Machine Learning for Beginners

Course Content:

Introduction to Machine Learning:
Definition of Machine Learning and its role in artificial intelligence.
Differences between supervised, unsupervised and reinforcement learning.
Applications and use cases of Machine Learning in different sectors.

Mathematical and Statistical Fundamentals:
Introduction to the basic mathematical concepts needed for Machine Learning, including linear algebra, calculus and probability.
Understanding of the fundamental statistical principles underlying Machine Learning algorithms.

Data Preprocessing:
Techniques for data cleaning, transformation and normalization.
Handling of missing, categorical and unbalanced data.
Use of tools and libraries for preprocessing, such as Pandas and Scikit-Learn.

Supervised Learning Algorithms:
Introduction to supervised learning algorithms, including linear regression, logistic regression, decision trees, and support vector machines (SVM).
Implementation and evaluation of models using performance metrics such as accuracy, recall and F1-score.

Unsupervised Learning Algorithms:
Exploration of unsupervised learning techniques, including clustering with K-means and principal component analysis (PCA).
Application of methods for dimensionality reduction and pattern identification in unlabeled data.

Model Evaluation and Validation:
Techniques for evaluating model performance, including cross-validation, partitioning of data into training and test sets, and hyperparameter fitting.
Analysis of overfitting and underfitting, and strategies to improve model generalization.

Introduction to Neural Networks:
Basic concepts of artificial neural networks and their operation.
Implementation of simple neural networks and use of frameworks such as TensorFlow and Keras to create basic deep learning models.

Practical applications of Machine Learning:
Development of practical projects using Machine Learning to solve real-world problems, such as sales predictions, sentiment analysis, and personalized recommendations.
Integration of Machine Learning models in business applications and systems.

Ethics and Considerations in Machine Learning:
Reflection on the ethical implications of Machine Learning, including data bias and privacy.
Best practices to ensure fairness and transparency in the development and application of Machine Learning models.

Final Project:
Participants will develop a hands-on project involving the application of Machine Learning techniques to a real or simulated problem.
The project will be reviewed and evaluated, with detailed feedback from Machine Learning experts.

Additional Benefits:

Certification in Machine Learning for Beginners:
Upon completion of the course, you will receive an industry-recognized certification that validates your proficiency in Machine Learning fundamentals.

Access to Exclusive Tools and Resources:
You’ll gain access to Machine Learning platforms, software libraries, and additional educational resources to facilitate your learning and practical application.

Mentoring and Networking:
During the course, you will have the opportunity to interact with expert Machine Learning mentors and participate in networking events with other industry professionals.

Course duration: 3 months

Modality: 100% online, with live sessions and access to recorded content.