PhD in Physics with 10 years of work experience in Data Science and Machine Learning. Former Researcher at LIGO (Nobel Prize in Physics, 2017).
I have been studying, teaching, and implementing cutting edge Data Science and Machine Learning in all organizational levels in the world-class universities and top tech companies.
Born in Iran, live in the USA, I truly enjoy helping people grow in their careers. Most of my clients are new to the US tech job market and need help to fit in fast and smoothly. And I can help with that!
Hit me up if you are interested. I'd love to learn about you and your goals in a 30-minutes FREE 1on1 session. Schedule yours now: https://calendly.com/saeedmirshekari/introduction
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My Mentoring Topics
- Becoming Data Scientist [an end-to-end perspective]
- Data Science [technical]
- Machine Learning [technical]
- Python [technical]
- Careers in Data Science [informational]
- Transition from Academia to Industry [informational]
The Elements of Statistical Learning - Data Mining, Inference, and Prediction
Trevor Hastie, Robert Tibshirani, Jerome Friedman
Here are some of the most important insights from the book: 1. **A strong emphasis on concepts**: The book provides a comprehensive overview of the field of statistical learning, with a particular focus on understanding the underlying concepts and principles, rather than just presenting a set of techniques. 2. **Real-world applications**: The authors use numerous real-world examples and case studies to illustrate how the concepts and techniques discussed in the book can be applied in practice. 3. **In-depth discussion of key techniques**: The book provides detailed explanations of key techniques in statistical learning, including regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and neural networks. 4. **Mathematical rigor**: The book is rigorous in its approach, with a strong emphasis on mathematical foundations. However, it also provides intuitive explanations for those less comfortable with mathematics. 5. **Focus on prediction**: One of the central themes of the book is the role of prediction in statistical learning, and the authors discuss various methods for assessing predictive accuracy. 6. **Emphasis on data mining**: The authors discuss the role of data mining in statistical learning, and provide guidance on how to mine data for useful patterns and insights. 7. **Introduction to inference**: The book provides an introduction to inference in the context of statistical learning, and discusses how to draw valid conclusions from data. 8. **Discussion of model selection and model assessment**: The authors discuss the important issue of model selection, and provide guidance on how to assess the performance of different models. 9. **Use of R and other software**: The book includes numerous examples and exercises using R and other software, which helps readers to gain practical experience with the techniques discussed in the book. 10. **Discussion of recent developments**: The authors discuss recent developments in the field of statistical learning, including deep learning, big data, and other emerging topics. 11. **Accessible to a wide audience**: While the book is rigorous and comprehensive, it is also accessible to a wide audience, and can be used by students, researchers, and practitioners alike. In-depth Analysis "The Elements of Statistical Learning" is a comprehensive and authoritative guide to the field of statistical learning, written by three of the leading experts in the field. The book provides a thorough introduction to the concepts and methods of statistical learning, with a particular focus on prediction and inference. One of the key strengths of the book is its emphasis on understanding the underlying concepts and principles. Rather than simply presenting a set of techniques, the authors provide detailed explanations of how these techniques work, and why they are useful. This approach helps to demystify the field of statistical learning, and makes the book accessible to readers with a wide range of backgrounds. The book also stands out for its practical orientation. The authors use numerous real-world examples and case studies to illustrate the concepts and techniques discussed in the book. These examples help to bring the material to life, and provide readers with a clear sense of how statistical learning can be applied in practice. Another notable feature of the book is its mathematical rigor. The authors do not shy away from the mathematical foundations of the techniques discussed in the book, and provide detailed derivations and proofs where appropriate. At the same time, they also provide intuitive explanations for those less comfortable with mathematics, which helps to make the material accessible to a wide audience. The book places a strong emphasis on prediction, which is one of the central themes of statistical learning. The authors discuss various methods for assessing predictive accuracy, and provide guidance on how to choose the best method for a given problem. In addition to prediction, the book also covers the important topic of inference. The authors provide an introduction to inference in the context of statistical learning, and discuss how to draw valid conclusions from data. This is a crucial skill in many fields, including