ARTIFICIAL INTELLIGENCE CERTIFICATION AUTHORITIES

Artificial Intelligence Course Features

ARTIFICIAL INTELLIGENCE LEAD MENTORS

ARTIFICIAL INTELLIGENCE COURSE FEE IN SURAT

Live Virtual

Instructor Led Live Online

154,000
81,900

  • 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
57,900

  • 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
86,900

  • 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 SURAT

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 SURAT

MODULE 1 : ARTIFICIAL INTELLIGENCE OVERVIEW 

• Evolution Of Human Intelligence
• What Is Artificial Intelligence?
• History Of Artificial Intelligence
• Why Artificial Intelligence Now?
• 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

MODULE3 : TENSORFLOW FOUNDATION

• 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
• 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 : PYTHON BASICS 

 • Introduction of python
 • Installation of Python and IDE
 • Python Variables
 • Python basic data types
 • Number & Booleans, strings
 • Arithmetic Operators
 • Comparison Operators
 • Assignment Operators

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
 • Basics of List
 • List: Object, methods
 • Tuple: Object, methods
 • Sets: Object, methods
 • Dictionary: Object, methods

MODULE 4 : PYTHON FUNCTIONS 

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

MODULE 1 : OVERVIEW OF STATISTICS 

 • Introduction to 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
 • Types of Sampling
 • Simple Random Sampling
 • Stratified Random Sampling
 • Cluster Random Sampling
 • Systematic Random Sampling
 • Multi stage Sampling
 • 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 & Properties
 • Z Value / Standard Value
 • Empherical Rule and Outliers
 • Central Limit Theorem
 • Normality Testing
 • Skewness & Kurtosis
 • Measures Of Distance: Euclidean, Manhattan And Minkowski Distance
 • Covariance & Correlation

MODULE 4 : HYPOTHESIS TESTING 

 • Hypothesis Testing Introduction
 • P- Value, Critical Region
 • Types of Hypothesis Testing
 • Hypothesis Testing Errors : Type I And Type II
 • Two Sample Independent T-test
 • Two Sample Relation T-test
 • One Way Anova Test
 • Application of Hypothesis testing

MODULE 1: MACHINE LEARNING INTRODUCTION 

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

MODULE 2: PYTHON NUMPY  PACKAGE 

• Introduction to Numpy Package
 • Array as Data Structure
 • Core Numpy functions
 • Matrix Operations, Broadcasting in Arrays

MODULE 3: PYTHON PANDAS PACKAGE

 • Introduction to Pandas package
 • Series in Pandas
 • Data Frame in Pandas
 • File Reading in Pandas
 • Data munging with Pandas

MODULE 4:  VISUALIZATION WITH PYTHON - Matplotlib 

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

MODULE 5: PYTHON VISUALIZATION PACKAGE - SEABORN

 • Seaborn: Basic Plot
 • Advanced Python Data Visualizations

MODULE 6: ML ALGO: LINEAR REGRESSION

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

MODULE 7: ML ALGO: LOGISTIC REGRESSION 

 • Introduction to Logistic Regression
 • How it works: Classification & Sigmoid Curve
 • Modeling and Evaluation 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 9: ML ALGO: KNN

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

MODULE 1:  FEATURE ENGINEERING 

 • Introduction to Feature Engineering
 • Feature Engineering Techniques: Encoding, Scaling, Data Transformation
 • Handling Missing values, handling outliers
 • Creation of Pipeline
 • Use case for feature engineering

MODULE 2: ML ALGO: SUPPORT VECTOR MACHINE (SVM)

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

MODULE 3: PRINCIPAL COMPONENT ANALYSIS (PCA)

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

MODULE 4: ML ALGO: DECISION TREE 

 • Introduction to Decision Tree & Random Forest
 • How it works
 • Modeling and Evaluation in Python

MODULE 5: ENSEMBLE TECHNIQUES - BAGGING

 • Introduction to Ensemble technique 
 • Bagging and How it works
 • Modeling and Evaluation in Python

MODULE 6: ML ALGO: NAÏVE BAYES

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

MODULE 7:  GRADIENT BOOSTING, XGBOOST 

 • Introduction to Boosting and XGBoost
 • How it works?
 • Modeling and Evaluation of in Python

