This is a **list of topics** which can be covered as part of** machine learning course curriculum**. In other words, it is a representation of **outline** of a **machine learning course**. This **course outline** is created by taking into considerations different topics which are covered as part of machine learning courses available on Coursera.org, Edx, Udemy etc. In case, you are planning to take up a machine learning course in near future, make sure that most of the following is covered.

### An Outline to Machine Learning Course Curriculum

- Introduction to machine learning
- Regression
- Linear Regression with One Variable
- Linear Regression with Multiple Variables

- Logistic Regression
- Introduction to Neural Networks
- Representation
- Learning

- Support Vector Machines (SVM)
- Unsupervised learning (Clustering and Retrieval)
- Dimensionality reduction: Dimensionality reduction is the process of reducing the number of random variables under consideration[1] by obtaining a set of principal variables. Following topics needs to be covered:
- Feature selection
- Feature extraction: Introduction to different techniques such as some of the following for transforming data in high-dimensional space to a space of fewer dimensions.
- Principal Component Analysis (PCA)
- Kernal PCA
- Graph based kernel PCA
- Linear discriminant analysis (LDA)
- Generalized discriminant analysis (GDA)

- Anomaly detection (also termed as Outlier detection)
- Unsupervised anomaly detection
- Supervised anomaly detection
- Semi-supervised anomaly detection

- Recommender systems
- Collaborative filtering
- Content-based filtering
- Hybrid recommender systems

- Machine Learning Examples/Case Studies

The following is a list of concepts which would be good to understand before one gets started with learning **machine learning** concepts.

- Introduction to Linear Algebra
- Regularization
- Introduction to probability and statistics

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