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About this course
Learn all about machine learning with "Machine Learning A-Z: AI, Python & R." This course, taught by Anand Gupta, a highly-rated instructor in data science, will guide you from beginner to expert. Anand Gupta is a well-respected instructor in data science, known for making complex topics easy to understand. He has extensive experience in the field and has helped many students succeed in data science and machine learning.
Each module includes hands-on projects that allow you to apply your knowledge in real-world scenarios. You'll work with popular programming languages and tools, such as Python and R, gaining practical experience that will be valuable in your career.
What You Will Learn:
1. Introduction to Machine Learning
- Learn what machine learning is and how it's used in the real world.
- Set up your tools, including Python, R, and important libraries.
2. Data Preprocessing
- Discover how to collect, clean, and prepare data for analysis.
- Explore data to find patterns and insights.
- Learn how to create and choose the best features for your models.
3. Supervised Learning
- Understand key algorithms like linear and logistic regression, support vector machines (SVM), decision trees, random forests, and K-nearest neighbors (K-NN).
- See how to use these algorithms in Python and R and evaluate their performance.
4. Unsupervised Learning
- Learn about clustering methods like K-means and hierarchical clustering.
- Understand principal component analysis (PCA) for reducing data dimensions.
5. Advanced Topics in Machine Learning
- Get an introduction to neural networks and deep learning.
- Explore natural language processing (NLP) with ChatGPT.
- Learn how to deploy and optimize machine learning models.
6. Working with R for Machine Learning
- Get started with R programming, including basic syntax and data handling.
- Apply machine learning algorithms in R.
7. Hands-on Projects and Case Studies
- Work on practical projects like predicting house prices, customer segmentation, and sentiment analysis with ChatGPT.
- Build a portfolio with real-world projects.
8. Course Wrap-Up and ChatGPT Prize
- Review the key concepts learned and explore the next steps.
- Participate in the ChatGPT Prize Challenge to show off your skills and win prizes.
By the end of the course, you'll have a deep understanding of machine learning concepts, a portfolio of projects, and the confidence to tackle real-world challenges. Plus, participate in the ChatGPT Prize Challenge for a chance to showcase your skills and win a prize.
Who this course is for:
Is anyone interested in Machine Learning?
Students who have at least high school knowledge in math and who want to start learning Machine Learning.
Any intermediate-level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
Any students in college who want to start a career in Data Science.
Any data analysts who want to level up in Machine Learning.
Any people who are not satisfied with their job and who want to become a Data Scientist.
Any people who want to create added value to their business by using powerful Machine Learning tools.
Join this course and let Anand Gupta guide you through the exciting world of machine learning with AI, Python, and R. Start learning today and take your skills to the next level!
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Introduction to the concept of machine learning, its importance, and real-world applications.
Understanding labeled data, key concepts, and types of problems solved using supervised learning.
Importance of splitting data into training, testing, and validation sets. Techniques like Holdout, Cross-Validation.
Understanding the concept of linear regression, assumptions, model building, and interpretation of results.
Understanding the concept of linear regression, assumptions, model building, and interpretation of results.
Concept of KNN, distance metrics, choosing the right 'K', and hands-on practice.
Understanding the K-Means algorithm, choosing the number of clusters, and hands-on practice with real datasets.
The concept of PCA includes understanding variance, eigenvectors, and eigenvalues, as well as practical implementation.
Understanding key evaluation metrics, confusion matrix, and when to use which metric.
Applying cross-validation on different models to assess their performance.
Introduction to hyperparameter tuning, grid search vs. random search, and practical examples.
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