ABHIGYAN JHA, BEAIDB15 12 SEPTEMBER 2024
OVERVIEW OF ML
Introduction
Welcome to the exciting world of machine learning! In this
presentation, we will explore the basics of machine learning, including
different types such as supervised, unsupervised, and reinforcement
learning. Get ready to dive into the ML pipeline, understanding each
step involved, and discovering essential libraries like numpy, keras,
matplotlib, scikit-learn, and scipy.
SUPERVISED LEARNING
Labeled Data Training
Regression Classification Labelled Data
Predict continuous values Predict categories or classes Training with known outcomes
UNSUPERVISED LEARNING
Cluster Analysis & Reduction
Cluster Analysis Dimensionality Reduction
Grouping data points without labels for patterns Reducing features while preserving data structure
AGENTS LEARN THROUGH TRIAL AND ERROR, REWARDS SYSTEM
Reinforcement
Learning
Reinforcement Learning is a type of machine learning where agents
learn to make decisions through trial and error, based on a rewards
system. It is commonly used in scenarios where an agent interacts
with an environment to achieve a goal, by taking actions and receiving
rewards based on those actions.
ML PIPELINE STEPS
Data Flow Basics
01 02 03 04
Data Collection Preprocessing Model Training Evaluation
Gathering relevant datasets Cleaning, transforming, and Algorithms learn patterns Assessing model
for analysis scaling data from data performance metrics
EFFICIENT ARRAY OPERATIONS
Numpy Library
01 02 03
Key features Popular functions Integration with other libs
Multidimensional array support Array manipulation, linear algebra Seamless integration with SciPy
KERAS LIBRARY
User-friendly API
01 02
High-level Modular Design
Keras is a high-level neural networks API, enabling fast Its user-friendly design allows for easy building and testing of
experimentation with deep learning models. neural networks through a modular approach.
DATA VISUALIZATION TOOL
Matplotlib Library
Matplotlib is a powerful data visualization library that enables users to
create interactive plots and charts to explore and communicate their
data effectively. With Matplotlib, you can customize every aspect of
your visualizations to tell a compelling story and provide insights to
your audience.
MACHINE LEARNING TOOLS
Scikit-Learn Library
01 02 03
Data Mining Data Analysis Model Building
Explore data patterns & insights Analyze and visualize data Develop machine learning models
SCIPY LIBRARY
Scientific Computing Functions
Numerical Analysis Data Visualization
Mathematical computation with arrays & matrices Plotting functions for data representation
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