Master Course : Fundamentals of Machine Learning (101 level)
What you'll learn
- Foundations of Machine Learning: Preprocessing, Supervised Learning, and Beyond
- Mastering Machine Learning: Unsupervised Techniques, Model Evaluation, and More
- Feature Engineering and Deep Learning: Unlocking the Power of Data
- TensorFlow, Keras, and NLP: Building Bridges to Natural Language Understanding
- Visualizing the Future: Computer Vision, Reinforcement Learning, and Ethical Dilemmas in AI
Requirements
- Basic skills and ideas of machine learning and deep learning
Description
Master Course : Fundamentals of Machine Learning (101 level)
Welcome to the exciting world of machine learning! In this master course, we'll delve into the fundamental concepts of machine learning at a 101 level. Machine learning is a subset of artificial intelligence (AI) that empowers computers to learn from data and make predictions or decisions without explicit programming. Understanding these basics will lay the groundwork for your journey into the vast and ever-evolving field of machine learning.
Machine learning is a branch of AI that focuses on creating algorithms and models that can learn from data. Instead of being explicitly programmed to perform specific tasks, machine learning models can identify patterns and relationships in the data and make decisions or predictions based on those patterns.
Machine learning has the potential to revolutionize various industries and improve decision-making processes. In this master course, we've covered the fundamentals of machine learning at a 101 level, introducing you to key concepts like supervised and unsupervised learning, the machine learning process, and evaluation metrics.
Types of Machine Learning
There are three main types of machine learning:
a) Supervised Learning: In this type, the algorithm learns from labeled data, meaning it's provided with input-output pairs during the training phase. The goal is for the model to learn a mapping function that can predict the output for unseen inputs accurately.
b) Unsupervised Learning: Unlike supervised learning, unsupervised learning works with unlabeled data. The algorithm's objective is to find patterns and structures in the data without explicit guidance. Clustering and dimensionality reduction are typical tasks in unsupervised learning.
c) Reinforcement Learning: This type of learning is inspired by behavioral psychology, where an agent interacts with an environment and learns to take actions that maximize rewards or minimize penalties. The agent explores the environment and learns from the feedback it receives.
The Machine Learning Process
The typical machine learning process involves several key steps:
a) Data Collection: Obtaining relevant and high-quality data is crucial for successful machine learning models. The data should be representative of the problem you want to solve.
b) Data Preprocessing: This step involves cleaning the data, handling missing values, and transforming the data into a suitable format for training the models.
c) Feature Engineering: Selecting and creating relevant features from the data is an essential part of building effective machine learning models. Good features can significantly impact the model's performance.
d) Model Selection: Choosing an appropriate algorithm or model architecture for the task at hand is essential. The choice of model depends on the problem type (classification, regression, etc.) and the nature of the data.
e) Model Training: In this step, the model is exposed to the training data to learn the underlying patterns and relationships. The algorithm adjusts its parameters to minimize the prediction errors.
f) Model Evaluation: Evaluating the model's performance on a separate set of data (validation or test set) is essential to ensure it generalizes well to unseen data and avoids overfitting.
g) Model Deployment: After a successful evaluation, the model can be deployed in a real-world setting to make predictions or decisions.
Evaluation Metrics
To assess the performance of a machine learning model, various evaluation metrics are used, depending on the type of problem. For classification tasks, metrics like accuracy, precision, recall, and F1-score are commonly used. For regression tasks, mean squared error (MSE) and mean absolute error (MAE) are popular metrics.
As you continue your journey into the world of machine learning, remember that practice is crucial. Experiment with different datasets, algorithms, and model architectures to gain hands-on experience. Stay curious, keep learning, and don't be afraid to explore the ever-expanding possibilities of machine learning!
In this master course, I would like to teach the 6 major topics:
1. Foundations of Machine Learning: Preprocessing, Supervised Learning, and Beyond
2. Mastering Machine Learning: Unsupervised Techniques, Model Evaluation, and More
3. Feature Engineering and Deep Learning: Unlocking the Power of Data
4. TensorFlow, Keras, and NLP: Building Bridges to Natural Language Understanding
5. Visualizing the Future: Computer Vision, Reinforcement Learning, and Ethical Dilemmas in AI
6. Model Evaluation and Validation in Data Science and Machine Learning
Enroll now and learn today !
Who this course is for:
- All UG and PG Computer Science and Information Technology and Business Systems Domain Students
- Interested students to learn about the concepts of Fundamentals of Machine Learning (101 level)
Instructor
- 4.2 Instructor Rating
- 36,188 Reviews
- 527,816 Students
- 310 Courses
Achievements on Udemy : (UDEMY BUSINESS BEST SELLER & FULL TIME UDEMY INSTRUCTOR)
[1] Enrolled over 527,000 students from 214 countries & 40+ UFB Courses.
