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Keep shoppingMachine Learning 0 to 100 | python | Artificial Intelligence
48 students
Machine Learning 0 to 100 | python | Artificial Intelligence
ML | Supervised Learning | Unsupervised Learning | ANN | Regression | Gradient Descent in ML | overfitting
Created byGhazal Lalooha
Last updated 2/2025
English
What you'll learn
- Understand the Fundamentals of Machine Learning: Define ML and differentiate it from traditional programming, explain the types of ML: Supervised, Unsupervised
- Identify Real-World applications of ML:Recognize various applications of ML across different industries, Discuss case studies of successful ML implementations.
- Grasp key ML terminology and concepts: Understand key terms: features, labels, models, training, testing, overfitting, underfitting, model evaluation metrics
- Comprehend Various ML Algorithms: Learn the principles of different algorithms (linear regression, decision trees, support vector machines and neural networks)
- Master data collection and preprocessing techniques: Learn methods for gathering data from various sources, understand techniques for cleaning, normalizing data
- Implement data visualization for insights: Create and interpret visualizations using libraries such as Matplotlib, Use visual tools to identify patterns,trends
- Develop proficiency in Python for ML: write efficient and readable python code using best practices, Utilize key Data Science libraries (NumPy,pandas,...)
- Evaluate and interpret ML models: Utilize metrics(accuracy, precision, recall, F1-score and ROC-AUC), Conduct thorough error analysis
- Implement model tuning techniques: Apply cross-validation, grid search, and random search to find optimal model parameters,Use ensemble models(bagging,boosting)
- Address Common Issues in ML:Identify and solve problems(data imbalance, high dimensionality, and multicollinearity),Implement techniques to handle missing data
- Understand the Deployment of ML models: Learn the steps and best practices for deploying models into production environments
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Coding Exercises
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Course content
2 sections • 20 lectures • 5h 39m total length
Requirements
- familiarity with Python programming language
- familiarity with linear algebra (recommended but not needed)
Description
Mastering Machine Learning from concepts to real-world coding.
Unlock the power of Machine Learning and accelerate your career with this comprehensive course, designed to bridge the gap between theory and practical applications. Whether you're an aspiring data scientist, a seasoned developer, or a business professional looking to harness the capabilities of Machine Learning, this course will equip you with the tools and knowledge you need to excel.
What you will learn:
-Foundation of Machine Learning: You will understand the core concept and terminology
-Algorithms and Models: You will dive deep into various Machine Learning algorithms
-Data preprocessing: You will master techniques like Data Cleaning, Normalization, Feature Engineering, and Feature Selection to prepare your data for analysis.
-Practical coding exercise: You will gain proficiency in Python and essential data science libraries.
-Model evaluation and tuning: You will learn to evaluate and interpret models using metrics like accuracy, precision, recall, and ROC-AUC. You will learn to Tune your model using cross-validation and hyperparameter optimization.
-Real-world coding exercises: You will apply what you've learned through hands-on projects that simulate real-world scenarios, from data collection to model deployment.
-Problem-solving techniques: You will address common issues like data imbalance, high dimensionality, and overfitting with practical solutions.
Course Highlights:
-Interactive lessons and real projects
-Expert instructions
-Hands-on approach
-Comprehensive Resources
Who this course is for:
- everyone who wants to get promotion in his job(in every field of work)
- everyone who doesn't want to be laid of his/her position at work