ARTIFICIAL INTELLIGENCE CERTIFICATION AUTHORITIES

Artificial Intelligence Course Features

ARTIFICIAL INTELLIGENCE LEAD MENTORS

ARTIFICIAL INTELLIGENCE COURSE FEE IN GWALIOR

Live Virtual

Instructor Led Live Online

154,000
99,323

  • 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
59,348

  • 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
113,673

  • 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 GWALIOR

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 GWALIOR

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 GWALIOR

ARTIFICIAL INTELLIGENCE TRAINING REVIEWS

ABOUT ARTIFICIAL INTELLIGENCE TRAINING IN GWALIOR

AI has the power to enhance productivity and efficiency in businesses. By automating repetitive tasks and analyzing large datasets, AI can help organizations streamline their operations. In fact, studies suggest that AI technologies have the potential to increase labor productivity by up to 40%, leading to significant cost savings and improved outcomes.

DataMites offers an extensive Artificial Intelligence Course in Gwalior, providing students with a comprehensive learning experience. This 9-month course spans over 780 learning hours, ensuring in-depth coverage of the subject. The course includes 100 hours of live online/classroom training, allowing students to interact with instructors and fellow learners in real-time. Additionally, students will engage in 10 Capstone projects and 1 Client project, giving them hands-on experience in applying AI concepts.

To further enhance the learning journey, DataMites provides a 365 Days Flexi Pass, allowing students to access course materials and resources at their convenience. The course also includes access to a Cloud Lab, facilitating practical exercises and experimentation.

DataMites also offers offline courses on demand in Gwalior, catering to individuals who prefer face-to-face learning experiences. These courses include:

  • Artificial Intelligence Engineer

  • Artificial Intelligence Expert

  • Certified NLP Expert

  • Artificial Intelligence Foundation

  • Artificial Intelligence for Managers

Now, let's explore 10 reasons why you should choose DataMites for Artificial Intelligence Training in Gwalior:

Ashok Veda and Faculty: DataMites boasts experienced instructors like Ashok Veda, who bring their expertise and industry knowledge to the classroom.

Comprehensive Course Curriculum: The course curriculum covers a wide range of AI topics, ensuring a thorough understanding of the subject.

Global Certification: DataMites offers certifications recognized globally, including IABAC, NASSCOM FutureSkills Prime, and JainX.

Flexible Learning: DataMites provides flexible learning options including online artificial intelligence courses in Gwalior and ON DEMAND artificial intelligence classroom training in Gwalior, allowing students to balance their studies with other commitments.

Projects with Real-World Data: Students work on projects involving real-world data, enabling practical application of AI concepts.

Internship Opportunity: DataMites offers artificial intelligence internship opportunities, providing valuable industry experience and exposure.

Placement Assistance and Job References: The institute assists students with artificial intelligence courses with placement, providing job references and connecting them with potential employers.

Hardcopy Learning Materials and Books: Students receive hardcopy learning materials and books, facilitating offline study and reference.

DataMites Exclusive Learning Community: Students become part of a dedicated learning community, fostering collaboration and knowledge sharing.

Affordable Pricing and Scholarships: DataMites offers competitive pricing for their courses and provides scholarships to eligible students.

Gwalior, located in the Indian state of Madhya Pradesh, is a historic city known for its rich cultural heritage and architectural marvels. The city offers a conducive environment for individuals aspiring to pursue Artificial Intelligence Certification in Gwalior, with ample opportunities for learning and professional growth.

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 Gwalior.

ABOUT ARTIFICIAL INTELLIGENCE COURSE IN GWALIOR

The term "Artificial Intelligence (AI)" refers to the development of intelligent machines that can perform tasks requiring human intelligence. It involves creating algorithms and systems capable of learning, reasoning, perceiving, and making decisions.

Pioneers and contributors in the field of AI include Alan Turing, John McCarthy, Marvin Minsky, and Arthur Samuel. However, AI has evolved through the collaborative efforts of many researchers and scientists over time.

Transitioning into an AI career from a different field involves:

  • Building a foundation in math, computer science, and programming.
  • Acquiring knowledge of AI concepts, algorithms, and technologies.
  • Learning programming languages like Python or R commonly used in AI.
  • Mastering machine learning and deep learning techniques.
  • Creating a portfolio of AI projects to showcase practical skills.
  • Staying updated with the latest advancements in AI research.

Yes, a career as an AI engineer is promising and rewarding. The demand for AI professionals is growing as organizations adopt AI technologies. AI engineers work on cutting-edge projects, solve complex problems, and contribute to technological advancements. The field offers competitive salaries and continuous learning opportunities.

A career in AI typically requires a bachelor's or master's degree in computer science, AI, data science, or a related field. Proficiency in programming languages like Python or Java, understanding of mathematics including linear algebra and calculus, and familiarity with machine learning algorithms and neural networks are essential.

The AI Engineer Course provides a comprehensive understanding of AI concepts, algorithms, and technologies. It covers topics such as machine learning, deep learning, natural language processing, computer vision, and AI deployment techniques. The curriculum includes hands-on projects to develop practical skills in building and deploying AI models.

To delve into AI in Gwalior, a basic understanding of programming concepts, familiarity with mathematics, and a curiosity to learn and explore AI technologies are helpful. It is advisable to check DataMites' specific prerequisites or recommended knowledge for their AI courses in Gwalior.

