Online Course – Google Certified Professional Internship in Applied Machine Learning

Improve your machine learning skills with advanced techniques for solving practical problems in data processing, computer vision, and neural networks.

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קורס אונליין - התמחות מקצועית מוסמכת בלמידת מכונה יישומית של Google

Professional Certificate

Beginners

No prior knowledge required

Time to complete the course

7-day free trial

No unnecessary risks

Skills you will acquire in the course

  • Practical machine learning skills
  • Guided learning
  • Network methods
  • Regression analysis
  • Unguided learning
  • Neural networks
  • Image classification
  • Extracting data features
  • Model optimization
  • Convolutional Neural Networks (CNN)
  • Reinforcement learning
  • A priori analysis
  • Solving real problems that drive data
  • Hands-on experience with Jupyter Notebook and PyTorch

What you will learn in the course

Courses for which the course is suitable

  • Data Engineer
  • Data Analyst
  • Machine learning model developer
  • Data Scientist
  • Computer Vision Expert
  • Artificial Intelligence Software Developer
  • Machine Learning Researcher
  • Forecasting Systems Analyst
  • Developer of advanced forecasting solutions
  • Reinforcement learning expert

Zip Code – Series of 3 Courses

This specialization is designed for graduate students who are interested in developing practical machine learning skills that can be applied across a variety of fields. Over three comprehensive courses, key techniques will be explored such as:

  • Guided learning
  • Network methods
  • Regression analysis
  • Unguided learning
  • Neural networks

The courses emphasize hands-on learning, providing the opportunity to apply machine learning to practical problems such as:

  • Image classification
  • Extracting data features
  • Model optimization

Delve into advanced topics such as:

  • Convolutional Neural Networks (CNN)
  • Reinforcement learning
  • A priori analysis

At the end of the internship, you will be well-equipped to tackle complex machine learning challenges in areas such as computer vision and data processing, making you a valuable asset in industries that need advanced predictive models.

Hands-on Learning Project

In this internship, students will work on real-world projects, such as predicting suicide rates using Kaggle data systems. By applying machine learning techniques, students will ingest data, identify important features, and develop predictive models.

They will work on complex challenges, such as determining whether to use classification or regression models, and calibrating machine learning algorithms to find robust methodologies across variables.

Using tools like Jupyter Notebook and PyTorch, learners will gain hands-on experience, creating a functional prototype that solves real data-driven problems.

Details of the courses that make up the specialization

Practical Machine Learning: Techniques and Applications

Course 1

19 hours

What will you learn?

  • Understand and apply machine learning techniques to computer vision tasks, including image recognition and object recognition.
  • Analyze data features and evaluate the performance of machine learning models using appropriate metrics and evaluation techniques.
  • Apply data preprocessing methods to effectively clean, transform, and prepare data for training a machine learning model.
  • Implement and optimize supervised learning algorithms for classification and regression tasks.

Skills you will acquire

  • Data preprocessing
  • Feature engineering
  • Supervised learning
  • Practical application
  • Model evaluation

Advanced methods in machine learning applications

Course 2

19 hours

What will you learn?

  • Understand and apply ensemble methods to improve model accuracy and robustness by combining multiple learning algorithms.
  • Explore advanced regression techniques for predicting continuous outcomes and modeling complex relationships in data.
  • Apply unsupervised learning algorithms for clustering, dimensionality reduction, and pattern recognition in unlabeled data.
  • Understand and apply reinforcement learning and epistemic analysis techniques for decision making and searching for associative rules.

Skills you will acquire

  • Ensemble learning
  • Unsupervised learning
  • Reinforcement learning
  • Epigenetic analysis
  • Advanced regression techniques

Understanding neural networks and model regularization

Course 3

16 hours

What will you learn?

  • Build neural networks from scratch and apply them to real data sets like MNIST.
  • Apply back-propagation to optimize neural network models and understand computational graphs.
  • Utilize L1, L2 regularization, drop-out, and pruning to reduce model overfitting.
  • Implement convolutional neural networks (CNN) and tensors using PyTorch for image and audio processing.

Skills you will acquire

  • Proficiency in PyTorch
  • Regularization techniques
  • Application of neural networks
  • Convolutional Neural Networks (CNN)
  • Control back disposal

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