Course 1: Introduction to Machine Learning
Basics of Machine Learning and Motivation : A first approach to machine learning. We’ll go over the main motivations, the main kind of algorithms, what they can be used for…
Course 2: Supervised Machine Learning
a. Statistical inference
Linear Regression (Part 1): We’ll explore the simple framework of OLS and multi-dimensional regression.
Linear Regression (Part 2): Random design matrix, Normal regression, Pseudo Least Squares and other extensions…
The Logistic Regression: One of the fundamentals algorithms for classification.
b. Core algorithms
The Bayes Classifier: At the core of any algorithm, the Bayes Classifier is considered as one of the first algorithm to master.
Support Vector Machine: Todo
Linear Discriminant Analysis (LDA) and QDA : Intuition behind LDA, when it should be used, and the maths behind it. We’ll also quick cover the Quadratic version of LDA.
c. Bagging Methods
d. Boosting Methods
Adaptative Boosting (AdaBoost) : A clear approach of boosting algorithms and adaptative boosting with illustrations. When should we use boosting ? What are the foundations of the algorithm ?
Gradient Boosting (Regression): The basics of gradient boosting regression, and implementation of a high level version in Python.
Gradient Boosting (Classification): Gradient boosting classification as an extension of the Regression.
e. Time Series
Introduction to Time Series : A first approach to exploring a time series in Python with open data.
Key Concepts in Time Series : Stationarity, ergodicity… We’ll cover the key concepts of time series.
Basics of Time Series Forecasting : How do we make a series stationary ? How do we forecast ?
Time Series Forecasting with Facebook Prophet : Explore time series forecasting using the Prophet open-source package.
Handle missing values in Time Series : A quick illustration of backward filling and forward filling.
f. Recommmendation Systems
Content-based Filtering : Todo
Colaborative Filtering : Todo
Course 3: Optimization and tuning
GridSearch vs. RandomizedSearch : When it comes to parameter selection, you usually encounter 2 main solutions. GridSearch and RandomizedSearch. What is the main difference between these 2 techniques ? What are the pros and cons of each technique ?
Bayesian Hyperparameter Optimisation (HyperOpt) : Bayesian Hyperparameter Optimization is a great alternative to GridSearch and RandomizedSearch. How does it work ? How do you implement it in Python ?
AutoML with h2o : The interest in AutoML is rising over time. AutoML algorithms are reaching really good rankings in data science competitions. But what is AutoML ? How does it work ? When to use it ? And how can you implement an AutoML pipeline in Python ?
Machine Learning Explainability : We’ll cover permutation importance, partial dependence plots and SHAP Values to better explain the outputs of a ML model.
Course 4: Unsupervised Machine Learning
Clustering algorithms: Todo
Reducing dimension: Todo
All codes and exercises are accessible on this repo. Don’t hesitate to show your suppot and star the repo:
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