Machine Learning Complete Course for Beginners | ML Tutorial | Machine Learning Career Path

This chapter covers the basics of machine learning, including an overview of the tutorial and what will be covered in the session.

This chapter focuses on the steps to becoming a machine learning engineer, including the required skills, programming skills, and data modeling.

Here, the chapter delves into the application of machine learning algorithms and libraries, highlighting the importance of knowing when and where to apply various algorithms.

This chapter discusses the technical and soft skills required for machine learning engineers, emphasizing the importance of domain knowledge, time management, and teamwork.

This chapter explores various tools and technologies essential for machine learning engineers, such as TensorFlow, Apache Kafka, Weka, and Matlab.

The chapter elaborates on the career scope and salary of machine learning engineers, highlighting the lucrative opportunities in the field.

This chapter differentiates between artificial intelligence and machine learning, explaining their roles and applications. It also touches on the significance of both in the digital world.

This chapter covers tree-based models in machine learning, discussing decision trees, random forests, bagging, and boosting algorithms.

The chapter introduces linear regression, explaining the concept of predictive modeling and demonstrating a practical example with the Boston dataset using R programming.

This section covers filtering houses based on the number of rooms, building a linear regression model, calculating the root mean square error, and evaluating the model accuracy through error calculation and analysis.

Discussion on splitting data into training and testing sets, building linear regression models, making predictions, and evaluating the model's performance using mean squared error in Python.

Explanation of recommender systems, their importance in the digital world, and examples of recommendation engines like Netflix and Amazon.

Introduction to image processing, steps involved, libraries used, and applications of image processing in various fields.

Overview of the three basic aspects of image processing: photography analysis, image management, data compression, and processing techniques.

Distinction between analog image processing used for hard copies like photography and printing and digital image processing requiring pre-processing, copy development, and display.

Encouragement for viewers to like the video, comment on the best image processing library, and a preview of upcoming content.

Discussing the machine learning problem and the importance of coming up with a feature set.

Exploring different algorithms like tree, random forest, neural networks, and deep learning for machine learning problems.

Discussing the justification of architectural decisions and considerations for deployment, including debugging strategies.

Covering system design problems and the importance of data engineering in the context of companies like Facebook and YouTube.

Ramit's background, career journey, involvement in research, and plans for the future.

Certain research-based roles in the U.S pay much more than the normal software engineering track and offer great exposure.

In Europe, masters programs are more affordable compared to the U.S.

Tips on improving research output by choosing between publishing in conferences or journals and getting more visibility for your paper.

Explains the distinction between conferences and journals in terms of session duration, submission processes, and spreading the word about published work.

Discusses the value of publishing interdisciplinary work in fields like biochemistry and health, and the impact it can have in different domains.

Addressing the importance of avoiding plagiarism in research papers and utilizing plagiarism detection tools to ensure academic integrity.

Encouragement to create content through blogs, medium, and LinkedIn, attend online conferences, and focus on making an impact with research work.

Highlighting the opportunities for improving research and machine learning and the potential for upskilling even for those already employed in the field.