Master Deep Learning Tensorflow With Industry Experts
Deep Learning program covering Neural Networks using TensorFlow, TensorBoard, and key DL concepts with 4-day intensive classroom/LVC training, 3 months LIVE project mentoring, and unlimited cloud lab access 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.
Module 1: Introduction To Deep Learning
• What is a neural network?
• Supervised Learning with Neural Networks – Python
• How Deep Learning is different from Machine Learning
Module 2: Overview of Machine Learning Concepts
• What is Machine Learning?
• Supervised Machine Learning algorithms
• K-Nearest Neighbors (KNN) concept and application
• Naive Bayes concept and application
• Logistic Regression concept and application
• Classification Trees concept and application
• Unsupervised Machine Learning algorithms
• Clustering with K-means concept and application
• Hierarchial Clustering concept and application
Module 3: TensorFlow Essentials
• Representing tensors
• Creating operators and excuting with sessions
• Introduction Jupyter notebook for TensorFlow coding
• TensorFlow variables
• Visualizing data using TensorBoard
Module 4: ML Algorithm - Linear Regression In TensorFlow
• Regression problems
• Linear regression applications
• Regularization
• Available datasets
• Coding Linear Regression with TensorFlow – Case study
Module 5: Deep Neural Networks In TensorFlow
• Basic Neural Nets
• Single Hidden Layer Model
• Multiple Hidden Layer Model
Module 6: Convolutional Neural Networks
• Introduction to Convolutional Neural Networks
• Input Pipeline
• Introduction to RNN, LSTM, GRU
Module 7: Reinforcement Learning In Tensorflow
• Concept of Reinforcement Learning
• Simple model applying Reinforcement Learning in TensorFlow
Module 8: Hands on Deep Learning Application With TensorFlow
• Example Application – Case study
• Hands on building the Deep Learning application with TensorFlow
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