The salary for a junior machine learning engineer in Austria can vary based on several factors, including the industry, the company's size, the region within Austria, and the individual's educational background and relevant experience. Generally as of my last update, starting salaries for junior machine learning positions in Austria could be in the range of approximately €35,000 to50,000 per year. It's always a good idea look at current job listings and salary surveys for the most up-to-date information.

As for companies in Austria recruiting for junior machine learning engineers, this can also change frequently as new positions open and close. Common industries hire machine learning engineers include technology companies, financial services, healthcare, and manufacturing. To find current job openings, it would be best to consult job boards, LinkedIn, company career pages, or recruitment agencies that specialize in tech placements.

Regarding requirements for junior machine engineer positions, they typically include:

1. Relevant academic background, such as a Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, or a related field.
2. Understanding of machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn).
3. Strong programming skills, particularly languages such as Python or R.
4. Familiarity with data manipulation and processing.
5. Basic knowledge of algorithms and statistics.
6. Sometimes, exposure to cloud platforms such as AWS, Google Cloud, or Azure is desired.
7. Good communication skills and the ability to work well in a team.

It's essential to check the specific job descriptions for each role, as the requirements and expectations can vary between and even from one job posting to another within the same company.

Creating a comprehensive roadmap to becoming a machine learning engineer in 1.5 years is quite ambitious, but with dedication and structured learning, it is feasible. Below is a structured plan broken into weeks. Please note that this schedule is aggressive and assumes a full-time commitment to learning.

Weeks 1-4: Foundations

• Learn Python programming (focus on data structures, control flow, and object-oriented programming).
• Start a GitHub account to document your progress and projects.
• Complete online Python tutorials and exercises.

Project: Basic Python scripting to solve mathematical problems.

Weeks 5-8: Intermediate Python & Intro to Data Science

• Learn about data manipulation with pandas.
• Learn data visualization with matplotlib and seaborn.
• Introduction to statistical analysis for data science.

Project: Data cleaning and visualization project with a dataset from Kaggle.

Weeks 9-14: Mathematics for Machine Learning

• Study linear algebra, probability, and statistics, which are foundational for understanding machine learning algorithms.
• Use Khan Academy, Coursera, or edX courses for this.

Project: Implement statistical analysis on a dataset.

Weeks 15-20: Machine Learning Fundamentals

• Study machine learning basics with online platforms such as Coursera's "Machine Learning" by Andrew Ng.
• Learn about different types of machine learning: supervised, unsupervised, and reinforcement learning.

Project: Implement simple machine learning models using scikit-learn.

Weeks 21-26: Deep Learning and Neural Networks

• Introduction to neural networks and deep learning.
• Study using TensorFlow and Keras.

Project: Simple image classification using a convolutional neural network (CNN).

Weeks 27-35: Specialization and Advanced Topics

• Choose a specialization: Natural Language Processing (NLP), Computer Vision, or another area of interest.
• Take advanced courses in your chosen specialization.
• Learn about model evaluation, tuning, and deployment.

Project: Complete a project related to your specialization area using relevant datasets.

Weeks 36-44: Real-World Data and Advanced Techniques

• Participate in a Kaggle competition related to your specialization.
• Study big data technologies such as Apache Spark or Hadoop.
• Learn about MLOps practices.

Project: Enter and compete in a Kaggle competition.

Weeks 45-60: Advanced Projects and Portfolio Building

• Develop complex projects that show your ability to handle real-world data and problems.
• Work on projects that require you to scrape data, clean it, and develop a machine learning or deep learning model.
• Continue to build your GitHub portfolio with these projects.

Week 61: Preparation for Job Search

• Polish your resume, LinkedIn profile, and GitHub portfolio.
• Prepare for technical interviews by practicing with questions from LeetCode, HackerRank, etc.

Weeks 62-66: Job Applications and Continued Learning

• Begin applying for junior machine learning positions.
• Network with professionals in the field through meetups, LinkedIn, and forums.
• Begin a blog or vlog to showcase your understanding and projects.

Weeks 67-78: Interview Preparation and Practice

• Continue applying to positions and networking.
• Focus on mock interviews, coding challenges, and machine learning case studies.
• Improve your understanding of system design and machine learning in production.

Project: End-to-end machine learning project that includes data preprocessing, model building, model deployment, and monitoring.

Continuously throughout this timeline:

• Document and reflect on your learning.
• Engage with the community: join forums, contribute to open source, attend webinars.
• Stay updated with the latest machine learning research and technologies.

Remember, flexibility in your learning process is essential. Adjust the timeline as needed based on progress and comprehension. Good luck!

Here's a list of resources suitable for the different stages of the timeline to become a Machine Learning Engineer:

Weeks 1-4: Foundations

• Python Programming: Codecademy, Learn Python the Hard Way, Python official documentation.
• Version Control: GitHub Guides, "Pro Git" book.

