Artificial Intelligence Engineer Course

BY
DataMites

Learn artificial intelligence along with industrial requirements with the Artificial Intelligence Engineer Course by DataMites.

Mode

Online

Duration

9 Months

Fees

₹ 57900 92000

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study, Virtual Classroom +1 more
Mode of Delivery Video and Text Based

Course overview

The Artificial Intelligence Engineer Course certification course is an advanced-level programme offered by DataMites. Overall, the course includes 120+ hours of live sessions and lectures along with the internship with live AI projects that a candidate can do after attaining the Artificial Intelligence Engineer Course certification. The candidate will get access to all programme offerings after enrolling in the course. 

They can access this material for the next 1-year post completion of Artificial Intelligence Engineer Course training. The course is of the comprehensive level that will be taught according to the industry requirement. The structure of the Artificial Intelligence Engineer Course certification syllabus will go under the industrial support and requirement only. To make it easy for candidates, the programme is available in three different modes namely, Blended learning, Classroom training and Live virtual. 

The highlights

  • Three different modes of learning 
  • Live virtual training 
  • Blended learning 
  • Classroom training
  • Capstone project 
  • Cloud lab access 
  • Accreditation by IABAC 
  • Course offered by DataMites

Program offerings

  • Reading material
  • Live sessions
  • Live lectures
  • Mentorship
  • Live projects
  • Blended
  • Classroom
  • Capstone project
  • Cloud lab
  • Internship
  • Videos
  • Data sets
  • Cheat sheets
  • Study material
  • Newsletters.

Course and certificate fees

Fees information
₹ 57,900  ₹92,000
  • There are different options available for the Artificial Intelligence Engineer Course fees according to the package training and schedule the candidate is choosing.

 Artificial Intelligence Engineer Course fees 

Particulars

Amount in INR

Discount Fee in INR

Virtual Live session

Rs.154,000

Rs.81,900

Blended learning (Self-learning + Live)

Rs.92,000

Rs. 57,900

Classroom in person

Rs.154,000

Rs. 86,900
certificate availability

Yes

certificate providing authority

IABAC

Who it is for

Following candidates can apply in the Artificial Intelligence Engineer Course programme - 

  • Team leaders
  • Senior analysts
  • The ones who are freshers or graduates and want to make a career in artificial intelligence should opt for the course. 
  • Analytics professionals willing to switch to AI

Eligibility criteria

Work experience 

No work experience is asked to mention while enrolling on the Artificial Intelligence Engineer Course by DataMites. 

Education 

As an educational prerequisite, the candidate needs to have basic knowledge of statistical tools and techniques, and a basic programming language such as Python to enroll in the Artificial Intelligence Engineer Course certification course. 

Certification qualifying details 

The candidate will get the Artificial Intelligence Engineer Course certification once after completing the course. 

What you will learn

Natural language processing Machine learning Knowledge of artificial intelligence

After the completion of the Artificial Intelligence Engineer Course programme, the candidate will learn the following skills and elements - 

  • The course begins with the fundamentals that an artificial intelligence field requires. 
  • The candidate will learn the elements of machine learning that are essential for the process. 
  • Enrolled students were easily able to implement the core learning algorithms. 
  • The complete training will be provided for the learning of natural language processing. 
  • Reinforcement skills must help the candidate in making their development better. 
  • The course will give a better understanding of the statistics for data science, deep learning, and machine learning. 

The syllabus

ARTIFICIAL INTELLIGENCE FOUNDATION

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
MODULE 3 : 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: 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

PYTHON FOUNDATION

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

STATISTICS ESSENTIALS

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

MACHINE LEARNING ASSOCIATE

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

MACHINE LEARNING EXPERT

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

ADVANCED DATA SCIENCE

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

DATABASE: SQL AND MONGODB

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

VERSION CONTROL WITH GI

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

BIG DATA FOUNDATION

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

CERTIFIED BI ANALYST

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

ARTIFICIAL INTELLIGENCE(AI) EXPERT

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

Admission details

Follow the steps to enroll in the Artificial Intelligence Engineer Course online course - 

Step 1: Visit the official site of the course.

Step 2: Tap on the option of “enquire now” of the particular plan with which the candidate is going. 

Step 3: After clearing the query, make the payment for the same, the candidate can make the payment through any of the options - a debit or credit card. 

Step 4: Afterward the candidate will receive the receipt of the payment on their registered mail. 

Step 5: The registration is successfully done. 

How it helps

The core Artificial Intelligence Engineer Course certification benefits are that after course completion, the candidate will get internship opportunities for working on live AI projects that will help the students to gain professional experience in the field at the same time. The candidate will get certification accredited by IABAC that holds high value in the industrial requirement nowadays that will help the candidate in his or her upcoming future. The candidate will achieve placement support after the completion of the course through the DataMites Placement Team only. The candidate can avail of different Artificial Intelligence Engineer Course certification benefits after completion of the course. 

FAQs

Is programming knowledge essential as a prerequisite for the course?

The candidates must have prior knowledge and experience in any basic programming language such as Python. 

What is the admission procedure for the course?

First, a candidate needs to make the payment for the course, thereafter they’ll receive a receipt for the payment on the registered mail and the registration is done under the course.

Will the candidate get certification in the end?

Yes, the candidate will get the Artificial Intelligence Engineer Course certification at the end only after completing the course.

After the course, will the candidate get career assistance?

Yes, placement support and career assistance are provided to the candidates. There is a dedicated placement and career mentoring team at DataMites that ensures to impart every support possible for candidates to appear for their placements. 

Can the candidate get a refund after canceling the admission?

Yes, the total tuition fee is refundable if the candidate cancels the admission within the period. But the exam fee is not refunded. 

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