Machine Learning & Data Analytics for IT
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Understand AI Fundamentals
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Identify Business Opportunities
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Plan & Execute Projects
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Evaluate Impact & Ethics
60 Hours
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Beginner level
No Prior Knowledge Required
Flexible Learning
Available in Online, Offline, and
Hybrid Modes
60 Hours
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Program Overview
A master’s-level / intensive training program targeted at IT professionals (or aspirants) that teaches Machine Learning and Data Analytics end-to-end: from data prep and statistical foundations, through advanced ML / deep learning, to tools & platforms for analyzing big data. Emphasis on hands-on projects, assignments, real-world case studies. (Based in Bangalore for many cohorts.)
Key Features
200+ hours of interactive learning
6+ Projects and 40+ Assignments
Lifetime access to content & resources
Flexible batches (weekdays, weekends, timing options)
24×7 support / doubt resolution
Tools & platforms exposure: PySpark, Python, Deep Learning, Data Visualization etc
Skills Covered
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Python programming & libraries (NumPy, Pandas etc.)
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Statistics & Data Preparation / Cleaning / Visualization
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Supervised & Unsupervised Learning; ML Algorithms (decision trees, ensembles, etc.)
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Deep Learning, Neural Networks (e.g. CNNs, RNNs)
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Natural Language Processing, Sequence Learning, Image Processing / Computer Vision
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Big Data tools (Spark MLlib)
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Data Analytics & Visualization (insights, dashboards)
Course Curriculum
Programming Foundations for Data Science & ML
Module 1 • 10 hour to complete
Module 1 • 10 hour to complete
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Python programming essentials
• Libraries: NumPy, Pandas, Matplotlib, Seaborn
• Data cleaning, transformation, and preprocessing
• Libraries: NumPy, Pandas, Matplotlib, Seaborn
• Data cleaning, transformation, and preprocessing
Statistics & Data Analytics
Module 2 • 10 hour to complete
Module 2 • 10 hour to complete
•
Probability & statistics for ML
• Hypothesis testing & significance analysis
• Exploratory Data Analysis (EDA)
• Data visualization & dashboarding techniques
• Hypothesis testing & significance analysis
• Exploratory Data Analysis (EDA)
• Data visualization & dashboarding techniques
Machine Learning Fundamentals
Module 3 • 10 hour to complete
Module 3 • 10 hour to complete
•
Supervised learning (linear regression, logistic regression, decision trees)
• Unsupervised learning (clustering, PCA, dimensionality reduction)
• Ensemble methods (Random Forest, XGBoost, Gradient Boosting)
• Model evaluation & validation
• Unsupervised learning (clustering, PCA, dimensionality reduction)
• Ensemble methods (Random Forest, XGBoost, Gradient Boosting)
• Model evaluation & validation
Advanced ML & Big Data Analytics
Module 4 • 10 hour to complete
Module 4 • 10 hour to complete
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Handling large datasets with Spark & MLlib
• Distributed training using PySpark
• Feature engineering for big data systems
• Case studies in enterprise-scale analytics
• Distributed training using PySpark
• Feature engineering for big data systems
• Case studies in enterprise-scale analytics
Deep Learning & Neural Networks
Module 5 • 10 hour to complete
Module 5 • 10 hour to complete
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Neural network basics (perceptrons, activation functions)
• CNNs for image analysis
• RNNs, LSTMs, and GRUs for sequence data
• Applications in vision, speech, and language tasks
• CNNs for image analysis
• RNNs, LSTMs, and GRUs for sequence data
• Applications in vision, speech, and language tasks
Natural Language Processing & Advanced Topics
Module 6 • 10 hour to complete
Module 6 • 10 hour to complete
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Text preprocessing & embeddings (Word2Vec, GloVe, Transformers)
• Sentiment analysis, text classification
• Sequence-to-sequence models for text generation
• Computer vision tasks: detection, recognition, segmentation
• Sentiment analysis, text classification
• Sequence-to-sequence models for text generation
• Computer vision tasks: detection, recognition, segmentation
Data Analytics & Visualization Tools
Module 7 • 10 hour to complete
Module 7 • 10 hour to complete
•
Building dashboards with Python, Tableau, or Power BI
• Business insights from data analytics
• Case studies across domains: retail, healthcare, IT operations
• Business insights from data analytics
• Case studies across domains: retail, healthcare, IT operations
Capstone Project
Module 8 • 10 hour to complete
Module 8 • 10 hour to complete
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End-to-end project integrating ML, DL, and analytics techniques
• Examples:
• Human action recognition via pose estimation (video + keypoint detection)
• Predictive modeling for key business metrics
• Image & text classification or generative AI project
• Big data analysis with Spark + visualization dashboard
• Examples:
• Human action recognition via pose estimation (video + keypoint detection)
• Predictive modeling for key business metrics
• Image & text classification or generative AI project
• Big data analysis with Spark + visualization dashboard
Tools & Technologies
Completion Certificate
Job Role
- Machine Learning Engineer
- Data Scientist
- Deep Learning Engineer / Computer Vision Engineer
- Machine Learning Engineer
- Data Analyst / Analytics Engineer
- Big Data Engineer (with Spark / MLlib)
Training Option
OFFLINE
₹7777
- 24/7 support
- 24/7 support
- 24/7 support
ONLINE
₹5555
- 24/7 support
- 24/7 support
- 24/7 support
HYBRID
₹6666
- 24/7 support
- 24/7 support
- 24/7 support
Why Join this Program
Earn a job
Receive complete job assistance tailored to your career goals. Get expert placement guidance to confidently step into the industry.
Leverage knowledge from industry experts
Learn directly from seasoned Trainers and Gain real-world insights that go beyond textbooks.
Industry-relevant Tools & Practical Learning
Get hands-on experience with the latest tools used by top companies. Hands-on learning through 200+ exercises and 10+ projects with seamless access to integrated labs.
Structured, industry-vetted curriculum
A curriculum shaped by experts to meet evolving industry demands. Structured learning ensures you're career-ready from day one.
Integrated with Gen AI Modules
The curriculum includes cutting-edge Generative AI modules designed to align with emerging tech trends.
Interview preparation & Placement assistance
Sharpen your interview skills with practical training and expert guidance. Receive complete placement support to connect with top recruiters.
FAQ
Basic Python is enough; we cover everything from scratch.
No — this course teaches ML fundamentals too.
Absolutely — this is designed for career transition.
You’ll need to put in extra effort, but many succeed.
Yes — the curriculum includes real-world LLM integration.
Yes — including techniques used by AI professionals.