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Master Certification in Data Science & Machine Learning

  • Last Update August 22, 2023
  • 0 already enrolled


Data Science (DS) and Machine Learning (ML) are two closely related fields that involve the extraction of valuable insights and predictions from data using advanced analytical techniques and algorithms. They play a crucial role in various industries by enabling data-driven decision-making, automation, and the development of intelligent systems. While related, Data Science and Machine Learning have distinct focuses and methodologies.

Accredited By:

An ISO 9001:2015 &
Teacher Scientist Network Certified

Course Overview:


Welcome to the Professional Certification Program in Data Science and Machine Learning! This comprehensive 90-day course is designed for beginners who are eager to embark on a journey into the world of data science and machine learning. With over 20 years of industry and teaching experience, our expert instructors will guide you through the fundamentals of data analysis, machine learning algorithms, and practical applications. By the end of this program, you will have a solid foundation in data science and machine learning concepts, empowering you to solve real-world problems and make data-driven decisions.

Course Objectives:


– Introduce the fundamentals of data science and its applications.

– Develop essential skills in data manipulation, exploration, and visualization.

– Understand the principles of machine learning and its algorithms.

– Gain hands-on experience in building machine learning models.

– Apply your knowledge through practical projects and case studies.


Course Structure:

The 90-day program is divided into ten modules, each focusing on core concepts in data science and machine learning:

– What is Data Science?

– What is Machine Learning?

– The Data Science Process

– Overview of Python for Data Science

– Introduction to Python

– Working with NumPy, Pandas, and Matplotlib

– Data Cleaning and Preprocessing

– Data Visualization with Seaborn and Matplotlib

– Descriptive Statistics

– Probability and Distributions

– Hypothesis Testing

– Introduction to Supervised and Unsupervised Learning

– Model Evaluation and Metrics

– Overfitting and Underfitting

– Linear Regression

– Logistic Regression

– Decision Trees and Random Forests

– Support Vector Machines (SVM)

– K-Means Clustering

– Hierarchical Clustering

– Dimensionality Reduction with PCA

– Neural Networks Overview

– Building and Training Neural Networks with TensorFlow/Keras

– Text Preprocessing

– Text Classification with NLP

– Collaborative Filtering

– Content-Based Filtering


Course Essentials :

Earned with Excellence:
Your Certificate
of Achievement

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  • Course level: Beginner
  • Total Enrolled 0
  • Last Update August 22, 2023
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