Machine Learning Mastery: From Basics to Advanced Techniques

Machine Learning Mastery: From Basics to Advanced Techniques, available at $54.99, has an average rating of 4.8, with 36 lectures, based on 20 reviews, and has 37 subscribers.

You will learn about Explore popular machine learning algorithms. Learn How to use natural language processing (NLP) with Supervised Machine Learning Algorithms for Sentiment Analysis & Text Classification. Implement real-world projects using Python and scikit-learn. Optimize models for accuracy and efficiency. Develop critical thinking skills to tackle real-world challenges. Showcase your skills to potential employers. This course is ideal for individuals who are Practical experience: Learn by doing with hands-on projects and exercises. or Portfolio building: Showcase your skills to potential employers. or Problem-solving: Develop critical thinking skills to tackle real-world challenges. or Continuous learning: Stay updated with the latest advancements in machine learning. It is particularly useful for Practical experience: Learn by doing with hands-on projects and exercises. or Portfolio building: Showcase your skills to potential employers. or Problem-solving: Develop critical thinking skills to tackle real-world challenges. or Continuous learning: Stay updated with the latest advancements in machine learning.

Enroll now: Machine Learning Mastery: From Basics to Advanced Techniques

Summary

Title: Machine Learning Mastery: From Basics to Advanced Techniques

Price: $54.99

Average Rating: 4.8

Number of Lectures: 36

Number of Published Lectures: 36

Number of Curriculum Items: 36

Number of Published Curriculum Objects: 36

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Explore popular machine learning algorithms.
Learn How to use natural language processing (NLP) with Supervised Machine Learning Algorithms for Sentiment Analysis & Text Classification.
Implement real-world projects using Python and scikit-learn.
Optimize models for accuracy and efficiency.
Develop critical thinking skills to tackle real-world challenges.
Showcase your skills to potential employers.

Who Should Attend

Practical experience: Learn by doing with hands-on projects and exercises.
Portfolio building: Showcase your skills to potential employers.
Problem-solving: Develop critical thinking skills to tackle real-world challenges.
Continuous learning: Stay updated with the latest advancements in machine learning.

Target Audiences

Practical experience: Learn by doing with hands-on projects and exercises.
Portfolio building: Showcase your skills to potential employers.
Problem-solving: Develop critical thinking skills to tackle real-world challenges.
Continuous learning: Stay updated with the latest advancements in machine learning.

Are you ready to dive into the exciting world of machine learning? Look no further! In this comprehensive Udemy course, you’ll learn everything you need to know about machine learning, from foundational concepts to cutting-edge techniques.

Are you ready to embark on an exhilarating journey into the world of machine learning? Look no further! Our comprehensive Udemy course, “Machine Learning Mastery: From Basics to Advanced Techniques,” is designed to empower learners of all levels with the knowledge and skills needed to thrive in this dynamic field.

In this course, we demystify machine learning concepts, starting from the fundamentals and gradually progressing to advanced techniques.

What You’ll Learn:

Understand the fundamentals of supervised and unsupervised learning
Explore popular machine learning algorithms.
Use natural language processing (NLP) with Supervised Machine Learning Algorithms for Sentiment Analysis & Text Classification.
Implement real-world projects using Python and scikit-learn.
Optimize models for accuracy and efficiency.

Why Take This Course?

Practical experience: Learn by doing with hands-on projects and exercises.
Portfolio building: Showcase your skills to potential employers.
Problem-solving: Develop critical thinking skills to tackle real-world challenges.
Continuous learning: Stay updated with the latest advancements in machine learning

Whether you’re a beginner or an experienced data scientist, this course will empower you to create intelligent solutions and make an impact in the field of machine learning. Enroll now and start your journey toward becoming a machine learning pro!