MODULE 1: TIME SERIES FORECASTING - ARIMA 

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

MODULE 2:  SENTIMENT ANALYSIS

 • Introduction to Sentiment Analysis
 • NLTK Package
 • Case study: Sentiment Analysis on Movie Reviews

MODULE 3:  REGULAR EXPRESSIONS WITH PYTHON 

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

MODULE 4: ML MODEL DEPLOYMENT WITH FLASK 

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

MODULE 5: ADVANCED DATA ANALYSIS WITH MS EXCEL 

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

MODULE 6:  AWS CLOUD FOR DATA SCIENCE

 • Introduction of cloud
 • Difference between GCC, Azure,AWS
 • AWS Service ( EC2 instance)

MODULE 7: AZURE FOR DATA SCIENCE

 • Introduction to AZURE ML studio
 • Data Pipeline
 • ML modeling with Azure

MODULE 8: INTRODUCTION TO DEEP LEARNING

 • Introduction to Artificial Neural Network, Architecture
 • Artificial Neural Network in Python
 • Introduction to Convolutional Neural Network, Architecture
 • Convolutional Neural Network in Python

MODULE 1: DATABASE INTRODUCTION

 • DATABASE Overview
 • Key concepts of database management
 • Relational Database Management System
 • CRUD operations

 MODULE 2: SQL BASICS

 • Introduction to Databases
 • Introduction to SQL
 • SQL Commands
 • MY SQL workbench installation

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
• Windows functions: Over, Partition , Rank 

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

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
 • Git Essentials: Copy & User Setup
 • Mastering Git and GitHub

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

MODULE 5: GIT WITH GITHUB AND BITBUCKET 

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

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

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

MODULE 1: TABLEAU FUNDAMENTALS 

 • Introduction to Business Intelligence & Introduction to Tableau
 • Interface Tour, Data visualization: Pie chart, Column chart, Bar chart.
 • Bar chart, Tree Map, Line Chart
 • Area chart, Combination Charts, Map
 • Dashboards creation, Quick Filters
 • Create Table Calculations
 • Create Calculated Fields
 • Create Custom Hierarchies

MODULE 2: POWER-BI BASICS 

 • Power BI Introduction 
 • Basics Visualizations
 • Dashboard Creation
 • Basic Data Cleaning
 • Basic DAX FUNCTION

MODULE 3 : DATA TRANSFORMATION TECHNIQUES

 • Exploring Query Editor
 • Data Cleansing and Manipulation:
 • Creating Our Initial Project File
 • Connecting to Our Data Source
 • Editing Rows
 • Changing Data Types
 • Replacing Values

MODULE 4 :  CONNECTING TO VARIOUS DATA SOURCES 

 • Connecting to a CSV File
 • Connecting to a Webpage
 • Extracting Characters
 • Splitting and Merging Columns
 • Creating Conditional Columns
 • Creating Columns from Examples
 • Create Data Model

MODULE 1: NEURAL NETWORKS 

 • Structure of neural networks
 • Neural network - core concepts(Weight initialization)
 • Neural network - core concepts(Optimizer)
 • Neural network - core concepts(Need of activation)
 • Neural network - core concepts(MSE & RMSE)
 • Feed forward algorithm
 • Backpropagation

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 - IMAGE RECOGNITION

• Convolutional neural networks (CNNs)
• CNNs with Keras-part1
• CNNs with Keras-part2
• Transfer learning in CNN
• Flowers dataset with tf2.X(part-1)
• Flowers dataset with tf2.X(part-2)
• Examining x-ray with CNN model

MODULE 4 : DEEP COMPUTER VISION - OBJECT DETECTION

 • What is Object detection
 • Methods of Object Detections
 • Metrics of Object detection
 • Bounding Box regression
 • labelimg
 • RCNN
 • Fast RCNN
 • Faster RCNN
 • SSD
 • YOLO Implementation
 • Object detection using cv2

MODULE 5: RECURRENT NEURAL NETWORK 

• RNN introduction
• Sequences with RNNs
• Long short-term memory networks(part 1)
• Long short-term memory networks(part 2)
• Bi-directional RNN and LSTM
• Examples of RNN applications

MODULE 6: NATURAL LANGUAGE PROCESSING (NLP)

• Introduction to Natural language processing
• Working with Text file
• Working with pdf file
• Introduction to regex
• Regex part 1
• Regex part 2
• Word Embedding
• RNN model creation
• Transformers and BERT
• Introduction to GPT (Generative Pre-trained Transformer)
• State of art NLP and projects

