Softlogic Systems Artificial Intelligence and Data Science Course Syllabus is specifically designed for College Students, Freshers, and Job Seekers. Our Artificial Intelligence and Data Science syllabus covers Python for AI, data preprocessing, statistical analysis, predictive modeling, and hands-on AI projects. Our Artificial Intelligence and Data Science Course Content helps you learn Artificial Intelligence and Data Science step by Step with real-time projects and Interview Preparations.
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
- Overview of Artificial Intelligence and Data Science
- Key applications and industries
- Role of AI in modern data processing
Mathematics for AI and Data Science
- Linear Algebra
- Probability and Statistics
- Calculus for Machine Learning
Data Preprocessing and Analysis
- Data cleaning and transformation techniques
- Exploratory Data Analysis (EDA)
- Feature engineering and selection
Machine Learning Algorithms
- Supervised Learning: Regression, Classification
- Unsupervised Learning: Clustering, Dimensionality Reduction
- Ensemble Methods and Model Evaluation
Deep Learning
- Neural Networks and Backpropagation
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs) and LSTMs
- Generative Adversarial Networks (GANs)
Natural Language Processing (NLP)
- Text preprocessing and tokenization
- Sentiment analysis and text classification
- Word embeddings (Word2Vec, GloVe)
- Transformers and BERT
Big Data Technologies
- Hadoop, Spark, and MapReduce
- Distributed data processing
- Cloud computing and AI integration
AI in Real-World Applications
- Computer Vision and Object Detection
- Speech Recognition and NLP in applications
- AI in healthcare, finance, and robotics
AI Ethics and Responsible AI
- Bias in AI models
- Fairness, transparency, and accountability
- Ethical considerations in AI development
Tools and Frameworks
- Python, R for Data Science
- TensorFlow, Keras, PyTorch for deep learning
- Scikit-learn, Pandas, and Matplotlib for data analysis
Capstone Project
- Real-world problem-solving
- Applying AI and Data Science techniques
- Project presentation and portfolio development
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