Machine Learning

Machine learning for absolute beginners

This introductory course is designed for those with no prior experience in machine learning. Participants will learn the foundational concepts of machine learning, including supervised and unsupervised learning, algorithms, and data preprocessing. The course covers practical examples and simple projects to help learners understand how machine learning is applied in real-world scenarios. By the end of the course, students will have a solid understanding of machine learning basics and be ready to explore more advanced topics.

  • 4/5.0
  • 100 Students
  • Beginner
  • English
Course Description

This introductory course is designed for those with no prior experience in machine learning. Participants will learn the foundational concepts of machine learning, including supervised and unsupervised learning, algorithms, and data preprocessing. The course covers practical examples and simple projects to help learners understand how machine learning is applied in real-world scenarios. By the end of the course, students will have a solid understanding of machine learning basics and be ready to explore more advanced topics.

What you’ll learn
  • Comprehensive Learning Path: Step-by-step guide to mastering topics.
  • Interactive Learning: Engage with quizzes, discussions, and activities.
  • Skill Development: Improve technical and soft skills.
  • Flexible Learning: Learn at your own pace.
  • Career Advancement: Equip yourself for job market success.
  • Networking Opportunities: Connect with professionals and experts.
  • Practical Tools and Resources: Access industry-standard tools and resources.
  • Ongoing Support: Get continuous feedback and assistance.
  • Hands-On Projects: Complete real-world projects and tasks.
  • Final Certification: Earn a recognized course certification.

Each course is designed with the highest quality standards to ensure you gain valuable knowledge and practical skills. Whether you're exploring new areas or deepening your expertise, our courses offer a comprehensive learning experience. With detailed explanations, real-world examples, and expert guidance, you'll be equipped to apply what you've learned immediately. Join us and unlock new opportunities for growth and success

What is Machine Learning?

Real-world applications of machine learning (e.g., recommendations, image recognition, fraud detection).

Types of machine learning: Supervised, unsupervised, and reinforcement learning.

Types of data: Structured vs. unstructured data

How to collect, clean, and preprocess data

Splitting data into training and testing sets

Hands-on: Working with sample datasets using Pandas

Introduction to supervised learning: What it is and how it works

Key algorithms: Linear regression, decision trees, and k-nearest neighbors (KNN)

Training models and making predictions

Hands-on: Building and evaluating a simple regression model

Introduction to unsupervised learning: Clustering and pattern discovery

Key algorithms: K-means clustering, hierarchical clustering, and dimensionality reduction

Hands-on: Implementing a K-means clustering algorithm

How to evaluate a machine learning model

Key metrics: Accuracy, precision, recall, F1-score, and confusion matrix

Cross-validation and overfitting

Hands-on: Evaluating a model’s performance on test data

The training process: Fitting models to data

Avoiding overfitting and underfitting

Test data vs. training data: Importance of unbiased testing

Hands-on: Train and test a simple model using a real dataset

Introduction to popular ML libraries: Scikit-learn, Matplotlib, and Pandas

Using Jupyter notebooks for coding and data analysis

Hands-on: Installing libraries and using them to create simple machine learning models

Bias in data and algorithms

Privacy and security concerns

The importance of transparency and explainability in models

Step-by-step guide to building a basic machine learning model

From data collection to model evaluation

Hands-on: Create a simple ML model to solve a problem

Resources for further learning: Books, courses, and online communities

Introduction to more advanced topics: Neural networks, deep learning, and natural language processing

How to keep practicing and developing your machine learning skills

instructor-image

amar verma

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About Instructor

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Inspiring Learning, One Step at a Time As an educator, your dedication transforms students' lives. Our platform is designed to empower you with tools, resources, and a community that values your expertise. Share your knowledge, inspire curiosity, and help learners achieve their dreams. Together, we build a brighter future, one lesson at a time.

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This course includes

  • Lectures 40 Classes
  • Duration Hours
  • Skills Beginner
  • Language English
  • Certificate Yes

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