Here’s what you can expect:

Foundational Knowledge:
- Understand the core principles of supervised and unsupervised learning.
- Explore regression, classification, clustering, and dimensionality reduction.
Algorithm Deep Dive:
- Dive into popular machine learning algorithms, including linear regression, decision trees, support vector machines, and neural networks.
- Learn how to choose the right algorithm for specific tasks.
Real-World Applications:
- Apply your knowledge to real-world projects using Python and libraries like scikit-learn.
- Tackle natural language processing (NLP) challenges with Supervised ML Algorithms for Sentiment Analysis & Text Classification.
Model Optimization:
- Discover techniques for model evaluation, hyperparameter tuning, and performance optimization.
- Learn how to avoid common pitfalls and enhance model accuracy.
Career Boost:
- Build a strong portfolio by completing hands-on exercises and projects.
- Gain practical experience that sets you apart in job interviews.
Stay Current:
- Keep pace with the ever-evolving field of machine learning.
- Stay informed about the latest research and trends.

Whether you’re a data enthusiast, aspiring data scientist, or seasoned professional, this course provides a solid foundation and equips you with practical skills. Enroll now and unlock the potential of machine learning!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to Machine learning with scikit-learn.

Lecture 2: Download All The Datasets Used in This Course From Here.

Chapter 2: The Supervised Machine Learning Workflow.

Lecture 1: The Supervised Learning Workflow

Lecture 2: Measuring Model Performance.

Chapter 3: Regression Supervised ML Algorithm.

Lecture 1: Introduction to Regression.

Lecture 2: The Basics of Linear regression & Regression Performance.

Lecture 3: Cross-validation for R-squared & Analyzing Cross-Validation Metrics.

Lecture 4: Regularized Regression.

Chapter 4: Binary Classification & Multiclass ML Supervised Classifiers.

Lecture 1: The k-Nearest Neighbors Classification Supervised ML Learning Algorithms.

Lecture 2: How Good is your Model? and How to use Random Forest Classifier?

Lecture 3: Logistic Regression and the ROC Curve.

Lecture 4: Hyperparameter Tuning with GridSearchCV & RandomizedSearchCV.

Chapter 5: Feature Engineering for ML Supervised Learning Algorithms.

Lecture 1: Preprocessing Data and Creating Dummy Variable for Categorical Data Variables.

Lecture 2: Handling Missing Data And Creating The ML Pipeline.

Lecture 3: Centering and Scaling Techniques in ML Supervised Learning Algorithms.

Chapter 6: How to Evaluate Multiple Models?

Lecture 1: Evaluating Multiple Models with Examples.

Lecture 2: Comparing Models.

Lecture 3: Logistic Regression and Support Vector Machines for Text Classification.

Chapter 7: Advanced Topics regarding ML Supervised Learning Algorithms.

Lecture 1: Linear Classifier & Decision Boundaries.

Lecture 2: Linear Classifiers & The Coefficients.

Lecture 3: What is The Loss Function?

Lecture 4: Loss Function Diagrams.

Lecture 5: Logistic Regression Regularization & Identifying Negative Reviews.

Lecture 6: Logistic Regression and Probabilities.

Lecture 7: Multi-class Logistic Regression.

Chapter 8: Clustering ML Unsupervised Learning Algorithms.

Lecture 1: Unsupervised Learning The Fundamentals of Clustering.

Lecture 2: Clustering Evaluation & Optimization.

Lecture 3: Transforming Features for Better Clustering & Clustering Stocks Using KMeans.

Lecture 4: Hierarchical Clustering & Visualizing Hierarchies.

Chapter 9: t-SNE for 2-dimensional maps.

Lecture 1: t-SNE for 2-dimensional maps.

Chapter 10: PCA ML Unsupervised Learning Algorithm.

Lecture 1: Visualizing The PCA Transformation & Calculating Cumulative Explained Variance.

Lecture 2: Dimension Reduction with PCA.

Lecture 3: Dimension Reduction with PCA and Non-negative Matrix Factorization (NMF).

Chapter 11: Capstone Project: Building Recommender System using NMF.

Lecture 1: Learn How to Building Recommender System using NMF!

Chapter 12: Review All Course Python ML Code From Here.

Lecture 1: Review All Course Python ML Code From Here.

Chapter 13: Bonus

Lecture 1: Thank you.

Instructors

Tamer Ahmed
Passionate Developer, Data Scientist and Data Engineer.

Rating Distribution

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3 stars: 1 votes
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5 stars: 19 votes

Frequently Asked Questions

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

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