Machine Learning

Machine learning is the study of computer algorithms that can improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence.

Machine Learning Syllabus:

• Introduction
  • Supervised and Unsupervised learning
  • Parametric Methods
  • Dimensionality Reduction
  • Clustering
  • Non-parametric Methods
  • Multilayer perceptron
  • Hidden Markov Model
  • Data Processing
  • Miscellaneous
  • ML using Python
• Introduction :
  • Getting Started with Machine Learning
  • Artificial Intelligence | An Introduction
  • What is Machine Learning ?
  • An introduction to Machine Learning
  • Introduction to Data in Machine Learning
  • Demystifying Machine Learning
  • Applications
  • Machine Learning and Artificial Intelligence
  • Difference between Machine learning and Artificial Intelligence
  • Agents in Artificial Intelligence
• Supervised and Unsupervised learning :
  • Types of Learning – Supervised Learning
  • Types of Learning – Part 2
  • Supervised and Unsupervised learning
  • Reinforcement learning
• Parametric Methods :
  • Regression and Classification
  • Understanding Logistic Regression
  • Understanding Logistic Regression
  • Multivariate Regression
  • Confusion Matrix in Machine Learning
  • Linear Regression(Python Implementation)
  • Softmax Regression using TensorFlow
  • Linear Regression using PyTorch
  • Identifying handwritten digits using Logistic Regression in PyTorch
• Dimensionality Reduction :
  • Parameters for Feature Selection
  • Introduction to Dimensionality Reduction
  • Underfitting and Overfitting in Machine Learning
  • Handling Missing Values
• Clustering :
  • Clustering in Machine Learning
  • Different Types of Clustering Algorithm
  • K means Clustering – Introduction
  • Analysis of test data using K-Means Clustering in Python
  • Gaussian Mixture Model
• Non-parametric Methods :
  • Decision Tree
  • Decision Tree Introduction with example
  • K-Nearest Neighbours
  • Implementation of K Nearest
  • Decision tree implementation using Python
• Multilayer perceptron :
  • Introduction to Artificial Neutral Networks | Set 1
  • Introduction to Artificial Neural Network | Set 2
  • Introduction to ANN (Artificial Neural Networks) | Set 3 (Hybrid Systems)
  • Image Classifier using CNN
• Hidden Markov Model :
  • Markov Decision Process
  • Chinese Room Argument in Artificial Intelligence
• Data Processing :
  • Getting started with Classification
  • Understanding Data Processing
  • Data Cleansing | Introduction
  • Data Preprocessing for Machine learning in Python
• Misc :
  • Pattern Recognition | Introduction
  • Calculate Efficiency Of Binary Classifier
  • Cross Validation in Machine Learning
  • R vs Python in Datascience
• ML using Python :
  • Introduction To Machine Learning using Python
  • Learning Model Building in Scikit-learn : A Python Machine Learning Library
  • Multiclass classification using scikit-learn
  • Classifying data using Support Vector Machines(SVMs) in Python
  • Classifying data using Support Vector Machines(SVMs) in R
  • Phyllotaxis pattern in Python | A unit of Algorithmic Botany
  • How to get synonyms/antonyms from NLTK WordNet in Python?
  • Removing stop words with NLTK in Python
  • Tokenize text using NLTK in python