Weeks 5-8: Intermediate Python & Intro to Data Science**

• ** Manipulation:** "Python for Data Analysis" by Wes McKinney. -Data Visualization:** "Data Visualization with Python and JavaScript" by Kyran Dale.
• Statistical Analysis: "Think Stats" by Allen B. Down (free).

Weeks 9-14: Mathematics for Machine Learning

• Linear Algebra: "Introduction to Linear Algebra" by Gilbert Strang, Linear Algebra Khan Academy.
• Statistics and Probability: "Statistics" by Robert S. Witte, Probability and Statistics Khan Academy.

Weeks 15-20: Machine Learning Fundamentals

• Machine Learning Course: Andrew Ng's Machine Learning on Coursera.
• Supplemented Material: "Pattern and Machine Learning" by Christopher M. Bishop.

Weeks 21-26: Deep Learning and Neural Networks

• Deep Learning: "Deep Learning" by Ianfellow, Yoshua Bengio, and Aaron Courville DeepLearning.AI's Deep Learning Specialization on Coursera.
• **Tools TensorFlow tutorials, Keras documentation.

Weeks 27-35: Special and Advanced Topics

• Courses for Specialization: Udemy, Courser, edX, Fast.ai for more in-depth specialization courses. -Model Evaluation:** "Evaluating Machine Learning Models" by Alice Zheng.

Weeks 36-44: Real-World and Advanced Techniques

• Big Data Technologies: Udemy "Apache Spark with Scala - Hands On with Big Data!", Coursera's "Big Data Specialization".
• MLOps: "Introducing MLOps" by Mark Treveil, and online materials/official documentation from MLflow, Kubeflow, TFX.

Weeks 45-60: Advanced and Portfolio Building

• Github Portfolio: "Building Data Portfolios" by Julien Kervizic.
• Project Ideas: Kaggle project discussions, "Machine Learning Yearning" by Andrew Ng.

Week 61 Preparation for Job Search

• Resume and LinkedIn Profile: "Writing a Winning Resume" by Matthew T. Cross, LinkedIn Learning Courses.
• Technical Interview Preparation: "Cracking the Coding Interview" by Gayle Laakmann McDowell.

s 62-66: Job Applications and Continued Learning

• Networking: LinkedIn, local or virtual meetups through Meetup.com, AI conferences.
• Blog/Vlog: Medium for blogging YouTube for vlogging about your learning journey.

Weeks67-78: Interview Preparation and Practice

• Mock Interviews: Pramp, Interviewing.io.
• Coding Challenges: LeetCode, HackerRank.
• System Design: "Designing Data-Intensive Applications" by Martin Kleppmann.

Remember to also take advantage of free resources, such as arXiv for keeping up with the latest research, and online documentation for various tools and libraries you'll use. Networking GitHub by contributing to open-source projects not only teaches you real-world coding skills but can also connect you with like-minded professionals.

Consistency is key; plan your days to include dedicated study times and balance them with project work and real-world applications solidify your understanding.

Of course, here are some free resources you can use to learn about machine learning and data science:

1. Coursera's Machine Learning Specialization by Andrew Ng: This is an excellent introduction to machine learning and covers the basics of Python programming, linear algebra, and statistics.
2. Stanford University's Machine Learning Course by Andrew Ng and Michael I. Jordan: This is another excellent course that covers the basics of machine learning and introduces you to more advanced topics such as neural networks and deep learning.
3. Python Data Science Handbook by Jake VanderPlas: This is a free online book that covers the basics of data science and machine learning using Python. It includes hands-on examples and exercises to help you practice your skills.
4. DataCamp's Machine Learning Courses: DataCamp offers a range of free and paid machine learning courses that cover topics such as supervised and unsupervised learning, linear regression, and neural networks.
5. Kaggle: Kaggle is a platform for data science competitions and hosting datasets. It also offers a range of free tutorials and resources for learning machine learning and data science.
6. OpenCV: OpenCV is a library for computer vision and image processing. It offers a range of free tutorials and resources for learning computer vision and image processing techniques.
7. TensorFlow Tutorials: TensorFlow is an open-source machine learning library developed by Google. It offers a range of free tutorials and resources for learning TensorFlow and machine learning.
8. Data Science Handbook by Jake VanderPlas: This is a free online book that covers the basics of data science and machine learning using Python. It includes hands-on examples and exercises to help you practice your skills.
9. edX's Machine Learning Course by MIT: This is an excellent course that covers the basics of machine learning and introduces you to more advanced topics such as neural networks and deep learning.
10. YouTube Channels: There are a number of YouTube channels that offer free machine learning and data science tutorials, such as Machine Learning Mastery, Data Science, and Python Tutor.

I hope these resources help you in your journey to learn machine learning and data science!