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Machine Learning "ML" - Coggle Diagram
Machine Learning "ML"
Definition
learn from data without being explicitly programmed
Classification
Regression
Topics
Supervised
Unsupervised
Semi-supervised
Reinforcement Learning
What you Expect to Learn
Giving Computers the Ability to Learn from Data
Training Simple Machine Learning Algorithms for Classification
A Tour of Machine Learning Classifiers
Building Good Training Datasets – Data Preprocessing
Compressing Data via Dimensionality Reduction
Learning Best Practices for Model Evaluation and Hyperparameter Tuning
Combining Different Models for Ensemble Learning
Applying Machine Learning to Sentiment Analysis
Embedding a Machine Learning Model into a Web Application
Predicting Continuous Target Variables with Regression Analysis
Working with Unlabeled Data – Clustering Analysis
Implementing a Multilayer Artificial Neural Network from Scratch
Parallelizing Neural Network Training with TensorFlow
Going Deeper – The Mechanics of TensorFlow
Modeling Sequential Data Using Recurrent Neural Networks
Classifying Images with Deep Convolutional Neural Networks
Generative Adversarial Networks for Synthesizing New Data
Reinforcement Learning for Decision Making in Complex Environments
Benefits
Automation
Medical Advances
Research and Data Analysis
Solving Complex Problems
Business Continuity
Minimizing Errors
24/7 Availability
Who Coined the “ML” term
Professor Arthur Samuel
Problems solved by ML
Spam identification
Product Recommendations
Customer Segmentation
Fraudulent Transactions Detection
Demand Forecasting
Virtual Assistant
Sentiment Analysis
Customer Service Automation
Predictive Maintenance
Job Role of the Machine Learning Engineer
building software for making automatic predictions
Needed Skills
Computer Fundamentals, Programming and Software Engineering
Mathematics
Data Modeling and Evaluation
Apply Machine Learning Algorithms
Additional Preferable Skills
Tools
Don’t Be Overwhelmed