Getting Started with Machine Learning
Explore the Machine Learning Tutorial Series and learn ML from Scratch
Fresh start with Machine Learning tutorials and become a pro
- Machine Learning Tutorial
- Machine Learning Software
- Machine Learning Use Cases
- Applications of Machine Learning
- Why Machine Learning is Popular
- Machine Learning Algorithm Types
- Machine Learning Tools
- Future of Machine Learning
- ML Advantages & Limitations
- ML Transfer Learning
- ML Java Libraries
- ML Clustering
- 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
- ML for entrepreneurs
- Machine Learning Infographic for Beginners
- Machine Learning Project – Credit Card Fraud Detection
- Machine Learning Project – Sentiment Analysis
- Machine Learning Project – Movie Recommendation System
- Machine Learning Project – Customer Segmentation
Develop the understanding of ML with new Deep Learning 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
Put your Machine Learning and Python Skills to work
- 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
Practical Learning with Machine Learning Projects
Strengthen your portfolio and get your dream Data Science job by implementing these real-world Machine Learning Projects. Display your true potential to recruiters and become the next data scientist.
Check out more cool technologies related to Machine Learning
Exploring the Idea of Machine Learning
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.
What is Machine Learning?
Machine Learning is the scientific study of algorithms that involves usage of statistical models that computers utilize to carry out specific tasks without any explicit instruction. It relies on patterns and other forms of inferences derived from the data.
Machine Learning algorithms are built on top of a mathematical model that makes use of a sample data known as “training data” for making decisions without any explicit programming.
Arthur Lee Samuel