Udemy

Fundamentals of Machine Learning through Python

Python, Scikit-Learn, and Practical ML: From Basics to Projects
Free tutorial
Rating: 4.3 out of 5 (84 ratings)
1,988 students
1hr 36min of on-demand video
English
English [Auto]

Learn the art of data cleaning, handling missing values, and feature engineering to ensure high-quality datasets for effective machine learning model training
Develop a solid understanding of Python essentials, control structures, and modular programming, providing a strong foundation for machine learning applications
Dive into supervised learning techniques, mastering linear regression for numerical predictions, and logistic regression for effective classification
Gain proficiency in assessing and optimizing model performance through cross-validation, addressing overfitting and underfitting, and fine-tuning
Delve into ensemble methods such as Random Forest, Gradient Boosting, Support Vector Machine
Apply acquired skills to a practical project, guiding learners through data preprocessing, model selection, training, and evaluation

Requirements

  • Basic knowledge of Python programming is recommended, but this beginner-friendly course welcomes learners with no prior machine learning experience

Description

Unlock the potential of machine learning with our comprehensive course, "Mastering Machine Learning: From Fundamentals to Practical Projects with Python and Scikit-Learn." Tailored for aspiring data enthusiasts and programmers, this course is an immersive journey through the key pillars of machine learning, ensuring a strong foundation and practical proficiency.

Begin with Python fundamentals, covering variables, control structures, and modular programming, before delving into the heart of data science: data preparation. Learn to wield Python for data cleaning, handle missing values, and engineer features to optimize dataset quality. Transition seamlessly into supervised learning, mastering linear and logistic regression for numerical predictions and categorical classifications.

Navigate the intricate landscape of model evaluation and validation, ensuring your models generalize well to unseen data. Harness the power of Scikit-Learn, building and training models with its intuitive interface. Explore advanced topics, from ensemble methods like Random Forest and Gradient Boosting to the complexity-solving capabilities of Support Vector Machines.

The course crescendos with a hands-on project, where learners apply acquired skills to real-world scenarios, from data preprocessing to model selection and evaluation. Emerging from this course, you'll possess the confidence to navigate the machine learning landscape, equipped with practical skills, project experience, and a deepened understanding of Python and Scikit-Learn. Start your machine learning journey today!


Who this course is for:

  • This course is designed for aspiring data enthusiasts, programmers, and beginners in machine learning who seek a comprehensive introduction to the field. Whether you're a Python novice or looking to transition into data science, this beginner-friendly journey will equip you with the essential skills to confidently explore and apply machine learning concepts in real-world scenarios.

Instructor

Instructor At Udemy
Meenakshi Nair
  • 4.4 Instructor Rating
  • 140 Reviews
  • 4,063 Students
  • 2 Courses

My name is Meenakshi Nair, a high school student from the San Francisco Bay Area, the world’s hub of technological development.


I was interested in technology from a very young age. I learned Python and java in middle school and started building mobile applications using Thunkable and Swift since 7th grade.


The COVID-19 pandemic changed my thought process and perception of technology. I became more interested in real-world applications and the impacts of technology on people during the lock down.


This new perspective led me to build a CO2 tracking and monitoring tool using Arduino and Python along with a group of two friends.


The app aimed to help businesses safely reopen and recover from COVID impacts. The app was built for IOS using Apple’s native IOS development environment, the Swift language, Google Cloud for Data Storage, HighCharts for trend analysis, and Text Recognition AIs provided by Apple’s CoreML.


My team was invited to pitch our app at Technovation’s annual World Summit event as a finalist team selected from over 2,700 teams from 62 countries and won the Technovation Girls Junior Division Grand Prize. I have been a Technovation ambassador for last two years, where my role is to make sure that girls in my community are aware of the program and support them through the Technovation journey.


I have facilitated several informational sessions for girls worldwide, including mobile development workshops on ideation, coding, and pitching


I am also passionate about conducting research and working on projects involving engineering, computer science and AI. I have published research papers on AI/ ML applications in the field of astronomy and agriculture.

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