[2] Delivered a staggering 3.73 million course enrollments, with 5,439,933 minutes taught Including UFB Courses (Upto August' 2024).
[3] Offered a diverse array of 310 courses, covering Business, General Management, Leadership, IT, Software and Systems, Psychology, Education, and Media Studies.
Credentials and Qualifications of Dr. José:
Dr. José's extensive qualifications showcase his dedication to various fields:
[1] Earned a Ph.D. in Entrepreneurship & Business Management in 2019, contributing to the understanding of business dynamics.
[2] Holds an M.Phil. in Business & General Management, establishing a solid foundation in comprehensive management knowledge.
[3] Completed a Masters in Financial Markets in 2015, demonstrating expertise in financial systems.
[4] Acquired a Masters in Information Technology in 2013, equipping him with technological proficiency.
[5] Achieved a Masters in Human Resource Management (MBA) in 2011, highlighting organizational skills.
[6] Secured a Bachelors in Mathematics in 2009, emphasizing analytical and statistics prowess.
[7] Earned a Diploma in Teacher Education in 2006, showing dedication to education.
This diverse academic background positions Dr. José as a well-rounded and versatile professional.
Teaching Experience:
Dr. José is an accomplished expert with a wealth of teaching and industry experience:
[1] Currently he is working as a Researcher and Professor of Florida Christian University (FCU Online), Orlando, FL.
[2] Serves as a Senior Researcher, Professor, and Subject Matter Expert at UTEL University, Universidad Centro Panamericano de Estudios Superiores - México, IBS-BFSU (International Business School - Beijing Foreign Studies University) China.
[2] Holds the title of Honorary and Visiting Professor at NGCEF Australia, ISCIP Canada, and Judge of the Research center for the Internationalization of companies from emerging economies (I-CEE) & Global Forum on Business Case Study.
[3] Dr. José, Specializes in digital transformation teaching, innovative teaching, and project based strategies for startup teaching in digital platform ecosystems.
Instructional Design and Curriculum Development Experience:
Dr. José's instructional design and curriculum development contributions are noteworthy:
[1] Designed and developed more than 5 specialized Post Graduate Master Degree courses for International Business School - Beijing Foreign Studies University (IBS-BFSU).
[2] Expertise in corporate entrepreneurship, organizational innovation, and learning.
[3] Over 15 years of experience as a business and IT consultant for over 20 successful startup businesses.
[4] Designed, developed, and hosted over 200 corporate business websites since 2007, receiving over 10 awards for graphic and web design.
Interests of Teaching Topics:
Dr. José's teaching interests span a wide range of subjects:
[1] Conducted, supervised, and handled more than 10,000 UG, PG students since 2007.
[2] Led training sessions for undergraduate and postgraduate Master Degree graduates in areas such as Entrepreneurship Development, Business Management, Human Resource Management and Development, Marketing, Digital Marketing, Finance and Financial Markets, Economics, Statistics, Education, Psychology, Leadership and Personal Development, Multimedia, Information Technology, IEEE Domains, and Software Certification Courses.
[3] Regular Keynote Speaker at AEIC China and Whither Our Economies (WoE) Lithuania.
[4] Currently guiding 5 Ph.D. Scholars in Business Administration, Computer Science, and IT Domains.
Certifications, Research and Publications:
Dr. José boasts an impressive list of certifications, research, and publications:
[1] Completed more than 30+ Software, IT, and Business Certifications.
[2] Serves as an Editor of Science Direct PLAS Journal, Ai Scholar China and certified reviewer for several renowned publishers from Springer Nature, Online Wiley, Emerald & SAGE.
[3] Published over 50 research and review articles, 8-12 solo-authored full case studies, 4 book chapters, and numerous book reviews and some listed in WoS & Scopus Listings.
[4] Google Scholar Citation 100+ and Research Gate Score is 270.90 with 54+ Citations, 59,299+ reads, and 27 Research Recommendations.
[5] European Alliance for Innovation (EAI) Score is 16.
Honors and Awards:
Dr. José's accomplishments are celebrated with various honors and awards:
[1] Top Funding Project Writer.
[2] Top Researcher Award 2019-2024.
[3] Best Ph.D. Thesis Award 2018.
[4] Distinguished Young Scientist 2017.
[5] University Topper (2011-2012).
[6] Seven-time recipient of the FCB - Faculty Competency Building Award (2011-2018).
[7] Mr. Perfect Award in 2008.
International Memberships and Activities:
[1] Holds memberships in 35+ International academic and professional bodies, including IEEE (ID : 96447154), Oulu Entrepreneurship Society - Finland, AfSAE (ID: 256521), ISPIM (ID: 1374), ITPA (ID: 29660) and many more.
[2] Volunteers for Scopus, Web of Science, and ABDC Indexed journals.
[3] Serves as an editorial and reviewer board member for over 30+ journals and has reviewed 200+ articles since 2019.