Machine Learning Tutorial

What is Machine Learning? Machine Learning Life Cycle Python Anaconda setup Difference between ML/ AI/ Deep Learning Understanding different types of Machine Learning Data Pre-processing Supervised Machine Learning

ML Regression Algorithm

Linear Regression

ML Classification Algorithm

Introduction to ML Classification Algorithm Logistic Regression Support Vector Machine Decision Tree Naïve Bayes Random Forest

ML Clustering Algorithm

Introduction to ML Clustering Algorithm K-means Clustering Hierarchical Clustering

ML Association Rule learning Algorithm

Introduction to association Rule Learning Algorithm

Miscellaneous

Top 5 programming languages and their libraries for Machine Learning Basics Vectors in Linear Algebra in ML Decision Tree Algorithm in Machine Learning Bias and Variances in Machine Learning Machine Learning Projects for the Final Year Students Top Machine Learning Jobs Machine Learning Engineer Salary in Different Organisation Best Python Libraries for Machine Learning Regularization in Machine Learning Some Innovative Project Ideas in Machine Learning What is Cross Compiler Decoding in Communication Process IPv4 vs IPv6 Supernetting in Network Layer TCP Ports TCP vs UDP TCP Working of ARP Hands-on Machine Learning with Scikit-Learn, TensorFlow, and Keras Kaggle Machine Learning Project Machine Learning Gesture Recognition Machine Learning IDE Pattern Recognition and Machine Learning a MATLAB Companion Chi-Square Test in Machine Learning Heart Disease Prediction Using Machine Learning Machine Learning and Neural Networks Machine Learning for Audio Classification Standardization in Machine Learning Student Performance Prediction Using Machine Learning Data Visualization in Machine Learning How to avoid over fitting In Machine Learning Machine Learning in Education Machine Learning in Robotics Network intrusion Detection System using Machine Learning

Machine Learning Workflow

Introduction: 

 Machine Learning is a subdivision of AI (artificial Intelligence) that is mainly focused on creating such systems that can automatically learn and improve from experience or data, rather than relying on explicit instructions.

The term "machine learning workflow" refers to the succession of phases or procedures involved in the process of developing a successful machine learning system. The basic machine learning flow is:

Data —> Model —> Prediction

Various Stages in Machine Learning Flow:

Machine Learning Workflow

The steps of the machine learning process are as follows:

  1. Collection Of Data: In this step, we collect data from various sources which include databases, sensors, APIs, and other means.
  2. Preprocessing of Data: Cleaning the data and preparing it for ML tasks. This includes removing missing or duplicate data, normalization, scaling, and feature selection.
  3. Splitting of Data: Splitting the data into different sets such as training, testing, etc. It is done to train the model on the training data and evaluate the performance of the model on the testing data.
  4. Model Selection: It is the process of selecting a suitable ML algorithm for the task at the problem. This is determined by the type of data, the nature of the problem, and the availability of resources for computation.
  5. Training Model: It is an iterative method here the training data is adjusted in a way that minimizes the errors or loss functions. It will iteratively repeat the process until the model finds a satisfactory solution.
  6. Choosing an Algorithm to Learn: It is important to know the types of Machine Learning because we need to know that which algorithm and where to apply to solve business problems.

Generally, there are three types of ML which include

  •  Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning

Let us learn in detail about these types.

Machine Learning Workflow

1. Supervised Learning:

Supervised learning is a form of machine learning in which a model is trained using labeled data. It is generally applicable, and since you already have training data and know the answer, the algorithm produces a set of input data and their matching output. Supervised Learning can be further classified into two types:

  1. Regression
  2. Classification

Examples:

  • Fraud Detection
  • Image recognition
  • Medical Diagnostics
  • Product Sales Prediction
  • Weather Forecasting
  • Market Forecasting
  • Population Growth Prediction

2. Unsupervised Learning:

In unsupervised learning, the machine must uncover and create the labels itself where you have data but look for insights within the data. It is further divided into below types of algorithms:

  1. Clustering: The task of clustering is to identify different patterns within the clusters of the data without any previous knowledge about labels.
  2. Dimensionality Reduction: It is a technique used in ML to reduce the number of attributes or variables in a set of data while keeping as much information as possible.

There are two main approaches to dimensionality reduction:

  • Feature Selection: This includes selecting a subset of features that are more relevant to the main problem given. It is done by analyzing the relation between features and target variables by using machine learning algorithms.
  • Feature Extraction: This involves creating new features that collect the most essential data from the original features.

Examples:

  • Product recommendations
  • Medical diagnostics
  • Visualization
  • Gene sequencing
  • Data Structure discovery

The advantage of unsupervised learning is that since we discover the patterns, they enable you to see patterns in data that you are not aware of before.

3. Reinforcement Learning:

 It is the process of learning through trial and error. Here the model learns based on experience and feedback. Unlike other ML types, reinforcement learning continuously improves its model by mining feedback from previous iterations. Most business problems are supervised learning problems.

It is used when the goal is already known but the path to achieving is not known and that path requires lots of trial and error to discover.

Examples:

  • AWS Deep Racer.

7. Predictions: At this point, Finally, the created system is used in the real world. The fundamental value of machine learning is realized here. In this step, machine learning model is fed with the real-world data, and model makes predictions on that data. As per the prediction, model accuracy and efficiency is calculated.