Hi Mona, below points can help

Domains and Concepts to Study:

• Machine Learning Fundamentals: Understand concepts like supervised learning, unsupervised learning, reinforcement learning, and their applications.
• Statistical Analysis: Learn about probability, statistics, and hypothesis testing for data analysis.
• Data Preprocessing: Master data cleaning, feature engineering, and data normalization techniques.
• Machine Learning Algorithms: Study regression, classification, clustering, and dimensionality reduction algorithms.
• Model Evaluation: Learn about metrics like accuracy, precision, recall, F1 score, ROC curves, and AUC for evaluating models.
• Deep Learning: Explore neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their applications in image recognition, natural language processing (NLP), and more.
• Deployment: Understand how to deploy machine learning models in production environments.

Programming Languages to Learn:

• Python: Essential for machine learning with libraries like NumPy, pandas, scikit-learn, TensorFlow, and PyTorch.
• R: Useful for statistical analysis and data visualization in machine learning projects.
• SQL: Knowledge of SQL is beneficial for working with databases and data manipulation.

Learning Methods:

• Online Courses: Enroll in platforms like Coursera, edX, Udacity, or LinkedIn Learning for structured courses on machine learning.
• Books: Refer to books like "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron or "Pattern Recognition and Machine Learning" by Christopher Bishop.
• Tutorials and Blogs: Follow machine learning blogs, forums, and communities like Towards Data Science, KDnuggets, and Stack Overflow for learning and staying updated.
• Hands-on Practice: Participate in Kaggle competitions, work on personal projects, and collaborate with peers to gain practical experience.

Remember, practice makes perfect. If you'd like more personalized guidance or want to work on these skills together, feel free to book a trial session with me. We can delve deeper into these topics and tailor a plan specifically for you

Machine learning is a rapidly growing field and as a beginner, it's important to build a strong foundation before diving into more complex concepts. Here’s a concise roadmap to help you get started:

• Learn the Basics: Understand the fundamentals of supervised, unsupervised, and reinforcement learning and also get familiar with mean, median, mode, standard deviation, and basic probability concepts.
• Learn Python (No need to learn any other programming language): Learn variables, loops, functions, and data structures. Get hands on with libraries like NumPy and pandas for data cleaning, handling missing values, and feature scaling.
• Hands-on Practice and Projects: Work on simple projects such as predicting house prices or classifying flowers using scikit-learn. I would also recommend participating in beginner-level competitions to apply what you've learned and gain practical experience.

Once you have a solid understanding of these fundamentals and have completed some hands-on projects, you’ll be ready to start applying for internships or entry-level positions in the field of machine learning. This practical experience will help you further develop your skills and prepare you for more advanced challenges in your career. Feel free to book a free trial session with me to dive deeper into any of these topics and get personalized guidance on your learning journey!