Master Artificial Intelligence With Industry Experts
Industry-focused AI program covering Python, Machine Learning, Computer Vision, NLP, and Generative AI with 5 months of in-depth training + LIVE project mentoring, hands-on cloud lab practice, and industry certification.
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Blended Learning
Self Learning + Live Mentoring
- Self Learning + Live Mentoring
- NASSCOM & IBM & AAAI Certification
- 1 Year Access to E-learning
- 25 Capstone & 1 Client Project
- Job Assistance
- 24x7 Learner Assistance
Live Virtual
Instructor Led Live Online
- NASSCOM & IBM & AAAI Certification
- 7-Month | 700 Learning Hours
- 120-Hour Live Online Training
- 25 Capstone & 1 Client Project
- 365 Days Flexi Pass + Cloud Lab
- Placement Assurance
Classroom
In-Person Classroom Training
- NASSCOM & IBM & AAAI Certification
- 7-Month | 700 Learning Hours
- 120-Hour Live Online Training
- 25 Capstone & 1 Client Project
- Cloud Lab Access
- Placement Assurance
Tools & Technologies
Master Industry Tools
Hands-on training on the tools and technologies used by top data teams
worldwide.
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NASSCOM & IBM & AAAI Certification
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No Cost EMI & Scholarships Available
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Companies That Hire Our Graduates
200+ leading companies trust Data Sprint graduates to drive their data
initiatives.
Why Data Sprint
Why Thousands Choose Data Sprint
We’re committed to transforming careers with quality education and real-
world experience.
Course Syllabus
Comprehensive Data Science Curriculum
A structured learning path covering foundations to advanced topics across data science, machine learning, and AI.
Artificial Intelligence Foundation – 6 Modules
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
Python Foundation – 4 Modules
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
Statistics Essentials – 4 Modules
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
• Empirical 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
Machine Learning Associate – 9 Modules
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 Regresssion
• 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
Machine Learning Expert – 7 Modules
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
Advanced Data Science – 8 Modules
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 Review
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
Database: Sql And Mongodb – 7 Modules
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
• Self Join, Cross join
• Windows function: 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
Git – 5 Modules
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
Big Data Foundation – 3 Modules
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
Bi Analyst – 4 Modules
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
Artificial Intelligence(Ai) Expert – 11 Modules
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
• Markov decision process
• Fundamental equations in RL
• Model-based method
• Dynamic programming model free methods
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