Machine Learning Fundamentals
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60,000+
Learners -
4+
Hours -
75
Lessons -
120+
Practice Q&A -
Flexible
Learning
Trusted by employees at leading organizations
Begin Your AI Career on a Strong Foundation of Machine Learning Expertise
Dive into the world of machine learning with 101 Blockchains under the guidance of industry experts.
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Understand the implications of different types of machine learning techniques in the real world.
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Develop the skills required to use ML algorithms in predictive analytics and optimization.
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Acquire fluency in applying different evaluation metrics to achieve better performing and reliable models.
Pursue Diverse AI Careers with a Machine Learning Fundamentals Course
Machine learning is one of the important disciplines that you need to learn for becoming an AI expert. Our new machine learning course is a comprehensive resource to gain insights on the fundamentals of machine learning and applications. The significance of machine learning in the radially expanding AI ecosystem revolves primarily around the focus on training machines to think, work, and act like humans. Machine learning offers different modalities to train machines for specific tasks with the help of massive datasets. As one of the crucial subdomains of AI, machine learning builds the foundations for AI applications of the future.
The new machine learning fundamentals course on 101 Blockchains offers guidance on using different types of ML techniques and algorithms. This course aims to provide a practical perspective on the applications of machine learning by emphasizing real-world problems. The modular distribution of topics covered in the new ML fundamentals training course aims to help learners familiarize with the basic concepts of machine learning instantly.
Learners can discover lessons on basic concepts of machine learning and its different types such as supervised, unsupervised and reinforcement learning. The course also sheds light on the different ML techniques and algorithms such as neural networks, decision trees and support vector machines. You will also learn the best practices to use evaluation metrics and techniques for model validation. Upon completing the course, learners can achieve practical fluency in the use of machine learning algorithms and techniques.
WHAT YOU WILL LEARN
Brush Up Your Machine Learning Knowledge by Learning…
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Basic Machine Learning Concepts
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Fundamentals of Supervised Learning
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Application of Unsupervised Learning
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Reinforcement Learning
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Core Machine Learning Techniques
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Methods for Evaluating and Validating ML Models
Push Your AI Career to New Heights with ML Skills Like…
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In-depth understanding of machine learning fundamentals
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Real-world uses of supervised learning
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Ways to use unsupervised learning
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Practical applications of reinforcement learning
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Fluency in using ML algorithms like neural networks
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Utilizing model evaluation and validation with metrics
Course Curriculum
- A message from the instructor
- Disclaimer
- Download Course Presentation
- Module Overview
- What is Machine Learning?
- History and Evolution of Machine Learning
- Types of Machine Learning
- Applications of Machine Learning
- Key Terminologies in Machine Learning
- How Machine Learning Works
- Challenges in Machine Learning
- Hands-on Exercise: Load and Explore a Dataset
- Module Summary
- Interactive Exercise
- Module Overview
- Overview of Supervised Learning
- Linear Regression
- Hands-on Exercise: Linear Regression
- Logistic Regression
- Hands-on Exercise: Logistic Regression
- Decision Trees
- Support Vector Machines (SVM)
- Hands-on Exercise: SVM
- K-Nearest Neighbors (KNN)
- Model Evaluation in Supervised Learning
- Module Summary
- Interactive Exercise
- Module Overview
- Overview of Unsupervised Learning
- Clustering: K-Means
- Hands-on Exercise: K-Means Clustering
- Clustering: Hierarchical Clustering
- Hands-on Exercise: Hierarchical Clustering Exercise
- Association Rule Learning
- Principal Component Analysis (PCA)
- Anomaly Detection
- Model Evaluation in Unsupervised Learning
- Module Summary
- Interactive Exercise
- Module Overview
- Introduction to Reinforcement Learning
- Key Concepts in Reinforcement Learning
- Markov Decision Processes (MDPs)
- Hands-on Exercise: MDPs
- Exploration vs. Exploitation
- Q-Learning
- Deep Q-Networks (DQNs)
- Applications of Reinforcement Learning
- Module Summary
- Interactive Exercise
- Module Overview
- Introduction to Regression & Classification
- Simple Linear Regression
- Multiple Linear Regression
- Logistic Regression for Classification
- Genetic Algorithms
- Hands-on Exercise: Genetic Algorithm
- Module Summary
- Interactive Exercise
- Module Overview
- Introduction to Model Evaluation
- Training and Testing Data
- Cross-Validation Techniques
- Confusion Matrix
- Hands-on Exercise: Model Evaluation
- Precision, Recall, and F1 Score
- ROC Curve and AUC
- Bias-Variance Tradeoff
- Hands-on Exercise: ROC-AUC and Bias Variance Tradeoff
- Module Summary
- Interactive Exercise
- Module Overview
- Course Summary
- Final Exam and Next Steps
- Exam Details
- Final Exam
- Bonus: Download Machine Learning Glossary
MAKE YOUR OWN WAY WITH MACHINE LEARNING EXPERTISE - WHO SHOULD JOIN?
