AI Builder

AI Builder

Harness the immense potential of Data Science, Machine Learning, and Deep Learning, amalgamating their capabilities to engineer robust Artificial Intelligence solutions for practical, real-world applications in the comprehensive course titled 'Artificial Builder: Build an AI. Explore AI development, gain skills to create solutions for today's complex challenges in this comprehensive course.

Overview of artificial intelligence, its history, and its role in various industries.
Week 1: Overview of JavaScript, its history, and its role in web development.
Week 2: Setting up the development environment, installing Python, Jupyter notebook, and essential libraries

Python Basics for AI
Week 3: Variables, data types, and basic operators in Python
Week 4: Control flow structures: if statements, loops (for, while), and functions

Data Cleaning and Preprocessing for AI
Week 5: Data exploration, data cleaning, and handling missing values
Week 6: Data transformation, feature scaling, and encoding categorical variables.

Data Analysis and Visualization for AI
Week 7: Data visualization with Matplotlib, Seaborn, and Plotly
Week 8: Exploratory data analysis (EDA), statistical analysis, and correlation

Machine Learning Fundamentals
Week 9: Introduction to machine learning, supervised vs. unsupervised learning, and model evaluation.
Week 10: Regression analysis, linear regression, and evaluation metrics.

Classification and Model Selection
Week 11: Classification algorithms, logistic regression, decision trees, and random forests.
Week 12: Model selection and hyperparameter tuning, k-fold cross-validation, and overfitting prevention.

Deep Learning with TensorFlow and Keras
Week 13: Introduction to deep learning, artificial neural networks, and activation functions
Week 14: Building deep neural networks with TensorFlow and Keras, image classification, and text analysis.

Real-World AI Applications
Week 15: Natural Language Processing (NLP), sentiment analysis, and chatbots
Week 16: Computer vision, image recognition, and real-world AI project development.

Advanced AI Topics
Week 17: Reinforcement learning, recommendation systems, and ensemble methods
Week 18: Deploying AI models, model interpretation, and ethics in AI.

Final AI Project and Course Review
Week 19: Students work on comprehensive AI projects that incorporate various concepts learned throughout the course
Week 20: Project continuation, model deployment, and sharing insights, course review, and certification.

  • Weekly coding exercises, quizzes, and assignments.
  • Mid-term AI project to apply concepts learned in the first half of the course.
  • Final AI project assessment that demonstrates proficiency in AI development.
  • Peer code reviews for collaborative learning.
Note: Encourage students to work on personal AI projects and contribute to open-source AI projects to gain practical experience. Stay up-to-date with the latest trends and developments in artificial intelligence to ensure the syllabus remains relevant.