are promising, with increasing demand for AI professionals across industries. AI is expected to transform existing job roles and create new ones, leading to significant growth in the job market.

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FAQ’S OF ARTIFICIAL INTELLIGENCE TRAINING IN KOZHIKODE

Individuals can obtain knowledge in Artificial Intelligence through various means, such as self-study using online resources, enrolling in AI courses or degree programs, attending workshops or conferences, engaging in practical projects, and gaining hands-on experience in the field.

DataMites is a favored option for Artificial Intelligence courses in Kozhikode due to its comprehensive curriculum, experienced instructors, practical approach, flexible learning options, and placement assistance. With a well-structured curriculum covering various AI concepts and techniques, participants gain hands-on experience through real-world projects. The knowledgeable instructors offer valuable guidance and support. Flexible learning options, including online and classroom training, cater to diverse schedules. DataMites also provides placement assistance to connect participants with job opportunities in the AI field.

DataMites provides certifications in Kozhikode for Artificial Intelligence, including AI Engineer Certification, Certified NLP Expert Certification, AI Expert Certification, AI Foundation Certification, and AI for Managers Certification.

The duration of the Artificial Intelligence course in Kozhikode offered by DataMites varies depending on the specific course chosen. The duration can range from one month to a year, with flexible training options available on weekdays and weekends.

The Certified NLP Expert course provided by DataMites in Kozhikode focuses on developing Natural Language Processing (NLP) skills and applications. It covers topics like text preprocessing, sentiment analysis, named entity recognition, topic modeling, language generation, and neural network-based NLP models. The course aims to train individuals in NLP techniques and applications, enabling them to solve real-world problems using NLP algorithms and models.

The AI Engineer Course in Kozhikode aims to equip individuals with the skills and knowledge required to become proficient AI engineers. The course covers various aspects of AI, including machine learning, deep learning, natural language processing, computer vision, and AI deployment techniques. Practical projects and case studies enable participants to gain hands-on experience in building and deploying AI models for real-world scenarios.

The fee for the Artificial Intelligence Training program at DataMites in Kozhikode can vary depending on the specific course selected and the program's duration. Generally, the fee for the Artificial Intelligence course in Kozhikode falls within the range of INR 60,795 to INR 154,000.

The AI Foundation Course in Kozhikode offers a comprehensive introduction to AI. It covers the basics of AI, machine learning, and deep learning. Topics include supervised and unsupervised learning, neural networks, deep learning algorithms, model evaluation, and deployment techniques. The course aims to provide participants with a strong foundation in AI concepts and techniques, preparing them for further specialization or practical AI projects.

The AI for Managers Training in Kozhikode covers topics like AI basics, machine learning, deep learning, natural language processing, computer vision, AI implementation challenges, ethical considerations, and AI project management. The course aims to equip managers with the necessary knowledge to make informed decisions regarding AI adoption and implementation.

The average salary for an Artificial Intelligence Engineer in Kozhikode may vary based on factors such as experience, skills, industry, and the specific organization. However, the approximate average annual salary for an AI Engineer in India is around ?9,44,075.

Generally, individuals with an interest in pursuing a career inArtificial Intelligence are eligible to enroll in an Artificial Intelligence Certification Training in Kozhikode. There are usually no strict prerequisites in terms of educational background or prior experience.

In case of inability to attend a session during the Artificial Intelligence training at DataMites in Kozhikode, participants can coordinate with instructors to schedule a makeup class at a convenient time. For online training, recorded sessions will be provided, allowing participants to catch up on missed content.

To ensure a smooth process for issuing the participation certificate and booking the certification exam, participants need to bring valid photo identification proofs, such as a National ID card or driving license, as proof of identity during the training session at DataMites in Kozhikode.

Yes, prospective participants have the opportunity to attend a free demo class before enrolling in the Artificial Intelligence course at DataMites in Kozhikode. The demo class serves as an introduction to the training program, providing a glimpse of the content, teaching methodology, and overall learning experience. This allows individuals to make an informed decision about whether to enroll in the course.

Yes, DataMites provides Artificial Intelligence Courses in Kozhikode with placement assistance. The organization has a dedicated Placement Assistance Team (PAT) that supports candidates who successfully complete the course. The PAT offers assistance in various aspects of the job search process, including job connections, resume creation, conducting mock interviews, and facilitating discussions on interview questions. The aim is to assist participants in securing employment opportunities in the field of Artificial Intelligence by providing guidance and resources throughout the placement process.

The DataMites Placement Assistance Team(PAT) facilitates the aspirants in taking all the necessary steps in starting their career in Data Science. Some of the services provided by PAT are: -

  • 1. Job connect
  • 2. Resume Building
  • 3. Mock interview with industry experts
  • 4. Interview questions

The DataMites Placement Assistance Team(PAT) conducts sessions on career mentoring for the aspirants with a view of helping them realize the purpose they have to serve when they step into the corporate world. The students are guided by industry experts about the various possibilities in the Data Science career, this will help the aspirants to draw a clear picture of the career options available. Also, they will be made knowledgeable about the various obstacles they are likely to face as a fresher in the field, and how they can tackle.

No, PAT does not promise a job, but it helps the aspirants to build the required potential needed in landing a career. The aspirants can capitalize on the acquired skills, in the long run, to a successful career in Data Science.

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