Our Artificial Intelligence And Data Science Syllabus is designed to provide a clear understanding of key concepts in Artificial Intelligence and Data Science. Our AI and Data Science syllabus includes important topics like machine learning, deep learning, data processing, and big data tools. The AI DS course syllabus ensures students gain practical experience with industry-standard tools and algorithms. This syllabus of artificial intelligence and data science helps learners develop the skills needed to solve real-world problems and succeed in the fast-growing tech field.
Artificial Intelligence And Data Science Syllabus
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Syllabus for The Artificial Intelligence And Data Science Course
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Introduction to AI and Data Science
1
- Overview of Artificial Intelligence and Data Science
- Key applications and industries
- Role of AI in modern data processing
Mathematics for AI and Data Science
2
- Linear Algebra
- Probability and Statistics
- Calculus for Machine Learning
Data Preprocessing and Analysis
3
- Data cleaning and transformation techniques
- Exploratory Data Analysis (EDA)
- Feature engineering and selection
Machine Learning Algorithms
4
- Supervised Learning: Regression, Classification
- Unsupervised Learning: Clustering, Dimensionality Reduction
- Ensemble Methods and Model Evaluation
Deep Learning
5
- Neural Networks and Backpropagation
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs) and LSTMs
- Generative Adversarial Networks (GANs)
Natural Language Processing (NLP)
5
- Text preprocessing and tokenization
- Sentiment analysis and text classification
- Word embeddings (Word2Vec, GloVe)
- Transformers and BERT
Big Data Technologies
7
- Hadoop, Spark, and MapReduce
- Distributed data processing
- Cloud computing and AI integration
AI in Real-World Applications
8
- Computer Vision and Object Detection
- Speech Recognition and NLP in applications
- AI in healthcare, finance, and robotics
AI Ethics and Responsible AI
9
- Bias in AI models
- Fairness, transparency, and accountability
- Ethical considerations in AI development
Tools and Frameworks
10
- Python, R for Data Science
- TensorFlow, Keras, PyTorch for deep learning
- Scikit-learn, Pandas, and Matplotlib for data analysis
Capstone Project
11
- Real-world problem-solving
- Applying AI and Data Science techniques
- Project presentation and portfolio development
Breakdown of Artificial Intelligence And Data Science Course Fee and Batches
Hands On Training
3-5 Real Time Projects
60-100 Practical Assignments
3+ Assessments / Mock Interviews
June 2025
Week days
(Mon-Fri)
Online/Offline
2 Hours Real Time Interactive Technical Training
1 Hour Aptitude
1 Hour Communication & Soft Skills
(Suitable for Fresh Jobseekers / Non IT to IT transition)
Course Fee
June 2025
Week ends
(Sat-Sun)
Online/Offline
4 Hours Real Time Interactive Technical Training
(Suitable for working IT Professionals)
Course Fee
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