MODULE 7: PROMPT ENGINEERING

• Introduction to Prompt Engineering
• Understanding the Role of Prompts in AI Systems
• Design Principles for Effective Prompts
• Techniques for Generating and Optimizing Prompts
• Applications of Prompt Engineering in Natural Language Processing

MODULE 8: REINFORCEMENT LEARNING

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

MODULE 9: DEEP REINFORCEMENT LEARNING

• Architectures of deep Q learning
• Deep Q learning
• Reinforcement Learning Projects with OpenAI Gym

MODULE 10: Gen AI

• Gan introduction, Core Concepts, and Applications
• Core concepts of GAN
• GAN applications
• Building GAN model with TensorFlow 2.X
• Introduction to GPT (Generative Pre-trained Transformer)
• Building a Question answer bot with the models on Hugging Face

MODULE 11: Gen AI

• Introduction to Autoencoder
• Basic Structure and Components of Autoencoders
• Types of Autoencoders: Vanilla, Denoising, Variational, Sparse, and Convolutional Autoencoders
• Training Autoencoders: Loss Functions, Optimization Techniques
• Applications of Autoencoders: Dimensionality Reduction, Anomaly Detection, Image

OFFERED ARTIFICIAL INTELLIGENCE COURSES IN SURAT

ARTIFICIAL INTELLIGENCE TRAINING REVIEWS

ABOUT ARTIFICIAL INTELLIGENCE TRAINING IN SURAT

Artificial Intelligence (AI) has become a transformative force, revolutionizing industries worldwide. Surat, known as the Diamond City of India, is rapidly evolving into a prominent hub for AI innovation. With its burgeoning tech ecosystem, strategic business initiatives, and a culture of innovation, Surat provides a fertile ground for professionals and enterprises to thrive in the AI and machine learning (ML) domains.

DataMites is a globally recognized provider of AI and ML courses in Surat, offering comprehensive programs in Surat to empower individuals to excel in the rapidly evolving AI domain. With a curriculum centered on hands-on projects, real-world applications, and career-oriented support, DataMites has become the preferred choice for Artificial Intelligence Course in Surat with placement assistance to its students.

The Artificial Intelligence Engineer Course offered by DataMites, accredited by IABAC and NASSCOM FutureSkills, aligns with global industry standards. This 9-month, immersive training program is offered at an offline center in Surat, blending in-person instruction with practical learning. The course offers live projects, internships, and training designed to cater to both professionals and students. With dedicated placement support, participants acquire the skills and confidence needed to thrive in AI-driven industries.

Surat: A Rising Technology Hub

Surat has grown beyond its traditional reputation as an industrial hub for textiles and diamonds to become a key player in India's tech landscape. The city’s focus on smart city projects and digital transformation has led to a surge in technology adoption across sectors. Initiatives like the Surat Smart City Project have created a robust infrastructure that fosters innovation and AI-driven solutions, particularly in logistics, urban planning, and manufacturing.

Nearby cities like Ahmedabad and Vadodara further amplify Gujarat’s tech ecosystem. Ahmedabad, with its established IT parks and educational institutions, complements Surat’s growing demand for AI expertise. Vadodara, known for its strong industrial base, is also embracing AI in sectors such as engineering and healthcare. Together, these cities contribute to a dynamic regional landscape, positioning Gujarat as a rising technology powerhouse in India.

Why Choose Surat for Artificial Intelligence Training?

Surat, often hailed as the economic powerhouse of Gujarat, is rapidly transforming into a prominent hub for technology and innovation. With a strong emphasis on infrastructure development, a thriving industrial ecosystem, and forward-thinking government policies, Surat is becoming a prime destination for professionals and students seeking to build a career in Artificial Intelligence Course in Surat.

