ARTIFICIAL INTELLIGENCE ENGINEER COURSE

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: 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

The Artificial Engineer course offers the knowledge and skills that are required to become a successful AI Engineer. Precisely, the course talks about the ways to find a solution to complex problems, by employing deep learning, machine learning, computer vision, and NLP methods.

The Artificial Intelligence Engineer course helps to get a better understanding of Statistics for Data Science, Machine Learning, Deep Learning, Programming Such as Python and Tensor Flow.

Some of the prerequisites for pursuing the Artificial Engineer course are:-

• Knowledge of basic statistical tools and techniques.
• Basics of programming languages such as Python
• Problem-solving skills.
• Curious mind

• Graduates/Freshers who wish to make a career in Artificial Intelligence.
• Professionals in the field of analytics, who wish to make a career switch to AI.
• Senior Analysts and Team Leaders.

Artificial Intelligence has gained importance across sectors and is instrumental in bringing change. Medical, Banking and Finance, Manufacturing, Retail, Hospitality, every sector of the economy uses AI. This can be attributed to the problem-solving capability in AI. Pursuing the Artificial Intelligence for Engineers course

• Helps to find solutions to complex real-world problems by using Machine Learning and Deep Learning techniques.
• Take important decisions in critical areas.
• Maximise revenue and Goodwill of the company.

DataMites  is an authorized institute by the International Association of Business Analytics Certifications (IABAC™) providing global data science certification courses

• An authorized institute by the International Association of Business Analytics Certifications (IABAC™) providing global data science certification courses
• Founded by Data Science/Analytics Experts, who have deep roots in providing management consulting to major companies in India, Europe and USA.
• Delivered 20+ sessions for Senior Executives of tier-1 companies in India including, CTS, TVS Group, Rane Group etc.,
• In collaboration with CII - Confederation of Indian Industry, conducted workshops for Senior executives in India.
• DataMites has in-house faculties who are from elite universities, IIM’s and PH.Ds in Data Science. 

Artificial intelligence (AI) refers to the use of computers and technology to mimic human intelligence and perform tasks that typically require human cognitive abilities, such as problem-solving and decision-making.

The term "artificial intelligence" was first introduced by John McCarthy, a renowned computer science professor at Stanford University.

• Robotics: AI-powered robots that can perform tasks and interact with their environment.
• Healthcare: AI systems used for disease diagnosis, personalized treatment, and medical image analysis.
• Data Security: AI algorithms that detect and prevent cyber threats and protect sensitive information.
• Gaming: AI opponents and game characters that exhibit intelligent behavior and adapt to player actions.
• Finance: AI algorithms used for fraud detection, risk assessment, and automated trading.
• Digital Media, Social Media: AI-powered recommendation systems and content analysis.
• Travel: AI chatbots for customer support and AI-powered travel planning.
• Automotive Industry: AI-enabled self-driving cars and advanced driver assistance systems.
• Customer Service: AI chatbots and virtual assistants for customer interaction.
• Facial Recognition: AI algorithms for biometric identification and authentication.

Learning Artificial Intelligence offers numerous benefits as it is a rapidly growing field with vast potential. AI technologies like intelligent voice assistants, self-driving cars, and robotic process automation have gained significant prominence. The demand for AI professionals is high, and it opens doors to lucrative career opportunities.

The trainers for the Artificial Intelligence Engineer course have experienced Data Scientists, AI and Machine Learning experts who possess good knowledge of the subject matter.

No. Coding is not required to learn Artificial Intelligence Engineer courses.

You can enrol for the Artificial Intelligence Engineer Course by visiting our website, and doing the payment through Debit/Credit card, Visa. The receipt for the payment done will be sent to your registered E-mail id.

Yes. You will be given a certificate after the completion of the Artificial Intelligence Engineer course.

DataMites offers IABAC Certification, NASSCOM Certification, a Course Completion Certificate, and an Internship Certificate for its Artificial Intelligence for Engineers program. Learners also receive an Experience Letter upon completing the internship.

This course is perfectly aligned to the current industry requirements and gives exposure to all the latest techniques and tools. The course curriculum is designed by specialists in this field and monitored and improved by industry practitioners on a continual basis.

No, the exam fees are already included in the course fee and you will not be charged extra.

Course fee needs to be paid in one payment as it is required to block your seat for the entire course as well as book the certification exams with IABAC™. In case, if you have any specific constraints, your relation manager at DataMites™ shall assist you with part payment agreements

DataMites™ has a dedicated Placement Assistance Team(PAT), who work with candidates on an individual basis in assisting for the right Data Science job.

You get a 100% refund training fee if the training is not to your satisfaction but the exam fee will not be refunded as we pay to accreditation bodies. If the refund is due to your availability concerns, you may need to talk to the relationship manager and will be sorted out on case to case basis