science, medicine, economics, and social sciences. The book also provides a comprehensive discussion of key techniques in statistical learning, including regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and neural networks. Each of these techniques is explained in detail, with examples and exercises to help readers gain practical experience. One of the unique features of the book is its discussion of data mining. The authors discuss the role of data mining in statistical learning, and provide guidance on how to mine data for useful patterns and insights. This is an increasingly important skill in the era of big data, and the authors provide valuable guidance on how to approach this task. Finally, the book is notable for its discussion of recent developments in the field of statistical learning. The authors discuss emerging topics such as deep learning and big data, and provide a glimpse of the future of the field. Conclusion "The Elements of Statistical Learning" is a comprehensive, rigorous, and practical guide to the field of statistical learning. Whether you are a student, a researcher, or a practitioner, this book will provide you with a deep understanding of the concepts and techniques of statistical learning, and equip you with the skills to apply these techniques in practice.View
An Introduction to Statistical Learning - with Applications in R
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Key Facts and Insights from "An Introduction to Statistical Learning - with Applications in R" Emphasis on Statistical Learning: The book focuses on statistical learning, a field that intersects with data science, statistics, and machine learning. Practical Applications: The book uses R, a popular programming language for data analysis, to demonstrate the concepts. Comprehensive Coverage: The book covers a wide range of concepts, from simple linear regression to more complex machine learning algorithms. In-depth Explanation: The authors provide detailed explanations and derivations of all significant algorithms and concepts. Real-World Examples: The book uses real-world datasets to illustrate the application of different statistical learning methods. Visual Illustrations: Graphical visualizations are liberally used throughout the book to enhance understanding. End-of-Chapter Exercises: Each chapter concludes with exercises that reinforce the concepts covered and help readers to apply them practically. Accessible Style: The authors aim to make the material accessible to readers with varying levels of mathematical background. Interdisciplinary Approach: The book draws on several disciplines, including computer science, statistics, and information theory. Emphasis on Understanding Over Memorization: The book stresses understanding the underlying principles of statistical learning rather than simply memorizing formulas and algorithms. Focus on Modern Methods: The book focuses on modern statistical learning methods, reflecting current best practices in the field. Detailed Summary and Analysis "An Introduction to Statistical Learning - with Applications in R" is a comprehensive guide to statistical learning, a discipline that lies at the intersection of statistics, data science, and machine learning. The authors, all of whom are renowned in the field, provide a rigorous yet accessible introduction to the subject, emphasizing understanding over rote memorization. The book starts with an introduction to statistical learning, discussing its importance and applications. It then dives into the heart of the subject, covering a broad range of topics, from simple linear regression to more complex machine learning algorithms. The authors take a deep dive into each topic, providing detailed explanations and derivations that will be invaluable to readers looking to gain a solid understanding of statistical learning. One of the standout features of the book is its use of R, a popular programming language for data analysis. All concepts and methods are illustrated with R code, allowing readers to see the practical application of the theories being discussed. This hands-on approach will be particularly useful for readers who learn best by doing. Another key strength of the book is its use of real-world datasets. Instead of relying on hypothetical examples, the authors use datasets from actual research studies to illustrate the application of different statistical learning methods. This not only makes the material more relatable but also demonstrates how statistical learning can be applied to solve real-world problems. The authors also make extensive use of graphical visualizations, which greatly enhance understanding. By presenting data and concepts visually, they make complex ideas more accessible and easier to grasp. This, combined with their clear and engaging writing style, makes the book a pleasure to read. Each chapter concludes with exercises that reinforce the concepts covered and provide an opportunity for readers to apply what they have learned. These exercises, along with the practical examples and R code, ensure that readers gain not just a theoretical understanding of statistical learning, but also the practical skills needed to use these methods in their own work. The book's interdisciplinary approach is another of its strengths. The authors draw on several disciplines, including computer science, statistics, and information theory, to provide a well-rounded introduction to statistical learning. This broad perspective will be particularly valuable to readers looking to apply statistical learning in a variety of contexts. In conclusion, "An Introduction to Statistical Learning - with Applications in R" is a comprehensive, accessible, and practical guide to statistical learning. Whether you're a student, researcher, or professional, this book will equip you with the knowledge and skills you need to understand and apply statistical learning methods. Regardless of your mathematical background, you'll find this book a valuable resource for learning about this important and rapidly evolving field.View
