Machine learning ( ML) is the analysis of machine algorithms that automated experimentation improves. The artificial intelligence is used as a branch. To order to render forecasts or calculations without complex scripting, machine learning constructs a statistical model dependent on experimental data known as "training" data. Machine learning is directly linked to quantitative statistics, which rely on forecasting using a PC.
Arthur Samuel, an American IBMer and a leader of computer games and artificial intelligence, invented the word "Machine computing" of 1959.
In this learn series, you'll learn the following:
- Introduction to Python
- Setting up Anaconda on Windows
- Python libraries for Machine Learning: Numpy
- Python libraries for Machine Learning: Pandas
- Python libraries for Machine Learning: Scikit-Learn
- Python libraries for Machine Learning: Matplotlib
- Python libraries for Machine Learning: Seaborn
- Python libraries for Machine Learning: Tensorflow
- Statistics: Introduction
- Statistics: Measure of Central Tendency
- Basics of Data Science
- Machine Learning: Introduction
- Machine Learning: Linear Regression
- Machine Learning: Logistic Regression
- Machine Learning: Multiple Linear Regression
- Machine Learning: Decision Tree
- Machine Learning: Naive Bayes
- Machine Learning: K-Means Clustering
- Machine Learning: K-Nearest Neighbors
- Machine Learning: Support Vector Machine
- Machine Learning Project 1: Housing Price Prediction
- Machine Learning Project 2: IRIS Dataset
- Machine Learning Project 3: Tweet Classifier
- Machine Learning Project 4: Recommendation System