  1. Emerging Tech Ecosystem: Surat’s growing IT and startup landscape are driving the adoption of AI across industries. With the presence of innovative companies and tech parks, Surat provides a fertile ground for individuals to learn and apply AI in real-world scenarios.
  2. Industrial Diversity and AI Demand: The city’s industrial diversity, encompassing textiles, gems, jewelry, and manufacturing, creates a robust demand for AI-powered solutions to optimize operations and enhance productivity. According to Indeed, the average salary for AI Engineers in Surat is estimated at INR 5 lakhs per annum, according to industry reports.
  3. Affordable Cost of Living: Compared to larger metropolitan areas, Surat offers a more affordable cost of living without compromising the quality of education or opportunities. This makes it an attractive destination for students and professionals looking to enhance their skill set while enjoying a high quality of life.
  4. Global Connectivity: Surat’s strategic location and well-developed transportation networks ensure seamless connectivity with major cities in India and abroad. This accessibility makes it easier for students and professionals to pursue training and connect with broader industry networks.

In-Demand AI Roles and Skills in Surat

Surat's diverse industries and emerging tech ecosystem are driving the demand for skilled Artificial Intelligence Training in Surat. Here’s a look at the most sought-after AI roles and skills in the city:

  1. Machine Learning Engineer: Building, training, and deploying machine learning models.
  2. Data Scientist: Analyzing data, building predictive models, and delivering actionable insights.
  3. AI Research Scientist: Developing advanced AI algorithms and working on innovative projects like computer vision or NLP.
  4. AI Solutions Architect: Designing AI solutions tailored to business needs.
  5. Computer Vision Engineer: Developing AI applications for image and video analysis.

To succeed in these roles, professionals must develop key Artificial Intelligence skills, including proficiency in programming languages like Python or R, building machine learning models, working with neural networks, and utilizing tools such as TensorFlow and Keras. Expertise in big data frameworks like Hadoop and Spark, as well as experience with cloud platforms and AI ethics, can greatly enhance their competitive edge.

Moreover, strong soft skills, such as analytical thinking, problem-solving, and effective communication, are crucial for interpreting AI insights and presenting them clearly to stakeholders.

Why Choose DataMites for Artificial Intelligence Training in Surat?

  1. Global Recognition: Our Artificial Intelligence courses in Surat are backed by credentials accredited by prestigious organizations like IABAC and NASSCOM FutureSkills.
  2. Expert Faculty: Learn from industry-leading experts, including the esteemed AI specialist Ashok Veda, who provide valuable insights and practical, real-world knowledge.
  3. Flexible Learning Options: DataMites provides both online and on demand offline Artificial Intelligence courses in Surat, with a conveniently located offline center for easy accessibility.
  4. Practical Project and Internships: Our Artificial Intelligence Courses in Surat with internships, seamlessly combine academic learning with practical training.
  5. Placement Assistance: DataMites offers Artificial Intelligence courses in Surat with placement assistance, ensuring a seamless transition from education to employment.

Innovative 3-Phase Learning Methodology at DataMites

DataMites follows a well-organized 3-Phase Learning Methodology, designed to deliver an engaging and hands-on educational experience.

Phase 1: Pre-Course Self-Study

Students kickstart their learning journey with premium video tutorials and comprehensive study materials, establishing a strong foundation in artificial intelligence concepts.

Phase 2: Immersive Training

This phase involves 20 hours of training per week, spread over a duration of three months.Learners have the option to choose between live online sessions or offline artificial intelligence courses in Surat. The curriculum integrates hands-on projects, expert mentorship, and industry-relevant content, providing a well-rounded and immersive learning experience.

Phase 3: Internship & Placement Assistance

Students work on 20 capstone projects and a client project, earning an esteemed internship certification. DataMites' Placement Assistance Team (PAT) provides dedicated career support to help students secure job opportunities with top-tier companies.

Comprehensive Artificial Intelligence Curriculum

Our Artificial Intelligence Engineer Courses in Surat integrate the AI Expert and Certified Data Scientist (CDS) programs, offering a thorough and comprehensive education in artificial intelligence and data science. The Artificial Intelligence course curriculum encompasses a broad spectrum of topics, including:

  1. Python Foundation
  2. Data Science Foundations
  3. Machine Learning Expert
  4. Advanced Data Science
  5. Version Control with Git
  6. Big Data Foundation
  7. Certified BI Analyst
  8. Database: SQL and MongoDB
  9. Artificial Intelligence Foundation

This comprehensive approach ensures that students acquire the crucial knowledge and skills required to succeed in the fast-evolving and dynamic field of artificial intelligence.