Together is Better - A Little Book of Inspiration
Key Facts and Insights from the Book: Importance of a Shared Vision: The book emphasizes the importance of a shared vision within a team or an organization. Value of Trust: Trust is a fundamental factor in successful relationships, whether personal or professional. Role of Leadership: Leadership is not about being in charge, it's about taking care of those in your charge. Power of Cooperation: Achieving goals is often more effective when done through cooperation rather than competition. Significance of Fulfillment: Fulfillment comes from the journey, not the destination. Essence of Inclusion: Sinek emphasizes the importance of inclusion and diversity in a team. Emphasis on Continuous Learning: The book stresses the necessity of continuous learning and development. Value of Authenticity: Authenticity in one's work and relationships leads to lasting success and satisfaction. Importance of Embracing Change: Change, though often uncomfortable, is a necessary part of growth and progress. Relevance of Empathy: Empathy plays a crucial role in understanding others and fostering a positive environment. Role of Optimism: Optimism is a powerful tool that can help individuals and teams overcome challenges and achieve their goals. In-Depth Analysis and Summary: "Together is Better: A Little Book of Inspiration" by Simon Sinek is a masterful exploration of the principles and values that contribute to successful relationships, organizations, and leadership. The book, crafted in a unique and engaging format, combines inspiring quotes, short narratives, and beautiful illustrations to convey its profound insights. Fundamental to Sinek's ideas is the importance of a shared vision. The author posits that a shared vision creates a sense of purpose and direction, fostering unity and cooperation within a team or organization. This concept aligns with the principle of Transformational Leadership Theory, which centers on a leader's ability to inspire and motivate through a shared vision. The book also underscores the value of trust. Trust, as Sinek defines it, is the confidence born of the character and competence of a person or an organization. This perspective resonates with the principles of Trust Theory, which emphasizes the role of trust in facilitating cooperation and promoting positive interpersonal relationships. Sinek's take on leadership is also noteworthy. He argues that true leadership is about taking care of those in your charge, not being in charge. This idea is reminiscent of Servant Leadership Theory, which suggests that leaders should serve their followers rather than the other way around. The book also highlights the power of cooperation. Sinek argues that we can achieve more when we work together rather than compete against each other. This thought aligns with Social Interdependence Theory, which posits that cooperative efforts lead to higher group productivity and individual achievement. Fulfillment, according to Sinek, comes from the journey, not the destination. This idea aligns with the principle of Process Theory of Motivation, which suggests that motivation is an ongoing process, not a one-time event. Sinek also emphasizes the importance of inclusion and diversity in a team, which aligns with the principles of Inclusion Theory. He suggests that diverse teams tend to be more innovative and productive. The book also stresses the necessity of continuous learning and development, reflecting the principles of Lifelong Learning Theory. Sinek suggests that learning should be an ongoing process, allowing individuals and organizations to adapt and evolve. Authenticity, according to Sinek, is key to lasting success and satisfaction. This concept resonates with Authentic Leadership Theory, which advocates for leaders to be genuine and true to their values. Sinek also encourages readers to embrace change, a necessary part of growth and progress. This idea aligns with Change Management Theory, which suggests that embracing change is vital for organizational success. Lastly, the book highlights the role of empathy and optimism in understanding others and overcoming challenges. These ideas align with Emotional Intelligence Theory and Positive Psychology, respectively. In conclusion, "Together is Better: A Little Book of Inspiration" offers a wealth of insights on leadership, teamwork, and personal growth. Drawing from various theories and concepts, Sinek presents a compelling case for the power of collaboration, trust, and shared vision. It's a must-read for anyone looking to foster positive relationships and create a culture of success and fulfillment.View