Machine Learning Syllabus

Machine learning is defined as the use of computational algorithms and statistics to learn from data without being explicitly programmed. It is a subset of artificial intelligence. In this article, I will take you through the complete Syllabus of Machine Learning that you should learn to start working on machine learning projects.

Machine Learning Syllabus

Below are all the main topics that contribute to the syllabus of machine learning:

1. Introduction to Machine Learning
2. Supervised Learning
3. Unsupervised Learning
4. Feature Engineering
5. Model Evaluation
6. Pipelines
7. Working with Text Data

Now let’s go through all the above topics of the Machine Learning Syllabus briefly.

Introduction to Machine Learning

Before starting with any course you should know what it is and what is the scope. So to start with Machine Learning you should know what is machine learning and what problems machine learning can solve. So below are the topics of an Introduction to machine learning that must be in the syllabus of machine learning:

1. What is Machine Learning?
2. Why we need Machine Learning?
3. Problems Machine Learning can solve
4. Types of Data you deal with

Supervised Learning

Like machine learning is a subset of Artificial Intelligence, the same way Supervised Learning is the subset of Machine Learning. Supervised Machine Learning includes solving problems of classification and regression. Below are the topics of Supervised Learning that must be covered in the syllabus of Machine Learning:

1. What is Supervised Learning?
2. Classification
3. Regression
4. Supervised Machine Learning Algorithms

Unsupervised Learning

Unsupervised Learning is another subset of Machine Learning. Unsupervised learning is used to solve problems of clustering and dimensionality reduction. Below are the topics of Unsupervised Learning that must be covered in the syllabus of Machine Learning:

1. What is Unsupervised Learning?
2. Preprocessing and Scaling Datasets
3. Dimensionality Reduction
4. Feature Extraction
5. Manifold Learning
6. Clustering
7. DBSCAN

Feature Engineering

Functionality engineering is about using knowledge of the domain of the dataset to create or extract those features that make machine learning algorithms work with great accuracy. Below are the topics of feature engineering that must be covered in the syllabus of Machine Learning:

1. Categorical Features
2. One-hot-encoding
3. Binning and Discretization
4. Linear Models and Trees
5. Interactions and Polynomials
6. Univariate Nonlinear Transformations
7. Feature Selection

Model Evaluation

It is important to choose the right features and parameters for your machine learning algorithms. This help to evaluate the performance of machine learning models and to select better parameters. This is known as the Model Evaluation. Below are the topics of model evaluation that must be covered in the syllabus of Machine Learning:

1. Overfitting and Underfitting
2. Cross-Validation
3. Grid Search
4. Evaluation Metrics
5. Model Selection
6. Hyperparameter Tuning

Pipelines

A machine learning pipeline is used to help automate a machine learning process. They work by allowing a sequence of data to be transformed and correlated together into a model that can be tested and evaluated to achieve a result, whether positive or negative. Below are the topics of pipelines that must be covered in Machine Learning:

1. Parameter Selection
2. Building Pipelines
3. Using Pipelines in Grid Search

Working with Text Data

Working with textual data in machine learning falls within the realm of natural language processing, which is a field devoted to algorithms and methods of processing human languages for computers. Below are the topics of working with text data that must be covered in Machine Learning:

1. Types of Textual Data
2. Analyzing Sentiments
3. Bag of words
4. Stopwords
5. Tf-Idf
6. Tokenization
7. Stemming
8. Lemmatization
9. Topic Modelling
10. Document Clustering

I hope you liked this article on the complete syllabus of Machine Learning that you need to follow to start working on projects. Feel free to ask your valuable questions in the comments section below.