Additional AI Certifications from DataMites

  1. Artificial Intelligence for Managers: A program tailored for business leaders, focusing on how to integrate AI into strategic decision-making and business operations.
  2. Certified NLP Expert: Specializing in Natural Language Processing, this course is ideal for those interested in AI's role in understanding and interpreting human language.
  3. Artificial Intelligence Expert: Designed for both beginners and intermediate data science professionals, this course provides a solid, career-driven foundation in AI.
  4. Artificial Intelligence Foundation: A beginner-friendly program that offers a comprehensive understanding of AI's core principles and concepts.

DataMites Artificial Intelligence Course Tools in Surat

In our Artificial Intelligence Certification in Surat, we provide comprehensive coverage of a wide array of AI tools, ensuring you gain the essential skills and expertise. These tools encompass:

  1. Anaconda
  2. Python
  3. Apache Pyspark
  4. Git
  5. Hadoop
  6. MySQL
  7. MongoDB
  8. Amazon SageMaker
  9. Google Bert
  10. Google Colab
  11. Advanced Excel
  12. Scikit Learn
  13. Azure Machine Learning
  14. Flask
  15. Apache Kafka
  16. Power BI
  17. GitHub
  18. Numpy
  19. TensorFlow
  20. Pandas
  21. Tableau
  22. Atlassian BitBucket
  23. Natural Language Toolkit
  24. PyCharm

Surat’s Transformation: Tradition Meets Technology

Surat, situated in the western state of Gujarat, is a dynamic city celebrated for its cultural heritage and flourishing business landscape. As a prominent commercial and economic center, Surat provides an ideal environment for both learning and career advancement. Surat's strategic location and robust infrastructure make it an ideal destination for individuals seeking quality Artificial Intelligence Institute in Surat and career prospects. Surat, with its forward-thinking approach, nurtures innovation and technological growth, creating a conducive environment for AI enthusiasts to thrive.

DataMites provides an in-depth Artificial Intelligence course for aspiring professionals who wish to earn certification in this groundbreaking digital technology. DataMites Artificial Intelligence training in Surat will allow professionals and entrepreneurs to dwell deep into AI. Experienced instructors lead the AI training sessions, offering valuable insights into real-world applications. Additionally, candidates receive hands-on experience, allowing them to tackle business challenges confidently by practicing extensively in a 24/7 accessible cloud lab.

Now is the perfect time to dive into the groundbreaking technology that is reshaping the digital landscape, guided by one of the leading training providers in the field. DataMites also provides training for Data Analytics, Machine Learning, Deep Learning, Python, IoT, Python for Data Science, and the entire Data Science courses.

ABOUT ARTIFICIAL INTELLIGENCE COURSE IN SURAT

Artificial Intelligence (AI) refers to the field of computer science that concentrates on the development of intelligent machines capable of performing tasks that typically require human intelligence, such as decision-making, problem-solving, understanding natural language, and recognizing visual patterns.

Advantages of artificial intelligence include the automation of repetitive tasks, improved efficiency and accuracy in data analysis, enhanced decision-making capabilities, and potential advancements in various industries such as healthcare and transportation. However, disadvantages can include job displacement due to automation, ethical concerns regarding privacy and bias, and the potential for AI systems to make errors or be vulnerable to malicious attacks.

Instances of artificial intelligence in daily life include virtual assistants like Siri, Alexa, and Google Assistant, which utilize voice recognition and natural language processing to provide information and carry out tasks. Another example is personalized recommendations on streaming platforms and e-commerce websites, where AI algorithms analyze user preferences to suggest relevant content or products.

Educational requirements for a career in artificial intelligence typically involve a strong background in computer science, mathematics, or related fields. A bachelor's or master's degree in computer science, AI, machine learning, or data science is often preferred. Proficiency in programming languages, algorithms, statistics, and machine learning concepts is also necessary.

Artificial intelligence is a broader concept that encompasses the development of intelligent systems capable of simulating human-like intelligence. On the other hand, machine learning is a subset of AI that focuses on algorithms and models allowing systems to learn from data and make predictions without explicit programming. In simpler terms, machine learning is a technique used to achieve AI.

Prominent companies known for hiring professionals in artificial intelligence roles include Google, Microsoft, Amazon, Facebook, IBM, Apple, and Tesla. However, there are also opportunities in various industries such as healthcare, finance, and manufacturing, where companies actively seek AI talent.

