Machine Learning

Machine learning : Beginner to Expert using Python

This comprehensive course takes you on a journey from beginner to expert in machine learning, using Python as the primary tool. Starting with the fundamentals of machine learning and Python programming, you will progress to mastering advanced algorithms, data preprocessing techniques, and model evaluation methods. The course covers practical applications, hands-on projects, and real-world case studies to ensure you gain both theoretical knowledge and practical experience. By the end of the course, you will be equipped to design, implement, and optimize machine learning models for complex problems.

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

This comprehensive course takes you on a journey from beginner to expert in machine learning, using Python as the primary tool. Starting with the fundamentals of machine learning and Python programming, you will progress to mastering advanced algorithms, data preprocessing techniques, and model evaluation methods. The course covers practical applications, hands-on projects, and real-world case studies to ensure you gain both theoretical knowledge and practical experience. By the end of the course, you will be equipped to design, implement, and optimize machine learning models for complex problems.

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

Overview of machine learning: What it is and how it works

Setting up Python for machine learning: Installing libraries (Scikit-learn, Pandas, Matplotlib)

Introduction to Jupyter notebooks and basic Python syntax

Hands-on: Writing your first Python code for data analysis

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

Understanding datasets: Features, labels, and target variables

Introduction to the machine learning pipeline: Data collection, model building, evaluation

Hands-on: Working with a basic dataset and training your first machine learning model

Cleaning data: Handling missing values, outliers, and duplicates

Data transformations: Normalization, scaling, and encoding categorical variables

Data splitting: Training, validation, and test sets

Hands-on: Preprocessing and preparing a dataset for machine learning

Linear regression and understanding the line of best fit

Classification algorithms: Logistic regression, decision trees, and k-nearest neighbors (KNN)

Evaluating models: Accuracy, confusion matrix, precision, recall, and F1 score

Hands-on: Implementing and evaluating a supervised learning model

Introduction to unsupervised learning: Clustering and dimensionality reduction

K-means clustering and hierarchical clustering

Principal Component Analysis (PCA) for dimensionality reduction

Hands-on: Implementing K-means clustering on a real-world dataset

Feature selection: Identifying important features for model training

Feature extraction and engineering techniques for improving model performance

Hyperparameter tuning: Grid search and random search for model optimization

Hands-on: Improving model performance with feature engineering and hyperparameter tuning

Support Vector Machines (SVM) and kernel tricks

Random forests, gradient boosting, and XGBoost

Ensemble learning: Bagging, boosting, and stacking

Hands-on: Implementing advanced algorithms for classification and regression tasks

Cross-validation techniques: K-fold and leave-one-out cross-validation

Evaluating model performance with various metrics

Avoiding overfitting and underfitting: Regularization techniques (L1, L2)

Hands-on: Evaluating and improving model accuracy through cross-validation

Step-by-step guide for designing machine learning projects

Integrating data collection, preprocessing, model training, and evaluation

Deploying models: Introduction to cloud platforms (e.g., AWS, Google Cloud)

Hands-on: Building a complete machine learning pipeline from scratch

Case studies of machine learning applications in various industries (e.g., finance, healthcare, marketing)

Building a portfolio: How to showcase machine learning projects

Introduction to career paths in machine learning and AI

Best practices for working as a machine learning engineer

Hands-on: Creating a portfolio project for your career

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Amar Verma

Hii ama

About Instructor

Hii ama

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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