Machine Learning

Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed. ML is one of the most exciting technologies that one would have ever come across. As it is evident from the name, it gives the computer that which makes it more similar to humans: The ability to learn. Machine learning is actively being used today, perhaps in many more places than one would expect.

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Recent Articles on Machine Learning !

Introduction :

  1. Getting Started with Machine Learning
  2. Artificial Intelligence | An Introduction
  3. What is Machine Learning ?
  4. An introduction to Machine Learning
  5. Introduction to Data in Machine Learning
  6. Demystifying Machine Learning
  7. Applications
  8. Machine Learning and Artificial Intelligence
  9. Difference between Machine learning and Artificial Intelligence
  10. Agents in Artificial Intelligence

Supervised and Unsupervised learning :

  1. Types of Learning – Supervised Learning
  2. Types of Learning – Part 2
  3. Supervised and Unsupervised learning
  4. Reinforcement learning

Parametric Methods :

  1. Regression and Classification
  2. Understanding Logistic Regression
  3. Understanding Logistic Regression
  4. Multivariate Regression
  5. Confusion Matrix in Machine Learning
  6. Linear Regression(Python Implementation)
  7. Softmax Regression using TensorFlow
  8. Linear Regression using PyTorch
  9. Identifying handwritten digits using Logistic Regression in PyTorch

Dimensionality Reduction :

  1. Parameters for Feature Selection
  2. Introduction to Dimensionality Reduction
  3. Underfitting and Overfitting in Machine Learning
  4. Handling Missing Values

Clustering :

  1. Clustering in Machine Learning
  2. Different Types of Clustering Algorithm
  3. K means Clustering – Introduction
  4. Analysis of test data using K-Means Clustering in Python
  5. Gaussian Mixture Model

Non-parametric Methods :

  1. Decision Tree
  2. Decision Tree Introduction with example
  3. K-Nearest Neighbours
  4. Implementation of K Nearest
  5. Decision tree implementation using Python

Multilayer perceptron :

  1. Introduction to Artificial Neutral Networks | Set 1
  2. Introduction to Artificial Neural Network | Set 2
  3. Introduction to ANN (Artificial Neural Networks) | Set 3 (Hybrid Systems)
  4. Image Classifier using CNN

Hidden Markov Model :

  1. Markov Decision Process
  2. Chinese Room Argument in Artificial Intelligence

Data Processing :

  1. Getting started with Classification
  2. Understanding Data Processing
  3. Data Cleansing | Introduction
  4. Data Preprocessing for Machine learning in Python

Misc :

  1. Pattern Recognition | Introduction
  2. Calculate Efficiency Of Binary Classifier
  3. Cross Validation in Machine Learning
  4. R vs Python in Datascience

ML using Python :

  1. Introduction To Machine Learning using Python
  2. Learning Model Building in Scikit-learn : A Python Machine Learning Library
  3. Multiclass classification using scikit-learn
  4. Classifying data using Support Vector Machines(SVMs) in Python
  5. Classifying data using Support Vector Machines(SVMs) in R
  6. Phyllotaxis pattern in Python | A unit of Algorithmic Botany
  7. How to get synonyms/antonyms from NLTK WordNet in Python?
  8. Removing stop words with NLTK in Python
  9. Tokenize text using NLTK in python

>>’Practice Problems’ on Machine Learning !



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