Machine Learning Techniques

Syllabus

UNIT-I

Machine Learning- 1. Machine Learning and its applications, 2. Life cycle of Machine Learning, 3. Supervised Machine Learning ,Unsupervised Machine Learning, Supervised Vs Unsupervised Learning, Examples of Machine Learning.

UNIT-II

Regression Analysis- Linear Regression, Simple Linear Regression, Multiple Linear Regression, Backward Elimination, Polynomial Regression, Logistic Regression, Linear Regression Vs Logistic Regression.

UNIT-III

Classification- Classification Algorithm, Classification Vs Regression, Classification Algorithms- K-NN, Support Vector Machine ,Naïve Bayes Classifier, Decision Tree, Random Forest.

UNIT-IV

Clustering- Clustering in Machine Learning, Hierarchical Clustering in Machine Learning, K-Means Clustering Algorithm, Machine Learning Vs A.I Vs Data Science Vs Deep Learning.

UNIT-V

Mathematics & Coding in Machine Learning - Semi-Supervised Learning, Precision and Recall in Machine Learning, Overfitting in Machine Learning, Types of Encoding Techniques , Feature Selection Techniques in Machine Learning.

What is Machine Learning ?

Machine Learning is a branch of Artificial Intelligence that enables systems to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses primarily on the creation of algorithms that enable a computer to independently learn from data and previous experiences.

Machine Learning is a branch of Artificial Intelligence (AI) that enables computers to learn and improve from experience without being explicitly (clear and exactly ) programmed. The goal of ML is to let the program learn patterns and make predictions or decisions based on data.

The term "machine learning" was coined by Arthur Samuel in 1959.

Applications (uses) of Machine Learning

  1. Healthcare: Diagnosing diseases, drug discovery, personalized treatment.
  2. Finance: Fraud detection, stock price prediction, credit scoring.
  3. Retail: Product recommendations, demand forecasting, customer segmentation.
  4. Transportation: Self-driving cars, route optimization, traffic prediction.
  5. Technology: Natural language processing (e.g., chatbots), image recognition, speech-to-text systems.

There are 3 types of Machine Learning Models

  1. Supervised Machine Learning .
  2. Unsupervised Machine Learning.
  3. Reinforcement learning.

Supervised Machine Learning

In supervised learning, models are trained using labelled dataset, where the model learns about each type of data. The labelled data means some input data is already tagged with the correct output. Supervised learning is a process of providing input data as well as correct output data to the machine learning model. The aim of a supervised learning algorithm is to find a mapping function to map the input variable(x) with the output variable(y).

Advantages

  1. Simple to understand and implement.
  2. Effective for well-defined problems with more than enough labeled data.
  3. Provides accurate predictions for structured data.
  4. Supervised learning allows collecting data and produces data output from previous experiences.
  5. Helps to optimize performance criteria with the help of experience.
  6. Supervised machine learning helps to solve various types of real-world computation problems.
  7. It performs classification and regression tasks.
  8. It allows estimating or mapping the result to a new sample. 
  9. We have complete control over choosing the number of classes we want in the training data.

Types of Supervised Learning

Supervised learning is classified into two categories of algorithms: 
  1. Regression
  2. Classification
Regression :

In regression algorithm there is a relation between the input and the output variables. The goal of the regression is to predict the output by understanding the relationship between the variables. The relationship between a dependent variable and an independent variable.  

Classification :

Classification is a supervised machine learning method where the model tries to predict the correct label of a given input data. In classification, the model is fully trained using the training data, and then it is evaluated on test data before being used to perform prediction on new unseen data. 

Unsupervised Machine Learning

Unsupervised learning is a type of machine learning where the computer learns from data that has no labels or categories. This means the data is not organized or classified beforehand. The goal of unsupervised learning is to find patterns, similarities, or relationships in the data on its own.

There is no teacher or training provided to the machine. The machine tries to group the data based on common features or differences. It works without any guidance to understand hidden structures in the data. Unsupervised learning is useful when we don’t have labeled data but still want to find meaningful patterns or clusters.

Advantages

  1. Unsupervised learning is used for more complex tasks as compared to supervised learning because, in unsupervised learning, we don't have labeled input data.
  2. Unsupervised learning is preferable as it is easy to get unlabeled data in comparison to labeled data
  3.  It does not require training data to be labeled.
  4. Dimensionality reduction can be easily accomplished using unsupervised learning. 
  5. Capable of finding previously unknown patterns in data.
  6. Unsupervised learning can help you gain insights from unlabeled data that you might not have been able to get otherwise.
  7. Unsupervised learning is good at finding patterns and relationships in data without being told what to look for. This can help you learn new things about your data.

Types of Unsupervised Learning

Unsupervised learning is classified into two categories of algorithms: 

  1. Clustering
  2. Association

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