Learn Machine Learning Step-by-step
INDEX
1. Introduction to Machine Learning
- What is Machine Learning?
- Applications of Machine Learning
- Machine Learning Lifecycle
- Types of Machine Learning
2. Exploratory Data Analysis
- Data Cleaning and Preprocessing
- Data Visualization Techniques
- Feature Extraction and Feature Selection
3. Supervised Learning
- Linear Regression
- Logistic Regression
- Decision Trees and Random Forests
- Naive Bayes
- k-Nearest Neighbors (k-NN)
- Support Vector Machines (SVM)
- Gradient Boosting and AdaBoost
4. Unsupervised Learning
- Clustering Algorithms (K-Means, Hierarchical Clustering)
- Principal Component Analysis (PCA)
- Anomaly Detection
- Model Evaluation and Selection
- Model Performance Metrics
- Cross-Validation Techniques
- Hyperparameter Tuning
- Model Selection Techniques
5. Neural Networks
- Introduction,
- Neural network representation
- Perceptron
- Multi-Layer Perceptron (MLP)
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- back propagation algorithm and Remarks
- An illustrative example of face recognition,
- Advanced topics in artificial neural networks
6. Deep Learning
- Introduction to Deep Learning
- Deep Neural Networks (DNN)
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Autoencoders
- Generative Adversarial Networks (GAN)
7. Natural Language Processing (NLP)
- Introduction to NLP
- Text Preprocessing
- Bag-of-Words and Word Embeddings
- Sentiment Analysis
- Recommender Systems
- Collaborative Filtering
- Content-Based Filtering
- Hybrid Recommender Systems
8. Concept learning
- Introduction
- Concept learning task
- Concept learning as search
- find-S: finding a maximally specific hypothesis
- Version spaces and the candidate
9. Decision Tree Learning
- Introduction and Representation
- Appropriate Problems for Decision Tree Learning
- The Basic Decision Tree Learning Algorithm
- Hypothesis Space Search in Decision Tree Learning
- Inductive Bias in Decision Tree Learning
- Issues in Decision Tree Learning
10. Evolutionary Learning and Evaluation Hypotheses
- Genetic Algorithms
- Genetic Operators
- Genetic Programming
Evaluation Hypotheses Motivation
- Estimation hypothesis accuracy,
- Basics of sampling theory
- A general approach for deriving confidence intervals
- The difference in error between the two hypotheses
- Comparing learning algorithms.
11. Ensemble learning
- Boosting
- Bagging
- Swarm Intelligence
- PSO.
12. Support Vector Machines
- Separating data with the maximum margin,
- finding the maximum margin
- efficient optimization with SMO algorithm,
- speeding up optimization with full Platt SMO,
- Using Kernels for More Complex Data
- Dimensionality Reduction Techniques(PCA)
13. Instance-Based Learning
- Introduction
- k -Nearest Neighbor Learning
- Locally Weighted Regression
- Radial Basis Functions
- Case-Based Reasoning,
- Remarks on Lazy and Eager Learning.
14. Bayesian learning
- Introduction
- Bayes theorem
- Bayes theorem and concept learning
- Maximum Likelihood and least squared error hypotheses
- maximum likelihood hypotheses for predicting probabilities
- minimum description length principle
- Bayes optimal classifier
- Gibs algorithm
- Naïve Bayes classifier
- Bayesian belief networks,
- EM algorithm
15. Computational learning theory
- Introduction
- Probably learning an approximately correct hypothesis
- Sample complexity for finite hypothesis space
- Sample complexity for infinite hypothesis spaces
- The mistake-bound model of learning.
16. Genetic Algorithms
- Motivation
- Genetic algorithms - an illustrative example
- Hypothesis Space Search
- Genetic Programming
- Models of Evolution and Learning
- Parallelizing Genetic Algorithms.
17. Learning Set of Rules
- Introduction
- Sequential Covering Algorithms
- Learning Rule Sets: Summary
- Learning First-Order rules
- Learning sets of First-Order rules: FOIL
- Induction as an inverted deduction, inverting resolution
18. Reinforcement Learning
- Introduction to Reinforcement Learning
- The learning Tasks
- Markov Decision Processes (MDP)
- Q-Learning and Deep Q-Learning
- Policy Gradient Methods
- Temporal difference Learning
- Generalizing from examples
- Relationship to Dynamic Programming
19. Analytical Learning
- Introduction
- learning with perfect domain theories: PROLOG-EBG
- Remarks on explanation-based learning
- Explanation-based learning of search control knowledge
- Using prior knowledge to alter the search objective
- Using prior knowledge to augment search operators.
- Combining Inductive and Analytical Learning
- Motivation
- Inductive-analytical approaches to learning
- Using prior knowledge to initialize the hypothesis.
20. Well-posed Learning
- Introduction
- Designing a learning system
- Perspectives and issues in machine learning
21. Questions and Answers
22. Research
Summary of Machine Learning
Artificial intelligence (AI)'s machine learning subfield enables computer systems to automatically learn from data without having to be explicitly programmed. It is a data-driven approach to creating computer algorithms that can identify patterns in data and make predictions based on that information. In this article, we will explore the concept of machine learning and how it is used in various applications.
One of the key features of machine learning is its ability to analyze large datasets and extract valuable insights from them. Machine learning algorithms are designed to learn from data and identify patterns that can be used to make predictions. These algorithms can be trained on a variety of data types, including images, text, and numerical data.
Machine learning algorithms come in many different varieties, such as supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the algorithm is trained on a labelled dataset, where the desired output is provided for each input. The algorithm uses this labelled data to learn patterns and make predictions on new, unseen data.
In unsupervised learning, the algorithm is trained on an unlabeled dataset, where no specific output is provided for each input. The algorithm uses this data to identify patterns and structure within the dataset, such as grouping similar data points together or finding anomalies in the data.
Algorithms can learn by making mistakes thanks to machine learning approaches such as reinforcement learning. The algorithm is provided with a set of actions and rewards, and it learns which actions to take based on the rewards received.
One of the most popular machine learning applications is in the field of natural language processing (NLP). NLP involves teaching computers to understand human language and respond to it appropriately. This has many practical applications, such as virtual assistants, chatbots, and voice assistants like Siri and Alexa.
Another area where machine learning is commonly used is in the field of computer vision. Computer vision involves teaching computers to understand images and videos. This has applications in areas like facial recognition, object detection, and autonomous vehicles.
Machine learning is also used extensively in the field of data science. Data science involves the collection, analysis, and interpretation of large datasets. Machine learning algorithms can be used to analyze this data and extract valuable insights that can be used to inform business decisions and strategies.
There are many different machine learning tools and platforms available today, including open-source libraries like TensorFlow and Scikit-learn, as well as cloud-based machine learning platforms like Amazon SageMaker and Google Cloud Machine Learning. These tools make it easier for developers and data scientists to build and deploy machine learning models.
Overall, machine learning is a powerful technology that has many practical applications across a variety of industries. From natural language processing and computer vision to data science and autonomous vehicles, machine learning is transforming the way we interact with technology and providing valuable insights that can be used to drive business success.
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