Advanced Machine Learning Algorithms from Coursera

In a world where data-driven solutions are revolutionizing industries, mastering advanced machine learning techniques is a pivotal skill that empowers innovation and strategic decision-making. This equips you with the expertise needed to harness advanced machine-learning algorithms. You will delve into the intricacies of cutting-edge machine-learning algorithms. Complex concepts will be simplified, making them accessible and actionable for you to harness the potential of advanced algorithms effectively. By the end of this course, you will learn to:

1. Employ regularization techniques for enhanced model performance and robustness.

2. Leverage ensemble methods, such as bagging and boosting, to improve predictive accuracy.

3. Implement hyperparameter tuning and feature engineering to refine models for real-world challenges.

4. Combine diverse models for superior predictions, expanding your predictive toolkit.

5. Strategically select the right machine learning models for different tasks based on factors and parameters.

What's inside

Syllabus

Getting Familiar with Regularisation

In the fast-evolving field of machine learning, overfitting and underfitting are persistent challenges that can hinder the performance of models. The Regularization module delves deep into the techniques that address these challenges head-on. Over a span of 2 hours, learners will develop a profound understanding of how regularization techniques can enhance model generalization and robustness.

Ensemble Learning - Bagging Algorithms

In this module, learners will explore Bagging Algorithms, which are techniques that group models together for more accurate predictions. Learners will start by learning the basics of Bagging and why it's better. They will discover how these algorithms work and why bootstrapping is a powerful idea. Next, they will dive deeper into types of Bagging Algorithms. They will explore Random Forests, Extra Trees, and how to use Bagging with classifiers.

Ensemble Learning - Boosting Algorithms

In this module, learners will grasp the essence of boosting techniques and their transformative impact on model accuracy. The focus then shifts to AdaBoost, with an exploration of its underlying algorithm and the pivotal role it plays in boosting's iterative approach. Then, they will learn about Gradient Boosting Machines (GBM). The final lesson introduces learners to advanced boosting algorithm variants: XGBoost, LightGBM, and CatBoost.

Feature Engineering and Hyperparameter Tuning

This module navigates learners through the process of refining models for increased performance and precision. They will explore the critical roles that hyperparameter tuning and feature engineering play in model enhancement. They will delve into the significance of datetime features and the techniques to harness text data for improved predictions. Further, they will explore the strategies for optimizing models by carefully selecting features. They will master the art of leveraging techniques like grid search and random search to find optimal parameter configurations.

Combining Models

This module, dedicated to 'Combining Models,' offers learners a concise yet insightful exploration into the realm of leveraging multiple models for superior performance. Learners will explore why mixing models is a great idea. They will delve into fundamental concepts of stacking, blending, and aggregation.

Model Selection

In this module, learners will dive into the important process of picking the right machine learning model for the job. The module begins by showing why choosing the right model matters. Learners will get to know about the factors they need to consider while choosing the model. They will get a handy guide that will help them in selecting the right model. They will learn about the essential things they need to look at while selecting a model, including performance metrics.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides hands-on learning through labs and interactive materials, which enhances learning experiences

Builds a solid foundation for learners who are new to advanced machine learning techniques

Taught by Analytics Vidhya, who are recognized experts in the field of machine learning

Suitable for individuals looking to enhance their skills in machine learning for strategic decision-making

Provides practical knowledge of regularization techniques to improve model performance and robustness

Leverages ensemble methods like bagging and boosting to enhance predictive accuracy

Career center

Learners who complete Advanced Machine Learning Algorithms will develop knowledge and skills that may be useful to these careers:

Machine Learning Architect

Machine Learning Architects are responsible for the design, implementation, and maintenance of machine learning solutions. They may work on projects related to artificial intelligence, big data, or cloud computing. This course would be particularly relevant to Machine Learning Architects who want to learn about advanced machine learning algorithms, such as regularization techniques, ensemble methods, and feature engineering.

See salaries and explore the career path for Machine Learning Architect

Data Scientist

Data Scientists use machine learning and other statistical methods to extract insights from data. They may work on projects related to fraud detection, customer segmentation, or predictive analytics. This course would be particularly relevant to Data Scientists who want to learn about advanced machine learning algorithms, such as regularization techniques, ensemble methods, and feature engineering.

