Course Overview
NumPy – Use in AI is an 8-week intensive course designed for M.Tech, M.Sc, and MCA students, as well as E0 & E1 level professionals. This course introduces participants to the powerful NumPy library, covering essential techniques for efficient numerical computations in AI. It emphasizes operations on large arrays and matrices, which are crucial for machine learning and data analysis tasks.
Course Goals
The course aims to equip participants with a strong foundational understanding of NumPy, focusing on its critical role in data handling and numerical computation for AI applications. By mastering NumPy, participants will enhance their ability to process large datasets and improve AI model performance.
Program Objectives
- Mastering NumPy: Gain a comprehensive understanding of NumPy’s functionalities and its applications in AI.
- Efficient Data Handling: Develop skills to handle and process large datasets efficiently using NumPy.
- AI Integration: Learn to integrate NumPy with AI models and other Python libraries for robust AI solutions.
Program Structure
- Module 1: Introduction to NumPy
- Basics of NumPy and setting up the environment
- Understanding NumPy arrays, data types, and array manipulations
- Module 2: NumPy in Data Handling
- Advanced techniques in indexing, slicing, reshaping, and transposing arrays
- Statistical analysis capabilities of NumPy for AI applications
- Module 3: NumPy for Machine Learning
- Utilizing NumPy in feature engineering for AI models
- Improving computation efficiency with vectorization and broadcasting
- Module 4: NumPy with Other Libraries
- Integrating NumPy with Pandas for data analysis
- Visualizing data with Matplotlib
- Practical applications in building neural networks and processing image and audio data
- Module 5: Real-World AI Applications Using NumPy
- Implementing NumPy in various AI-driven projects
- Scaling AI solutions with NumPy for real-world applications
Eligibility
- Students: M.Tech, M.Sc, MCA students specializing in AI, data science, or related fields.
- Professionals: E0 & E1 level professionals in IT, data science, and other related areas looking to improve their numerical computing and AI skills.
Learning Outcomes
- Numerical Expertise: Gain advanced knowledge and practical experience with NumPy’s powerful array functions.
- AI Model Development: Learn to apply NumPy in developing and optimizing AI models for better performance.
- Industry Preparedness: Be ready to tackle real-world data challenges using advanced numerical and AI techniques.
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