Introduction To
Machine Learning is a sub-area of artificial intelligence, whereby the term refers to the ability of IT systems to independently find solutions to problems by recognizing patterns in databases. In other words: Machine Learning enables IT systems to recognize patterns on the basis of existing algorithms and data sets and to develop adequate solution concepts. Therefore, in Machine Learning, artificial knowledge is generated on the basis of experience.
In a way, Machine Learning works in a similar way to human learning. For example, if a child is shown images with specific objects on them, they can learn to identify and differentiate between them. Machine Learning works in the same way: Through data input and certain commands, the computer is enabled to "learn" to identify certain objects (persons, objects, etc.) and to distinguish between them.
The system can perform the following tasks by Machine Learning:
1. Finding, extracting and summarizing relevant data
2. Making predictions based on the analysis data
3. Calculating probabilities for specific results
4. Adapting to certain developments autonomously
5. Optimizing processes based on recognized patterns
Course Structure
Introduction
- Getting started with Machine Learning
- Artificial Intelligence | An Introduction
- ML | What is Machine Learning ?
- An introduction to Machine Learning
- ML | Introduction to Data in Machine Learning
- Demystifying Machine Learning
- Machine Learning – Applications
- Machine Learning and Artificial Intelligence
- Difference between Machine learning and Artificial Intelligence
- Agents in Artificial Intelligence
Supervised and Unsupervised learning
Parametric Methods
- Regression and Classification | Supervised Machine Learning
- Understanding Logistic Regression
- Multivariate Regression
- Confusion Matrix in Machine Learning
- Linear Regression (Python Implementation)
- Softmax Regression using TensorFlow
- Linear Regression using PyTorch
- Identifying handwritten digits using Logistic Regression in PyTorch
Dimensionality Reduction
Clustering
Non-parametric Methods
Multilayer perceptron
Hidden Markov Model
Data Processing
Misc
ML using Python
- Introduction To Machine Learning using Python
- Learning Model Building in Scikit-learn : A Python Machine Learning Library
- Multiclass classification using scikit-learn
- Classifying data using Support Vector Machines(SVMs) in Python
- Classifying data using Support Vector Machines(SVMs) in R
- Phyllotaxis pattern in Python | A unit of Algorithmic Botany
- How to get synonyms/antonyms from NLTK WordNet in Python?
- Removing stop words with NLTK in Python
- Tokenize text using NLTK in python