Description
Machine Learning Bootcamp: Learn, Practice & Build. This course covers machine learning from basic concepts to practical implementation of algorithms with Python and building real projects. This course teaches machine learning concepts from scratch with a structured, step-by-step approach. In this course, learners first learn the mathematics and intuitive understanding behind each algorithm before implementing algorithms in Python. Using real datasets and examining industry-oriented case studies, this course provides the opportunity to gain practical experience and develop problem-solving skills to build comprehensive machine learning solutions. This course is distinguished by its simple explanations, comprehensive theoretical training with implementation, real projects in the fields of regression, classification, clustering, anomaly detection and recommendation systems, as well as providing notes, exercises and downloadable source code. By the end of this course, participants will be able to confidently build and evaluate machine learning models, select the right algorithm for different business problems, and perform feature engineering, dimensionality reduction, and model optimization operations. The course includes over 100 videos with hands-on exercises using tools like Scikit-learn to help individuals build strong portfolios for job interviews in the fields of AI and data science. With over 14 years of experience in the IT industry, the instructor of this course teaches complex concepts in a very simple and practical way so that learners can achieve complete mastery. In addition, the course has designed the learning path in such a way that all technical and practical aspects are covered simultaneously. Learners learn how to preprocess raw data and prepare it for entering complex models. The course’s special focus on preparing individuals for job interviews ensures that concepts are presented along with key industry points. This comprehensive approach helps scholars not only learn how to code models, but also properly understand the logic behind each decision and apply it to real-world projects.
What you will learn
- Mastering Machine Learning Algorithms: Training from fundamental concepts to advanced topics using Python and Scikit-learn.
- Building functional models: Implementing regression, classification, clustering, mixed learning, and anomaly detection models with real data.
- Model evaluation and optimization: using cross-validation, performance evaluation criteria, and hyperparameter tuning.
- Comprehensive project development: Implementation of complete projects such as sentiment analysis, recommendation systems, and model deployment.
- Model performance optimization: applying feature selection methods, dimensionality reduction, and data imbalance management.
- Gain practical experience: Complete over 25 real projects with full source code, notes, and exercises.
- Understanding the mathematics and logic of algorithms: Deep understanding of the mathematics behind models to better interpret the outputs.
- Job Interview Preparation: Practice practical scenarios and industry-based examples to succeed in job interviews.
This course is suitable for people who:
- Students: People who want to learn machine learning from scratch using Python.
- Python Developers: Programmers who want to enter the field of machine learning and artificial intelligence.
- Data analysts: professionals who seek to build predictive models and solve business problems.
- Software Engineers: Individuals preparing for career opportunities in machine learning, artificial intelligence, and data science.
- Working professionals: People who are willing to improve their skills with practical projects.
- AI enthusiasts: Those who want to learn algorithms through mathematics, coding, and intuitive understanding.
- Recent graduates: Students preparing for internships and technical interviews.
- Want to learn machine learning in a hands-on way: Anyone who wants to master it by doing multiple projects and exercises.
Machine Learning Bootcamp: Learn Practice & Build Course Details
- Publisher: Udemy
- Instructor: EduMentor Deepti
- Training level: Beginner to advanced
- Training duration: 51 hours and 46 minutes
- Number of lessons: 115
Course topics
Machine Learning Bootcamp: Learn Practice & Build Course Prerequisites
- Basic knowledge of Python programming (variables, loops, functions, and object-oriented programming) is recommended.
- Basic familiarity with NumPy, Pandas, Matplotlib, and Seaborn will be helpful but is not mandatory.
- A computer running Windows, macOS, or Linux with at least 8 GB of RAM is recommended for practical sessions.
- Python 3.x, Jupyter Notebook (or JupyterLab), and Visual Studio Code or any Python IDE should be installed.
- No prior Machine Learning experience is required—this course starts from the fundamentals and gradually progresses to advanced topics.
- A willingness to learn, practice, and implement Machine Learning algorithms through hands-on projects is the only essential requirement.
Course images
Sample course video
Installation Guide
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Subtitles: None
Quality: 2160p
Download link
File(s) password: www.downloadly.ir
File size
125.4 GB