About This Course
This comprehensive program is designed to provide you with a solid foundation in Artificial Intelligence and Machine Learning. Whether you're a complete beginner or have some programming experience, this course will equip you with the skills to understand and implement AI algorithms.
You'll start with Python fundamentals and progressively move to more advanced topics like deep learning and natural language processing. By the end of the course, you'll have built several AI projects and gained practical experience that you can apply to real-world problems.
Learning Outcomes
- Understand the fundamentals of AI, ML, and Deep Learning
- Master Python programming for data science
- Perform data wrangling and visualization using NumPy, Pandas, and Matplotlib
- Implement machine learning algorithms using Scikit-learn
- Build and evaluate regression and classification models
- Understand the basics of Neural Networks and their applications
- Apply AI concepts to solve real-world problems
- Develop a portfolio project showcasing your AI skills
Prerequisites
This course is designed for beginners, but some familiarity with programming concepts will be helpful. No prior experience with AI or Python is required.
- Basic computer literacy
- High school level mathematics (algebra, basic statistics)
- Problem-solving mindset and eagerness to learn
Curriculum Breakdown
Week 1: AI & Python Fundamentals
Topics Covered:
- Defining AI, ML, DL, examples, brief history & types
- Ethical considerations in AI (introduction)
- Setting up Python (Anaconda) & Jupyter/VS Code
- Basic Python syntax: variables, data types, lists, dictionaries, control flow
E-Tivities:
- Introductory videos/articles on AI
- Python environment setup
- Beginner Python tutorials focusing on syntax and data structures
- Practice writing simple Python scripts
Week 2: Data Wrangling & Visualization
Topics Covered:
- NumPy: creating arrays, basic operations
- Pandas: Series, DataFrames, loading CSV
- Basic indexing & filtering
- Introduction to Matplotlib/Seaborn for visualization
E-Tivities:
- NumPy tutorials
- Practice loading and manipulating CSVs with Pandas
- Create basic visualizations from DataFrames
Week 3: Core ML Concepts & Regression
Topics Covered:
- What is Machine Learning? Supervised vs. Unsupervised learning
- Train/test split
- What is Regression? Linear Regression (conceptual)
- Introduction to Scikit-learn
- Training a simple Linear Regression model
- Evaluation: MAE/MSE (conceptual)
E-Tivities:
- Articles/videos on ML fundamentals and train/test split
- Implement Linear Regression with Scikit-learn on a simple dataset
- Interpret predictions and basic error metrics
Week 4: Classification & Unsupervised Learning Basics
Topics Covered:
- What is Classification? Logistic Regression (conceptual)
- KNN (conceptual)
- Training a simple classification model with Scikit-learn
- Evaluation: Accuracy, Confusion Matrix (conceptual)
- Introduction to Unsupervised Learning
- Clustering & K-Means (conceptual)
- Implementing K-Means with Scikit-learn (basic)
E-Tivities:
- Implement a classification algorithm using Scikit-learn
- Interpret accuracy and confusion matrix components
- Run K-Means on a simple dataset and visualize results
Week 5: Deep Learning & NLP Introduction
Topics Covered:
- What are Neural Networks (basic idea, layers)?
- Problems they solve well (images, text)
- Mention TensorFlow & PyTorch
- Brief intro to NLP: working with text
- Concept of Bag-of-Words
E-Tivities:
- Introductory videos on Neural Networks
- Explore examples of NLP tasks
- Focus on conceptual understanding
Week 6: Review, Applications & Next Steps
Topics Covered:
- Recap of key AI/ML/DL concepts
- Supervised/unsupervised learning, algorithms
- Real-world AI applications
- Ethics reminder
- Where to learn more (DL frameworks, specific ML, NLP, CV)
E-Tivities:
- Review notes
- Explore AI applications
- Think about potential simple projects
- Research further learning resources
Certificate Information
Upon successful completion of the course, you will receive an IgniteSkillz Certificate in Artificial Intelligence Foundations. This certification demonstrates your proficiency in AI fundamentals and can be shared on your resume and LinkedIn profile.
Certificate Benefits:
- Industry-recognized credential
- Verification links for employers
- LinkedIn integration