Embark on a two-month journey into the heart of Machine Learning with our hands-on course, where practicality meets the cutting edge. Ideal for math and code enthusiasts or anyone passionate about modern technology, this program focuses on supervised learning and neural networks, paving the way for you to comprehend machine learning fundamentals and implement algorithms using Python libraries.

Admission Requirements

• Programming Experience: Some familiarity with Python is preferred, but solid knowledge in any programming language is sufficient.
• Mathematics Proficiency: Comfort with high-school level mathematics, including derivatives, matrix, and vector operations.
• Intermediate English: A good understanding of English for effective course engagement.

Course Modules Highlights

• Introduction: Machine learning fundamentals and programming environment.
• Linear Regression: Multiple features, regularization, and basic algorithms.
• Logistic Regression: Binary and multiple class classification.
• Tree Algorithms: Using scikit-learn and xgboost libraries.
• Recommender Systems: Unsupervised learning, clustering, anomaly detection.
• Neural Networks: Fundamentals, TensorFlow, Keras, and Google Colab.
• Image Recognition: Transfer learning with popular networks.
• NLP and RNN: Natural Language Processing and recurrent neural networks.
• ML Ops Overview: Deploying models with TensorFlow-serving and Docker.
• Advanced Topics: Convolutional neural networks, object detection, transformers, and more.

The Learning Experience

• Flipped Classroom: Theory self-study, practical application in live classes.
• Live Classes: Engage with expert teachers, up to 15 students per class.
• Career Support: CV crafting, interview preparation, and job market navigation.

Ready to unlock the potential of Machine Learning? Apply now and dive into the future of tech innovation!