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Unit - II ML Python Libraries

  Python Libraries for Machine Learning  Introduction to Machine Learning Libraries Machine Learning (ML) is a field of Artificial Intelligence that enables computers to learn patterns from data and make predictions or decisions without being explicitly programmed. Python is the most popular language for ML because it has many powerful libraries. The most important ones are: NumPy – for numerical computing Pandas – for data handling and analysis Matplotlib – for data visualization Scikit-learn (sklearn) – for building ML models 1. NumPy (Numerical Python) What is NumPy? NumPy is a Python library used for working with arrays and numerical data . It is faster and more efficient than Python lists. NumPy is mainly used for: Mathematical operations Linear algebra Handling large datasets Importing NumPy import numpy as np Creating Array: array() An array is a collection of elements of the same type. import numpy as np arr = np.array([ ...
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Practical for IML

  Aim 1: Explore any one Machine Learning tool (Scikit-learn) Program # Exploring Scikit-learn library import sklearn print ( "Scikit-learn version:" , sklearn.__version__) print ( "Scikit-learn is used for machine learning tasks such as:" ) print ( "Classification, Regression, Clustering, and Model Evaluation" ) Aim 2: NumPy basic operations Program import numpy as np # Convert list to 1D NumPy array list1 = [ 1 , 2 , 3 , 4 , 5 ] arr1 = np.array(list1) print ( "1D Array:" , arr1) # Create 3x3 matrix from 2 to 10 matrix = np.arange( 2 , 11 ).reshape( 3 , 3 ) print ( "3x3 Matrix:\n" , matrix) # Append values to array arr2 = np.append(arr1, [ 6 , 7 ]) print ( "Appended Array:" , arr2) # Reshape array from 3x2 to 2x3 arr3 = np.array([[ 1 , 2 ], [ 3 , 4 ], [ 5 , 6 ]]) reshaped = arr3.reshape( 2 , 3 ) print ( "Reshaped Array:\n" , reshaped) Aim 3: NumPy mathematical operations Program import numpy ...
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Unit - 1 Introduction to machine learning

  CHAPTER 1 INTRODUCTION TO MACHINE LEARNING** 1.1 Overview of Human Learning and Machine Learning Human Learning Human learning is a natural process where people gain knowledge from experience, observation, practice, and reasoning . For example: A child learns to recognize animals by seeing pictures. A driver learns to drive better with practice. Humans learn using the brain , emotions, and thinking abilities. Machine Learning Machine Learning (ML) is a branch of Artificial Intelligence (AI) that enables machines (computers) to learn from data and improve performance without being explicitly programmed . Example: Email spam filters learn from past emails. Recommendation systems suggest movies based on user behavior. 👉 Key Difference Human Learning Machine Learning Uses brain Uses algorithms Learns from experience Learns from data Emotional & logical Mathematical & statistical 1.2 Types of Machine Learning Machine Learning can be classified in...
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