The AI For Managers Course covers AI basics, machine learning, deep learning, natural language processing, computer vision, AI implementation challenges, ethical considerations, and AI project management. It equips managers with the knowledge to make informed decisions regarding AI adoption and implementation.

Python is widely considered the best language for AI. It offers simplicity, extensive libraries like TensorFlow and PyTorch, and an active community. Python's versatility makes it suitable for various AI tasks, including machine learning, natural language processing, and computer vision.

Specific AI job roles include AI Engineer/Developer, Data Scientist, Machine Learning Engineer, AI Research Scientist, AI Consultant, AI Project Manager, and AI Ethicist. These roles involve various responsibilities such as designing and implementing AI solutions, analyzing data, conducting research, and managing AI projects while considering ethical implications.

To pursue a career as an AI engineer, follow these steps:

  • Acquire a strong foundation in mathematics, computer science, and programming.
  • Gain knowledge of AI concepts, algorithms, and technologies.
  • Learn programming languages commonly used in AI, such as Python or R.
  • Master machine learning and deep learning techniques.
  • Build a portfolio showcasing practical AI skills through projects.
  • Stay updated with the latest advancements and research in AI.

AI and ML are closely related but serve different purposes. AI is a broader field focused on creating intelligent machines that can simulate human intelligence. ML, a subset of AI, involves training machines to learn from data and make predictions. The choice depends on your interests and career goals. AI offers diverse applications, while ML provides specific techniques for data analysis and pattern recognition.

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FAQ’S OF ARTIFICIAL INTELLIGENCE TRAINING IN GWALIOR

Learning Artificial Intelligence in Gwalior is significant due to the increasing demand for AI professionals across industries. It equips individuals with skills that are highly sought after in the job market, opens up career opportunities, and allows them to contribute to technological advancements using AI.

Obtaining an Artificial Intelligence Certification in Gwalior is important as it validates one's knowledge and skills in AI. It enhances job prospects, adds credibility to their profile, and demonstrates a commitment to continuous learning and professional development in the field.

DataMites is a preferred choice for AI courses in Gwalior due to the following reasons:

Experienced Faculty: DataMites has industry practitioners and subject matter experts as instructors, providing valuable insights and practical knowledge.

Practical Approach: The courses focus on hands-on learning with projects and case studies, enabling students to apply their knowledge in real-world scenarios.

Industry-Relevant Curriculum: The curriculum is designed based on industry requirements, covering the latest advancements in AI and ensuring students gain relevant skills.

Placement Assistance: DataMites offers placement assistance to help students connect with job opportunities in the AI field.

Eligibility criteria for enrolling in an Artificial Intelligence Certification Training in Gwalior may vary. Generally, individuals with backgrounds in computer science, engineering, mathematics, or related fields are eligible.

DataMites offers various certifications in 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 provided by DataMites in Gwalior varies depending on the specific course selected. It can range from one month to one year, with training sessions available on both weekdays and weekends to accommodate different schedules and preferences.

Individuals can acquire knowledge in the field of Artificial Intelligence through self-study using online resources, textbooks, research papers, and tutorials. They can also enroll in AI courses and training programs, pursue degree or diploma programs in AI or related fields, attend workshops and conferences, and engage in practical projects and competitions to gain hands-on experience.

The AI Engineer Course in Gwalior offered by DataMites aims to equip individuals with the skills and knowledge required to become proficient AI engineers. It covers various aspects of AI, including machine learning, deep learning, natural language processing, computer vision, and AI deployment techniques, preparing participants to build and deploy AI models in real-world scenarios.

The Certified NLP Expert course in Gwalior offered by DataMites focuses on Natural Language Processing (NLP), a subfield of AI. The course covers fundamental NLP concepts, text preprocessing, sentiment analysis, named entity recognition, topic modeling, language generation, and neural network-based NLP models. It trains individuals in NLP techniques and their applications to solve real-world problems.

The Artificial Intelligence for Managers Course in Gwalior offered by DataMites covers AI basics, machine learning, deep learning, natural language processing, computer vision, AI implementation challenges, ethical considerations, and AI project management. It provides managers with the knowledge necessary to make informed decisions regarding AI adoption, implementation, and leveraging AI technologies for business growth.

The AI Foundation Course in Gwalior at DataMites provides a comprehensive introduction to AI. It covers the basics of AI, machine learning, and deep learning. The course content includes an overview of AI, supervised and unsupervised learning, neural networks, deep learning algorithms, model evaluation, and deployment techniques. It lays a strong foundation in AI concepts and techniques.

DataMites offers a range of certifications in Artificial Intelligence, including prestigious certifications from renowned organizations like IABAC, JAINx, and NASSCOM FutureSkills Prime. Completing the Artificial Intelligence training at DataMites provides participants with the opportunity to earn these esteemed certifications, enhancing their professional credibility and marketability in the AI field. These certifications validate their skills and knowledge, making them stand out in the competitive job market.

The Flexi-Pass feature available at DataMites allows participants to attend training sessions at their convenience. It offers flexibility in scheduling, allowing participants to choose from multiple batch options based on their availability. This feature ensures individuals can balance their learning with other commitments and attend classes as per their preferences.

The fee for the Artificial Intelligence Training program at DataMites in Gwalior may vary based on factors such as the chosen course and its duration. Generally, the fee ranges from INR 60,795 to INR 154,000.

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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