Software developers or software engineers can use the course to add new skills to their professional portfolio.
Information technology professionals can discover new ways to design and create better solutions with ML.
Innovation managers and entrepreneurs should try this course to discover innovative business ideas for ML.
Individuals who want to work with machine learning and AI will get a promising start for their career with this course.
PURSUE EXCELLENCE IN YOUR ML AND AI CAREER - CLOSE THE SKILL GAP
The machine learning course for beginners is a trusted learning resource for anyone seeking a breakthrough in the world of AI. Machine learning is one of the building blocks of the massive artificial intelligence landscape. With our new course on ML fundamentals, you can learn how to use machine learning techniques to solve real-world problems. The practical experience offered in this course will empower every learner to apply their knowledge in different machine learning use cases.
Enroll Now
Bonus Materials
Course Presentation
Download the course presentation and access it anytime, anywhere.
Additional Lectures
Get access to additional lectures and improve your skills even more.
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Machine Learning Fundamentals Course FAQs
The important topics in the new ML course include supervised learning, unsupervised learning, reinforcement learning, core machine learning techniques and best practices for evaluating and validating ML models.
The machine learning fundamentals course does not have any deadline. It is a self-paced training course that you can complete conveniently according to your schedule.
The ML course online does not have any specific prerequisites for enrolment. You can have a better learning experience in the course with prior knowledge of important machine learning terms. Professionals with fluent understanding of data science concepts can also have an additional edge in the course.
Yes, you can learn machine learning fundamental concepts without any worries with unrestricted expert support. Learners can find timely and relevant solutions to their doubts that are easy to understand, thereby avoiding obstacles in their learning journey.
Supervised learning is a machine learning approach that uses labeled datasets for training algorithms. On the other hand, the unsupervised learning approach uses unlabeled datasets to recognize hidden data patterns.
The machine learning course for beginners is a trusted pick for business owners to empower their workforce with ML skills. Business owners can use this course to help their employees learn the fundamental concepts and practical applications of machine learning.
The machine learning course is the ideal pick for software developers and software engineers as well as IT professionals. Innovation managers and entrepreneurs can also gain new business ideas with ML after completing the course. Anyone interested in AI and ML should take this course to enhance their skills.
The ML fundamentals course helps your career by providing a gateway into an emerging field of technology. Learners can use the machine learning course with certificate to showcase their expertise in ML algorithms, models and techniques. Employers are likely to trust candidates with professional machine learning training that will translate into benefits for their career.
Yes, the ML Fundamentals course is available with the Standard and Premium Plans. Both the pricing plans of 101 Blockchains offer unlimited access to all the training courses in our library.
The machine learning algorithm is any software program that can detect hidden patterns in data and make predictions. The program must also have the capability to improve its own performance by learning through experience.
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