Typically, educational qualifications for a career in artificial intelligence include a bachelor's or master's degree in computer science, AI, machine learning, or related fields. A solid foundation in programming languages such as Python or Java, understanding of algorithms, statistics, and knowledge of machine learning and deep learning concepts are beneficial.

Starting a career in artificial intelligence with no prior experience can be achieved by self-studying through online courses or tutorials in AI and machine learning. Building an AI project portfolio, participating in Kaggle competitions, and gaining practical experience through internships or freelance work are valuable steps. Networking with professionals in the field and joining AI communities provide opportunities for mentorship and guidance.

The AI Expert Course in Surat is an advanced-level program focusing on advanced AI algorithms, cutting-edge research, emerging trends, and complex applications. It often includes specialized modules or tracks in areas such as 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 can be accomplished by gaining relevant skills and knowledge through self-study, online courses, or specialized AI training programs. Building a strong AI portfolio, participating in AI projects or competitions, and networking with professionals in the field facilitate connections and opportunities for career transition. Leveraging transferable skills from the previous field and seeking internships.

To prepare for AI job interviews and technical assessments, focus on developing a strong understanding of AI concepts, mastering programming languages and frameworks, practicing implementing AI algorithms and models, staying updated with the latest advancements, solving AI-related coding problems, preparing for technical interview questions, and enhancing communication skills.

The future prospects of AI in the job market are promising. AI is in high demand across industries, creating opportunities for AI engineers, data scientists, and AI researchers. As AI continues to advance, it is expected to transform existing job roles and create new ones, leading to a significant expansion of the job market.

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FAQ’S OF ARTIFICIAL INTELLIGENCE TRAINING IN SURAT

Individuals can acquire knowledge in Artificial Intelligence through various means, including 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 preferred choice for Artificial Intelligence courses in Surat due to its comprehensive curriculum, experienced instructors, practical approach, flexible learning options, and placement assistance. With a well-designed curriculum covering various AI concepts and techniques, participants gain hands-on experience through projects and real-world applications. The experienced instructors provide valuable guidance and support. Flexible learning options, including online and classroom training, cater to different schedules. Additionally, DataMites offers placement assistance to connect participants with job opportunities in the AI field.

DataMites provides certifications in Surat 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 Surat 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 both weekdays and weekends.

The AI Engineer Course offered by DataMites in Surat aims to equip individuals with the skills and knowledge necessary to become proficient AI engineers. This course covers various aspects of AI, including machine learning, deep learning, natural language processing, computer vision, and AI deployment techniques. The aim is to provide hands-on experience through practical projects and case studies, enabling participants to build AI models and deploy them in real-world scenarios.

The Certified NLP Expert course offered by DataMites in Surat focuses on Natural Language Processing (NLP) skills and applications. The course covers topics such as 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 fee for the Artificial Intelligence Training program at DataMites in Surat can vary depending on factors such as the specific course selected and the duration of the program. Generally, the fee for the Artificial Intelligence course in Surat falls within the range of INR 60,795 to INR 154,000.

The AI Foundation Course offered by DataMites in Surat provides a comprehensive introduction to AI. The course 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 AI Foundation Course aims to provide participants with a solid foundation in AI concepts and techniques, preparing them for further specialization or practical AI projects.

The AI for Managers Course provided by DataMites in Surat covers topics such as AI basics, machine learning, deep learning, natural language processing, computer vision, AI implementation challenges, ethical considerations, and AI project management. The course aims to provide managers with the necessary knowledge to make informed decisions regarding AI adoption and implementation.

Generally, anyone with an interest in pursuing a career in Artificial Intelligence can enroll in an Artificial Intelligence Certification Training in Surat. There are usually no strict prerequisites in terms of educational background or prior experience.

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

To ensure a smooth process for issuing the participation certificate and booking the certification exam, participants are required 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 Surat.

In case of inability to attend a session during the Artificial Intelligence training at DataMites in Surat, 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.

Yes, it is possible to attend a free demo class before enrolling in the Artificial Intelligence course at DataMites in Surat. The demo class serves as an introduction to the training program, allowing potential participants to get a glimpse of the content, teaching methodology, and overall learning experience. Attending a demo class helps individuals make an informed decision about whether to enroll in the course.

DataMites has a dedicated Placement Assistance Team (PAT) that provides placement facilities to candidates who successfully complete the Artificial Intelligence course. The PAT offers support 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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