Machine Learning Theory (Basic) New

Best Theory Course for ML

What you'll learn

Where to Collect Data For Machine Learning? | Data Collection

Data Preprocessing Techniques/Steps

Feature Engineering for Machine Learning

Supervised vs Unsupervised vs Reinforcement Learning

Requirements

Basic Computer Literacy: Familiarity with using a computer, including browsing the internet, using basic software, and managing files.

Interest in Programming: A genuine interest in learning programming and problem-solving techniques.

Access to a Computer: A personal computer with a stable internet connection to participate in online classes, complete assignments, and practice coding.

Basic Understanding of Mathematics: Knowledge of high school-level mathematics, including algebra, is beneficial for understanding algorithms and data structures.

Description

The "Machine Learning Theory (Basic)" course offers a thorough introduction to the core principles and foundational concepts of machine learning, making it an ideal starting point for beginners. This course is designed to demystify the complex world of machine learning by breaking down the essential topics that form the backbone of this rapidly growing field. Students will begin with understanding the basics of data collection, learning where and how to gather relevant data, a critical first step in any machine learning project.As the course progresses, students will delve into data preprocessing techniques, which are vital for transforming raw data into a format suitable for modeling. This includes learning how to clean data, handle missing values, and normalize datasets, ensuring that the data is in optimal condition for analysis.Feature engineering, another key topic, will teach students how to create and select the most relevant features to enhance model performance. This skill is crucial as it directly impacts the accuracy and effectiveness of machine learning models.The course also provides a comprehensive overview of the different learning paradigms—supervised, unsupervised, and reinforcement learning—offering students insight into when and how to apply each method. By the end of this course, students will have gained a strong theoretical foundation in machine learning, equipping them with the knowledge to advance to more specialized studies or to begin applying these concepts to real-world problems with confidence.

Overview

Section 1: Where to Collect Data For Machine Learning? | Data Collection

Lecture 1 Data Collection and upload dataset in google colab

Section 2: Data Preprocessing Techniques/Steps

Lecture 2 Data Preprocessing Techniques/Steps

Section 3: Feature Engineering for Machine Learning

Lecture 3 Feature Engineering for Machine Learning

Section 4: Supervised vs Unsupervised vs Reinforcement Learning

Lecture 4 Supervised vs Unsupervised vs Reinforcement Learning

Section 5: Mastering Missing Data Handling

Lecture 5 Mastering Missing Data Handling

Beginners in Machine Learning: Individuals who are new to the field of machine learning and want to understand the foundational concepts and theories.,Aspiring Data Scientists and ML Engineers: Those who aim to build a career in data science or machine learning and are looking for an entry point into the field.,Professionals Seeking to Enhance Their Skills: Professionals who want to add machine learning knowledge to their existing skill set, regardless of their background.,Individuals Preparing for Further Studies: Those planning to pursue advanced studies or certifications in machine learning and wish to establish a strong theoretical foundation.