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AICourse Overview
In this AI, Machine Learning (ML), and Data Science course, you will explore the key concepts and techniques that drive modern data-driven decision-making. Designed for individuals seeking to enter or advance in the field, this course covers foundational topics in statistics, data analysis, and programming, as well as advanced machine learning algorithms.
You will gain hands-on experience with popular tools and frameworks, including Python, TensorFlow, and Scikit-learn, enabling you to build, train, and evaluate models. The curriculum emphasizes practical applications through real-world projects, preparing you for the challenges faced in the industry.
With our 100% job guarantee, you will finish the course with the skills and confidence needed to excel in AI and data science roles.
Course Content
- Introduction to Python
- Installing Python
- setting up the development environment
- Python syntax and basic data types
- operators
- Conditional statements
- List, tuple and dictionary
- Loops
- Functions
- File handling
- Object-Oriented Programming (OOPS) concepts
- Introduction of Data science
- Required Python
- Numpy
- pandas for data analysis
- Matplotlib & Seaborn for data visualization
- Machine Learning Fundamentals
- Supervised, Unsupervised, Reinforcement Learning
- Regressions (linear, Logistic, Polynomial, Evaluation metrics )
- Decision Tree & Random Forest
- Naïve bayes
- Support Vector Machines
- Clustering (K-Means, Hierarchical, Evaluation metrics)
- PrinModel Deployment and Conclusion
- TensorFlow and Keras
- Artificial Neural Network (ANN)
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- DataBase (Mysql, MongoDB)
- pySpark
- Real-World Applications and Case Studies
- Introduction to Cloud Platforms (AWS, Azure, Google Cloud)
- Introduction to Artificial Intelligence: Overview of AI concepts, history, and applications.
- Mathematics for AI: Understanding linear algebra, calculus, and statistics relevant to AI.
- Deep Learning: Exploring neural networks, CNNs, RNNs, and their applications in various domains.
- Computer Vision: Understanding image processing and object detection algorithms.
- AI Ethics and Bias: Discussing ethical considerations and the impact of bias in AI systems.
- Real-World Applications: Case studies of AI in industries such as healthcare, finance, and automotive.
- Final Project: Developing and presenting a comprehensive AI project that demonstrates learned skills.