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.

Artificial Intelligence Engineer Course by DataMites: Fee, Duration, How to Apply

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

DATA SCIENCE FOUNDATION

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

MODULE 3 : 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

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 FOUNDATION

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

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

Particulars	Amount in INR	Discount Fee in INR
Virtual Live session	Rs.154,000	Rs.101,745
Blended learning (Self-learning + Live)	Rs.92,000	Rs.60,795
Classroom in person	Rs.154,000	Rs.116,445

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Artificial Intelligence Engineer Course

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DATA SCIENCE FOUNDATION

MODULE 1 : DATA SCIENCE COURSE INTRODUCTION

MODULE 2 : DATA SCIENCE ESSENTIALS

MODULE 3 : DATA SCIENCE DEMO

MODULE 4 : ANALYTICS CLASSIFICATION

MODULE 5 : DATA SCIENCE AND RELATED FIELDS

MODULE 6 : DATA SCIENCE ROLES & WORKFLOW

MODULE 7 : MACHINE LEARNING INTRODUCTION

MODULE 8 : DATA SCIENCE INDUSTRY APPLICATIONS

PYTHON FOUNDATION

MODULE 1 : PYTHON BASICS

MODULE 2 : PYTHON CONTROL STATEMENTS

MODULE 3 : PYTHON DATA STRUCTURES

MODULE 4 : PYTHON FUNCTIONS

MODULE 5 : PYTHON NUMPY PACKAGE

MODULE 6 : PYTHON PANDAS PACKAGE

STATISTICS ESSENTIALS

MODULE 1 : OVERVIEW OF STATISTICS

MODULE 2 : HARNESSING DATA

MODULE 3 : EXPLORATORY DATA ANALYSIS

MODULE 4 : HYPOTHESIS TESTING

MODULE 5 : CORRELATION AND REGRESSION

MACHINE LEARNING ASSOCIATE

MODULE 1: MACHINE LEARNING INTRODUCTION

MODULE 2: PYTHON NUMPY & PANDAS PACKAGE

MODULE 3: VISUALIZATION WITH PYTHON

MODULE 4: ML ALGO: LINEAR REGRESSSION

MODULE 5: ML ALGO: KNN

MODULE 6: ML ALGO: LOGISTIC REGRESSION

MODULE 7: PRINCIPLE COMPONENT ANALYSIS (PCA)

MODULE 8: ML ALGO: K MEANS CLUSTERING

MACHINE LEARNING EXPERT

MODULE 1: MACHINE LEARNING INTRODUCTION

MODULE 2: ML ALGO: LINEAR REGRESSION

MODULE 3: ML ALGO: LOGISTIC REGRESSION

MODULE 4: ML ALGO: KNN

MODULE 5: ML ALGO: K MEANS CLUSTERING

MODULE 6: PRINCIPLE COMPONENT ANALYSIS (PCA)

MODULE 7: ML ALGO: DECISION TREE

MODULE 8 : ML ALGO: NAÏVE BAYES

MODULE 9: GRADIENT BOOSTING, XGBOOST

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

MODULE 11: ARTIFICIAL NEURAL NETWORK (ANN)

MODULE 12: ADVANCED ML CONCEPTS

ADVANCED DATA SCIENCE

MODULE 1: TIME SERIES FORECASTING - ARIMA

MODULE 2: FEATURE ENGINEERING

MODULE 3: SENTIMENT ANALYSIS

MODULE 4: REGULAR EXPRESSIONS WITH PYTHON

MODULE 5: ML MODEL DEPLOYMENT WITH FLASK

MODULE 6: ADVANCED DATA ANALYSIS WITH MS EXCEL

MODULE 7: AWS CLOUD FOR DATA SCIENCE

MODULE 8: AZURE FOR DATA SCIENCE

DATABASE: SQL AND MONGODB

MODULE 1: DATABASE INTRODUCTION

MODULE 2: SQL BASICS

MODULE 3: DATA TYPES AND CONSTRAINTS

MODULE 4: DATABASES AND TABLES (MySQL)

MODULE 5: SQL JOINS

MODULE 6: SQL COMMANDS AND CLAUSES

MODULE 7: DOCUMENT DB/NO-SQL DB

VERSION CONTROL WITH GI

MODULE 1: GIT INTRODUCTION