Applied data mining & machine learning

Data Mining & Applied Machine Learning

$100 – $200

  • Course Duration: 12 weeks
  • Total Video Length: 41:33 hours
  • Target Audience: Enthusiasts of advanced analysis and modeling (Data Analysts and Data Scientists)
  • Prerequisites: Dive into Data Science, Statistics for Data Science
  • Instructor: Dr. Zahra Zolghadr
  • Lifetime access to course videos and the option for free retraining in hybrid format
  • Language: Persian
SKU: N/A

This course is designed to familiarize students and researchers with the implementation of data mining processes, such as data preparation and building predictive models using specialized data mining software. By gaining a deep understanding of various problem types in this field and developing a comprehensive insight into problem-solving methods, you will be better prepared to approach data-driven analysis with a broader perspective. Python will be used as the tool for the practical implementation throughout the course.

The hybrid courses include the following:

  • Video Content:
    In the hybrid course format, all educational materials—including the instructor’s voice and image, desktop screen, and whiteboard presentations—are recorded and edited in a studio environment. These high-quality video lessons are then made available to participants.
  • Mentoring Service:
    However, the videos are only part of the learning experience. For each week’s content, we offer a two-hour online mentoring session. These sessions are usually scheduled on weekdays (Monday to Friday) outside of business hours. After watching the video lessons, you can ask any questions or clarify doubts directly with the instructor during the live mentoring session, fostering a direct interaction with the course instructor.
    Additionally, instructors can use this service to monitor the progress of remote participants, track assigned tasks, and follow up on exercises.
  • Telegram Support:
    For each ongoing program, a dedicated Telegram group will be created, where all participants of hybrid courses will be added. In this group, you can share resources, discuss content-related questions, ask individual queries, and engage in discussions. The course instructors are also present in this group and will respond to inquiries as much as possible.
  • Certificate:
    At the end of the hybrid courses, participants will receive a certificate. The certificate is issued in English, can be verified online, and is shareable on social media platforms.

The self-paced courses include the following:

  • Video Content:
    In the self-paced format, all educational materials—including the instructor’s voice and image, desktop screen, and whiteboard presentations—are recorded and edited in a studio environment. These high-quality video lessons are then made available to participants.
  • Telegram Support:
    For each ongoing program, a dedicated Telegram group will be created, where all participants of self-paced courses will be added. In this group, you can share resources, discuss content-related questions, ask individual queries, and engage in discussions. The course instructors are also present in this group and will respond to inquiries as much as possible.
  • Certificate:
    Self-paced courses do not include certification.
Course Type

Hybrid, Self-paced
Syllabus

• Introduction to Data Science Fundamentals
• Data Exploration and Descriptive Statistics
• Data Quality Assessment: Outliers and Inconsistent Data
• Data Quality Assessment: Identifying and Handling Outliers
• Data Quality Assessment: Missing Values and Imputation Techniques
• Data Transformation and Feature Engineering
• Implementing Data Transformations in Python
• Dimensionality Reduction: Feature Selection Techniques
• Housing Price Prediction Project: Data Preparation and Feature Selection
• Dimensionality Reduction: Feature Extraction Techniques
• Advanced Data Preparation Topics: Data Standardization, Normalization, and Discretization
• Building Data Science Project Pipelines
• Introduction to Predictive Modeling and Evaluation Metrics
• Decision Trees and Regression Trees
• Model Evaluation Methods: Cross-Validation, Training/Test Splits, and Performance Metrics
• Implementing and Evaluating Decision Trees for Classification
• Implementing and Evaluating Regression Trees for Prediction
• Handling Imbalanced Data: Resampling Techniques and Cost-Sensitive Learning
• Naive Bayes Algorithm: A Probabilistic Classifier
• Statistical Models: Linear Regression Algorithm
• Statistical Models: Logistic Regression Algorithm
• K-Nearest Neighbors (KNN) Algorithm: Classifying and Predicting
• Artificial Neural Networks (ANN) Algorithm: Classifying and Predicting
• Keras Framework and Implementing Neural Networks
• Support Vector Machines (SVM) Algorithm
• Ensemble Learning Predictive Models
• Implementing Ensemble Learning Models
• Time Series Forecasting: Predicting Future Values
• Unsupervised Learning: Clustering Models for Data Segmentation
• Unsupervised Learning: Association Rule Mining for Pattern Discovery
• Final Project: Customer Behavior Analysis (Real Project)

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