Learn and Implement Machine Learning Projects using Python

Learn and Implement Machine Learning Projects using Python, available at $44.99, with 42 lectures, and has 3 subscribers.

You will learn about Foundational ML Concepts: Understand the core principles behind machine learning, including the different types of ML algorithms. Python for ML: Master the use of Python libraries like NumPy, Pandas, Matplotlib, and Scikit-learn for ML development. Developing ML Projects: Learn the step-by-step process of creating a machine learning project, from data collection and preprocessing to model training and eval Real-World Applications: Apply your skills to real-world projects across various domains, such as finance, healthcare, and more. Model Deployment: Discover how to deploy your machine learning models to production environments. This course is ideal for individuals who are Beginners interested in machine learning and data science. or Software developers looking to expand their skills into ML. or Data analysts aiming to apply ML techniques to their work. or Anyone curious about how machine learning can be applied to solve problems. It is particularly useful for Beginners interested in machine learning and data science. or Software developers looking to expand their skills into ML. or Data analysts aiming to apply ML techniques to their work. or Anyone curious about how machine learning can be applied to solve problems.

Enroll now: Learn and Implement Machine Learning Projects using Python

Summary

Title: Learn and Implement Machine Learning Projects using Python

Price: $44.99

Number of Lectures: 42

Number of Published Lectures: 42

Number of Curriculum Items: 42

Number of Published Curriculum Objects: 42

Original Price: $34.99

Quality Status: approved

Status: Live

What You Will Learn

Foundational ML Concepts: Understand the core principles behind machine learning, including the different types of ML algorithms.
Python for ML: Master the use of Python libraries like NumPy, Pandas, Matplotlib, and Scikit-learn for ML development.
Developing ML Projects: Learn the step-by-step process of creating a machine learning project, from data collection and preprocessing to model training and eval
Real-World Applications: Apply your skills to real-world projects across various domains, such as finance, healthcare, and more.
Model Deployment: Discover how to deploy your machine learning models to production environments.

Who Should Attend

Beginners interested in machine learning and data science.
Software developers looking to expand their skills into ML.
Data analysts aiming to apply ML techniques to their work.
Anyone curious about how machine learning can be applied to solve problems.

Target Audiences

Beginners interested in machine learning and data science.
Software developers looking to expand their skills into ML.
Data analysts aiming to apply ML techniques to their work.
Anyone curious about how machine learning can be applied to solve problems.

Embark on a transformative journey into the realm of machine learning (ML) with our meticulously crafted course, “Mastering Machine Learning Projects with Python: From Basics to Project Deployment.” Designed to cater to both beginners and intermediate enthusiasts, this course stands as a beacon for those aspiring to navigate the complexities of ML and leverage Python’s powerful libraries to solve real-world problems.

At the heart of this course is a commitment to demystify machine learning, making it accessible and engaging for all learners. Whether you aim to pivot your career towards data science, augment your existing skill set, or bring machine learning capabilities to your projects, our course is tailored to meet these ambitions head-on. Through a rich blend of theoretical foundations and practical application, you’ll not only grasp the underlying principles of machine learning but also gain the hands-on experience necessary to implement your knowledge in tangible projects.

Our curriculum is structured to provide a deep dive into the essential aspects of machine learning, starting with an exploration of foundational concepts such as supervised, unsupervised, and reinforcement learning. You’ll learn about the significance of data in ML, how to preprocess and visualize data for better insights, and the intricacies of model selection, training, and evaluation.

Python, being at the forefront of ML development, serves as the perfect tool for this journey. You’ll become proficient in utilizing Python’s rich ecosystem, including libraries like NumPy for numerical operations, Pandas for data manipulation, Matplotlib for data visualization, and Scikit-learn for building and deploying models. These skills will empower you to tackle hands-on projects across diverse domains, from predicting financial trends to diagnosing medical conditions, ensuring you have the competence to address a wide array of challenges.

Moreover, the course doesn’t just end at model development. We delve into model deployment, teaching you how to bring your ML models into production environments, a critical skill in today’s data-driven landscape.

With high-quality video content, engaging lectures, practical projects, and comprehensive support through quizzes, assignments, and community interaction, our course guarantees an enriching and enjoyable learning experience. Upon completion, not only will you receive a certificate of completion to validate your expertise, but you’ll also possess the confidence to apply machine learning techniques in a variety of settings.

Join us on this exciting journey to unlock the potential of machine learning and Python, and take the first step towards becoming an adept ML practitioner capable of turning data into insights and actions.

Course Curriculum

Chapter 1: Data at the Core

Lecture 1: How Top Companies Drive Success with Data Science

Lecture 2: Overview of Machine Learning

Lecture 3: Whats Artificial Intelligence

Lecture 4: Artificial intelligence Applications

Lecture 5: Learn Data Science Without Any Background

Lecture 6: Business Analysts v Data Analyst

Lecture 7: Data Analytics v Data Scientist

Chapter 2: Python Programming Concepts

Lecture 1: Python 101 – Basic Python

Chapter 3: Learning Database Programming

Lecture 1: SQL 101 – Session 1 – Introduction to Database

Lecture 2: SQL 101 – Session 2 – MYSQL Installation

Lecture 3: SQL 101 – Session 3 – Launching MYSQL and Creating a database

Lecture 4: SQL 101 – Session 4 – Designing a RDBMS database

Lecture 5: Lecture 12: Running SQL Queries

Chapter 4: Statistics for Machine Learning

Lecture 1: Introduction to Statistics

Lecture 2: Data Types

Lecture 3: Measure of Central Tendency

Lecture 4: Measure of Central Tendency – Application in Business

Lecture 5: Measure of Variability and use of metric

Lecture 6: Measure of Dispersion – Introduction to RMSE MSE MAE VIF

Lecture 7: Working with Measure of dispersion metric

Lecture 8: Applications of Variance and Standard Deviation

Lecture 9: Applications of RMSE, MSE, MAE, VIF

Lecture 10: Introdution to Graphical Techniques

Lecture 11: Understanding QQ Plot

Lecture 12: Understanding Variance plot

Lecture 13: Understanding Normal Distribution

Lecture 14: Skewness and Kurtosis

Lecture 15: Imputation in Statistics

Chapter 5: Understanding Data Visualization

Lecture 1: Introduction to Data Visualization

Lecture 2: Important Visualization plots and graphs

Chapter 6: Foundation of Machine Learning

Lecture 1: Basic Concepts

Lecture 2: Machine Learning Models

Lecture 3: Advanced AI Applications

Lecture 4: Understanding the Machine Learning process

Lecture 5: Preprocessing of Dataset

Chapter 7: Regression Algorithm

Lecture 1: Regression Introduction

Lecture 2: Python example of Simple Linear Regression

Lecture 3: Understanding Regression model evaluation

Chapter 8: Classification Algorithms

Lecture 1: Evaluating a Classification Model

Chapter 9: Data Visualization

Lecture 1: Data Visualization using Power BI

Chapter 10: Additional Content

Lecture 1: Learn about Federated Machine Learning

Lecture 2: Federated Machine Learning in IoT

Instructors

Swapnil Saurav
Industry Expert

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Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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