Master Machine Learning With Industry Experts
Comprehensive Machine Learning program covering Python/R, key ML algorithms, and real-world model deployment with intensive 6-day / 3-weekend classroom or LVC training, plus 3 months of LIVE project mentoring and unlimited access to the Data Science Cloud Lab for hands-on practice.
Choose Your Learning Path
Flexible Plans Designed to Fit Your Schedule & Learning Style
Select the learning mode that works best for you — self-paced, live online, or in-person classroom training
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.
Certification
NASSCOM & IBM & AAAI Certification
Flexible Financing Options
Pay in Easy Installments at 0% Interest
We’re dedicated to making our programs accessible. No hidden costs, transparent pricing
EMI Available
ShopSe
No Cost EMI & Scholarships Available
Our Partners
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.
Machine Learning Introduction
Module 1: Machine Learning Introduction: Supervised and Unsupervised Learning
• Linear Regression Theory
• Linear Regression Programming with R
• Working on Case Study
Module 2: Multiple Linear Regression
• Theory behind multiple linear regression
• Multiple Linear Regression with R
• Working on Case Study
Module 3: Decision Tree
• Theory Behind Decision Tree
• Decision Tree with R
• Working on Case Study
Module 4: Naive Bayes
• Theory behind Naïve Bayes classifiers
• Naive Bayes Classifiers with R
• Working on Case Study
Module 5: Support Vector Machines
• Theory behind Support Vector Machines
• Support vector machines with R
• Improving the performance with Kernals
• Working on Case Study
Module 6: Association Rule
• Theory behind Association Rule
• Working on Case Studies
Module 7: Neural Net
• Artificial Neural Network
• Connection Weights in Neural Network
• Generating Neural Network with R
• Improving Neural Network Accuracy with Hidden Layers
• Working on Case
Module 8: Random Forest
• Theory behind Random Forest
• Random Forest with R
• Improving performance of Random Forest
• Working on Case Study
Module 9: Recommendation Engine
• Theory behind Recommendation Engines
• Working on Case Study with R
Module 10: Dimension Reduction
• Theory behind Recommendation Engine
• Working on Case Studies
Machine Learning Algorithms
• Popular Machine Learning Algorithms
• Clustering, Classification and Regression
• Supervised vs Unsupervised Learning
• Choice of Machine Learning
Supervised Learning
• Simple and Multiple Linear Regression
• KNN etc…
Linear Regression and Logistic Regression
• Theory of Linear Regression
• Hands on with use Cases
K-Nearest Neighbour (KNN)
Decision Tree
Naïve Bayes Classifier
• Naïve Bayes for text classification
• New Articles Tagging
Unsupervised Learning
• K-means Clustering
Advanced Machine Learning Concepts
• Tuning with Hyper Parameters
• Popular ML Algorithms
• Clustering, Classification and Regression
• Supervised vs Unsupervised
• Choice of ML Algorithm
Random Forest – Ensemble
• Ensemble Theory
• Random Forest Tuning
Support Vector Machine (SVM)
• Simple and Multiple Linear regression
• KNN
Natural Language Processing (NLP)
• Text Processing with Vectorization
• Sentiment analysis with TextBlob
• Twitter sentiment analysis.
Artificial Neural Network (ANN)
• Basic ANN network for regression and classification
Tensorflow overview and Deep Learning Intro
• Tensorflow work flow demo and intro to deep learning
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