Week 1: Introduction to Python
- Python basics: Syntax, Variables, Operators, Control flow, Functions
- Data structures in Python: Lists, Tuples, Dictionaries, Sets
- Intermediate Python: List comprehensions, Lambda functions, Modules, Error handling
Week 2: Python for Data Analysis
- Numpy: Arrays, Array operations, Random module, Numpy functions
- Pandas: DataFrames, Series, Reading from various data sources, Data cleaning, Data wrangling
- Data Visualization: Matplotlib, Seaborn
Week 3: Introduction to Machine Learning
- What is AI? Differences between AI, Machine Learning, and Deep Learning
- Supervised Learning vs Unsupervised Learning
- Overview of common Machine Learning algorithms: Linear Regression, Logistic Regression, Decision Trees, Random Forest, SVM, KNN, k-means, Hierarchical Clustering
- Introduction to Scikit-Learn
Week 4: Supervised Learning with Python
- Linear Regression in Scikit-Learn
- Logistic Regression in Scikit-Learn
- Decision Trees and Random Forest in Scikit-Learn
- Support Vector Machines in Scikit-Learn
- Model evaluation: Accuracy, Precision, Recall, F1 Score, ROC AUC Score
Week 5: Unsupervised Learning with Python
- k-means Clustering in Scikit-Learn
- Hierarchical Clustering in Scikit-Learn
- Principal Component Analysis in Scikit-Learn
- Model evaluation for unsupervised learning: Silhouette score, Inertia
Week 6: Introduction to Neural Networks and Deep Learning
- What are Neural Networks?
- Perceptrons, Multi-Layer Perceptrons
- Activation functions: Sigmoid, Tanh, ReLU
- Backpropagation and Gradient Descent
- Introduction to TensorFlow and Keras
Week 7: Deep Learning with Python
- Building a Neural Network with TensorFlow and Keras
- Convolutional Neural Networks
- Recurrent Neural Networks
- Model evaluation and tuning: Overfitting, Underfitting, Regularization, Dropout
Week 8: Natural Language Processing
- Text processing: Tokenization, Stemming, Lemmatization, Stop words removal
- Bag of Words, TF-IDF
- Sentiment Analysis
- Introduction to Transformers, Attention, and BERT
Week 9: Reinforcement Learning
- Introduction to Reinforcement Learning: The RL Framework, Exploration vs Exploitation
- Q-Learning
- Implementing a simple Reinforcement Learning model with OpenAI’s Gym
Week 10: Ethics in AI and Future Trends
- Bias in AI
- Explainability and Interpretability of AI models
- Privacy concerns in AI
- Overview of current trends in AI: GPT-3, AutoML, Transfer learning
Throughout the course:
- Assignments: To encourage hands-on practice
- Quizzes: To test understanding and recall
- Project: Towards the end of the course, a project where students apply what they’ve learned to a real-world problem
This curriculum assumes a basic knowledge of high school math (mainly algebra, probability, and a bit of calculus), but you can also include a quick math review in the first or second week if necessary. Python is taught from scratch, but any previous coding experience will be helpful. The course ends with a look at ethics, which is increasingly important as AI technology becomes more prevalent.