- Lesson 01
- Outline & directions
- Lesson 02
- Fundamentals of Linear Algebra
- Introduction to Numpy
- Boolean Indexing with Numpy
- Case Study: NYC Taxi-Airport data
- Lesson 03
- Fundamentals of Machine Learning
- What is ML?
- Types
- Main challenges
- Test & Validating
- Lesson 04
- Look at the big picture
- Get the data
- Discover and visualize the data to gain insights
- Prepare the data for Machine Learning algorithms
- Select a model and train it
- Fine-tune your model
- Present your solution
- Lesson 07
- Univariate and Multivariate KNN
- Hyperparameter optimization
- Cross-Validation
- Pipeline & Gridsearch
- Lesson 09
- Linear regression
- Case study: housing price prediction
- Present the notion of a cost function
- Introduce the gradient descent method for learning.
- Refresher on linear algebra concepts.
- Lesson 11
- First competition
- Lesson 13
- Loglogistic regression
- Classification
- Binary Classification
- Decision Boundary
- Cost Function
- Multiclass Classification
- Regularization (L1, L2)
- Hands on Scikit-Learn
- Lesson 17
- Introduction to Decision Tree
- Converting categorical variables
- Splitting Data
- Decision Trees as flows of data
- Entropy & Gini
- Information gain
- Applying Decision Trees
- Overfitting problem
- Case study: classification problem
- Lesson 18
- Hypothesis test
- Significant test
- Chi-squared test
- Feature Selection for ML
- Univariate selection
- Recursive feature elimination
- Pipelines
- Hypothesis test
- Lesson 19
- Ensembles (introduction)
- Voting classifiers
- Bagging & Pasting
- Random Forest
- Feature Importante (XAI)
- Case Study
- Lesson 20
- Ensembles (cont.)
- Boosting
- Adaboost
- Gradient Boost
- XGBoost
- Lesson 21
- Getting Started with Kaggle
- Kaggle Workflow
- Lesson 22
- Clustering Basic
- K-Means
- Case study: senators votes, nba
- Lesson 23
- Representing neural network
- Nonlinear activation functions
- Hidden Layers
- Case study: build a handwritten digit classifier