Getting Started with Machine Learning
Understand the basics of ML now and get started with it today.
Wipe the slate clean and learn Machine Learning from scratch
Machine Learning Tutorial
Machine Learning Software
Applications of Machine Learning
Future of Machine Learning
ML Advantages & Limitations
Algorithms for ML
Artificial Neural Networks (ANN) – ML
Convolutional Neural Networks (CNN) – ML
Recurrent Neural Networks(RNN) – ML
ML ANN Applications
ML ANN Learning Rules
ML ANN Model
ML ANN Algorithms
Level up to more exciting and challenging chapters
Deep Learning Tutorial- ML
ML-DL Terminologies
ML-DL For Audio Analysis
Support Vector Machine(SVM) – ML
SVM Applications – ML
SVM Kernel Functions – ML
Dimensionality Reduction – ML
ML Gradient Boosting Algorithm
ML XGBoost Tutorial
ML XGBoosting Algorithm
ML AdaBoost Algorithm
Deep Learning vs Machine Learning
DL vs ML vs AI vs DS
Master new skills and evolve as an expert
Python Machine Learning Tutorial
Python Machine Learning Environment Setup
Data Preprocessing for Python Machine Learning
Train & Test Sets for Python Machine Learning
Python ML Techniques
Python ML Applications
Python ML Algorithms
Python Deep Learning Tutorial
Python DL Applications
Python DL Environment Setup
Python Libraries for Deep Learning
Neural Network for DL with Python
Computational Graphs- Python DL
Introduction To Bayesian Methods
Bayesian Networks and their Characteristics
Probabilistic Bayesian Network Inference
Bayesian Network Applications
Check out more cool technologies
Exploring the Idea
Let’s take a look at some facts about machine learning and its philosophies.
In 1959, computer gaming and AI pioneer Arthur Samuel coined the term at IBM. This is a field of computer science that makes use of statistical techniques to give computer systems the ability to learn without being explicitly programmed. This comes through a quest for artificial intelligence- as they say, necessity is the mother of invention. Many researchers like to claim this is the best way to progress toward human-level AI. With machine learning, we build algorithms with the ability to receive input data and use statistical analysis to predict output while updating output as newer data become available.
We often make use of techniques like supervised, semi-supervised, unsupervised, and reinforcement learning to give machines the ability to learn.
Arthur Lee Samuel