Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. Machine Learning technology has widely changed the lifestyle of a human beings as we are highly dependent on this technology. It is the subset of Artificial Intelligence, and we all are using this either knowingly or unknowingly. For example, we use Google Assistant that employs ML concepts, we take help from online customer support, which is also an example of machine learning, and many more.
1. Speech & Image Recognition
2. Ads Recommendation
3. Self-driving cars
4. Auto-Friend Suggestion in FB
5. Email Spam filter
6. Google Translation
7. ChatBot and many more.....
Machine learning is also often referred to as predictive analytics, or predictive modelling.machine learning uses programmed algorithms that receive and analyse input data to predict output values within an acceptable range. As new data is fed to these algorithms, they learn and optimise their operations to improve performance, developing ‘intelligence’ over time.
- Supervised Machine Learning.
- Un Supervised Machine Learning.
- Reinforcement Learning.
In supervised learning, the machine is taught by example. The operator provides the machine learning algorithm with a known dataset that includes desired inputs and outputs, and the algorithm must find a method to determine how to arrive at those inputs and outputs. While the operator knows the correct answers to the problem, the algorithm identifies patterns in data, learns from observations and makes predictions. The algorithm makes predictions and is corrected by the operator and this process continues until the algorithm achieves a high level of accuracy/performance. Regression and Classification are two categories in supervised machine learning.
1. Simple Linear Regression
2. Multiple Linear Regression
3. Polynomial Regression
4. Support Vector Regression (SVR)
5. Decision Tree Regression
6. Random Forest Regression
1. Logistic Regression
2. K Nearest Neighbours(KNN)
3. Supper Vactor Machines(SVM)
4. Decision Tree Classification
5. Naive Bayes Classification
6. Random Forest Classification
7. XG Boost
Unsupervised learning refers to the use of artificial intelligence (AI) algorithms to identify patterns in data sets containing data points that are neither classified nor labeled. The algorithms are thus allowed to classify, label and/or group the data points contained within the data sets without having any external guidance in performing that task.In other words, unsupervised learning allows the system to identify patterns within data sets on its own.Unsupervised learning algorithms can perform more complex processing tasks than supervised learning systems.However, unsupervised learning can be more unpredictable than a supervised learning model.
1. K-Means Clustering
2. Hierarchical Clustering
3. overlapping clustering
Implementation of ML algorithms from scratch using python, numpy and pandas. It will be good if reader has some python, mathematics knowledge