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Machine Learning Engineer

Machine Learning Engineers are responsible for the development and deployment of machine learning models. They may work on projects related to natural language processing, computer vision, or speech recognition. This course would be particularly relevant to Machine Learning Engineers who want to learn about advanced machine learning algorithms, such as regularization techniques, ensemble methods, and feature engineering.

See salaries and explore the career path for Machine Learning Engineer

Quantitative Analyst

Quantitative Analysts use mathematical and statistical methods to analyze financial data. They may work on projects related to risk management, portfolio optimization, or algorithmic trading. This course would be particularly relevant to Quantitative Analysts who want to learn about advanced machine learning algorithms, such as regularization techniques, ensemble methods, and feature engineering.

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Research Scientist

Research Scientists conduct research in the field of machine learning. They may work on projects related to new algorithms, new applications, or new theories. This course would be particularly relevant to Research Scientists who want to learn about advanced machine learning algorithms, such as regularization techniques, ensemble methods, and feature engineering.

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Software Developer

Software Developers design, implement, and maintain software applications. They may work on projects related to web development, mobile development, or enterprise software. This course would be particularly relevant to Software Developers who want to learn about advanced machine learning algorithms, such as regularization techniques, ensemble methods, and feature engineering.

See salaries and explore the career path for Software Developer

Business Analyst

Business Analysts use data analysis and modeling techniques to help businesses make better decisions. They may work on projects related to market research, customer segmentation, or financial planning. This course would be particularly relevant to Business Analysts who want to learn about advanced machine learning algorithms, such as regularization techniques, ensemble methods, and feature engineering.

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Data Analyst

Data Analysts collect, clean, and analyze data. They may work on projects related to data visualization, data mining, or data warehousing. This course would be particularly relevant to Data Analysts who want to learn about advanced machine learning algorithms, such as regularization techniques, ensemble methods, and feature engineering.

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Statistician

Statisticians use statistical methods to analyze data. They may work on projects related to public health, economics, or marketing research. This course would be particularly relevant to Statisticians who want to learn about advanced machine learning algorithms, such as regularization techniques, ensemble methods, and feature engineering.

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Financial Analyst

Financial Analysts use financial data to help businesses make investment decisions. They may work on projects related to stock analysis, bond analysis, or portfolio management. This course would be particularly relevant to Financial Analysts who want to learn about advanced machine learning algorithms, such as regularization techniques, ensemble methods, and feature engineering.

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Operations Research Analyst

Operations Research Analysts use mathematical and statistical methods to solve business problems. They may work on projects related to supply chain management, logistics, or scheduling. This course would be particularly relevant to Operations Research Analysts who want to learn about advanced machine learning algorithms, such as regularization techniques, ensemble methods, and feature engineering.

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Healthcare Analyst

Healthcare Analysts use data analysis techniques to help healthcare organizations improve patient care. They may work on projects related to disease management, population health, or quality improvement. This course would be particularly relevant to Healthcare Analysts who want to learn about advanced machine learning algorithms, such as regularization techniques, ensemble methods, and feature engineering.

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Market Researcher

Market Researchers collect and analyze data to help businesses understand their customers. They may work on projects related to market segmentation, product development, or advertising campaigns. This course would be particularly relevant to Market Researchers who want to learn about advanced machine learning algorithms, such as regularization techniques, ensemble methods, and feature engineering.

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Actuary

Actuaries use mathematical and statistical methods to assess risk. They may work on projects related to insurance, pensions, or investments. This course would be particularly relevant to Actuaries who want to learn about advanced machine learning algorithms, such as regularization techniques, ensemble methods, and feature engineering.

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Economist

Economists use economic data to analyze economic trends and make predictions. They may work on projects related to economic forecasting, monetary policy, or public policy. This course would be particularly relevant to Economists who want to learn about advanced machine learning algorithms, such as regularization techniques, ensemble methods, and feature engineering.

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Advanced Machine Learning Algorithms

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