Ml Fundamentals & Data Preparation

This section introduces the foundational concepts of machine learning, emphasizing data preparation critical for effective model training.

Module Objectives

This section outlines the objectives of Module 1 on ML fundamentals and data preparation, detailing what students will learn upon completion.

Week 1: Introduction To Machine Learning & Ecosystem

This section introduces the fundamentals of machine learning, including its definition, types, workflow, and the essential Python libraries.

Core Concepts

This section introduces the foundational concepts of machine learning, including its definition, types, applications, and workflow.

Definition Of Machine Learning (Ml)

Machine learning is a subfield of AI that enables systems to learn from data without being explicitly programmed.

Types Of Machine Learning

This section covers the different types of machine learning, including supervised, unsupervised, semi-supervised, and reinforcement learning, along with their key characteristics and applications.

Supervised Learning

Supervised Learning is a predominant machine learning paradigm where models learn from labeled datasets to make predictions or classifications based on input features.

Unsupervised Learning

Unsupervised learning allows models to identify patterns in unlabeled data, facilitating tasks like clustering and dimensionality reduction.

Semi-Supervised Learning (Conceptual)

Semi-supervised learning is a machine learning approach that uses both labeled and unlabeled data to improve training performance.

Reinforcement Learning (Conceptual)

Reinforcement learning is a type of machine learning where an agent learns to make decisions through interactions with its environment, aiming to maximize cumulative rewards.

Key Applications And Impact Of Ml

This section explores the transformative applications of machine learning across various industries, showcasing its significant impact on healthcare, finance, marketing, and more.

The Machine Learning Workflow: A Lifecycle

This section outlines the structured workflow of a machine learning project, emphasizing its critical steps from problem definition to deployment.

Python Ml Ecosystem: Essential Libraries

This section details key Python libraries critical for machine learning development, including tools for data manipulation, visualization, and interactive computing.

Lab: Environment Setup & Basic Eda

This section guides students through setting up their Python environment and conducting basic exploratory data analysis (EDA).

Lab Objectives

This section outlines the key objectives for a lab session focused on setting up a Python environment and performing basic exploratory data analysis.

Activities

This section outlines the practical activities designed to reinforce the concepts taught in prior modules on machine learning.

Week 2: Data Preprocessing & Feature Engineering

This section covers crucial techniques in data preprocessing and feature engineering that impact machine learning model performance.

Core Concepts

This section outlines the fundamental concepts of machine learning, including its definition, types, applications, workflow, and essential Python libraries.

Data Types And Their Implications

This section outlines different data types in machine learning and their implications for data preprocessing and model performance.

Handling Missing Values

This section discusses the issues related to missing data in machine learning and outlines strategies for their identification and management.

Feature Scaling

Feature scaling is a crucial preprocessing step in machine learning that transforms numerical features to a common scale, improving model performance and training stability.

Encoding Categorical Features

This section explains the importance and methods of converting categorical data into numerical formats for machine learning algorithms.

Feature Engineering Principles

Feature engineering is the process of creating and transforming features to enhance model performance in machine learning.

Dimensionality Reduction: Principal Component Analysis (Pca) Introduction

This section introduces Principal Component Analysis (PCA), a technique for reducing the dimensionality of data while preserving variation.

Lab: Comprehensive Data Cleaning, Transformation, And Basic Feature Engineering

This section provides a comprehensive hands-on lab experience focusing on data cleaning, transformation, and basic feature engineering techniques essential for preparing datasets for machine learning.

Lab Objectives

This section outlines the objectives and expected outcomes for the week's lab focused on setting up a Python environment and conducting basic exploratory data analysis (EDA).

Activities

The activities section provides a hands-on approach to understanding key concepts in machine learning through practical exercises.