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The course covers popular ML libraries and tools such as Scikit-Learn, TensorFlow, and PyTorch.
Difficulty
Hours per week
Live Sessions
Next Date
Objective
The objective of this course is to provide participants with a comprehensive understanding of the fundamentals of supervised and unsupervised machine learning models, and popular ML libraries and tools. Participants will learn how to automate the machine learning process using AutoML techniques, including data preparation, feature engineering, model selection, and hyperparameter tuning. They will gain knowledge and skills in building supervised models that learn from labeled data for prediction tasks. Additionally, participants will explore unsupervised models that learn from unlabeled data for clustering and dimensionality reduction tasks. The course will also cover popular ML libraries and tools such as Scikit-Learn, TensorFlow, and PyTorch, enabling participants to effectively use these tools to build and deploy machine learning models. By the end of the course, participants will be equipped with the necessary knowledge and skills to leverage AutoML techniques, build and evaluate supervised and unsupervised models, and utilize ML libraries and tools, empowering them to apply machine learning in various domains for predictive and analytical purposes.
• Basic understanding of machine learning concepts, including supervised and unsupervised learning.
• Familiarity with programming and Python, beneficial for hands-on implementation using ML libraries and tools.
• Access to a computer with the required software and libraries installed to practice the concepts covered in the course.
Gain an understanding of advanced analytics and its wide range of applications across various industries. Explore real-world case studies that showcase how advanced analytics is used to solve complex business problems and drive data-driven decision-making in different domains.
Intro to supervised machine learning models, starting with linear regression for predicting continuous outcomes. Learn how to apply logistic regression for binary classification tasks. Decision trees and random forests will be explored as tree-based models suitable for both regression and classification problems.
Unsupervised learning techniques will be introduced, beginning with K-Means Clustering to group data based on similarity. Hierarchical clustering and Principal Component Analysis (PCA) will be used for dimensionality reduction and feature extraction.
Essential Python libraries and tools for machine learning. Utilize NumPy and Pandas for data manipulation and preprocessing tasks. The functionalities of the Scikit-learn library for machine learning models. Introduction to deep learning using TensorFlow and Keras to build and train neural networks. Model deployment techniques will be covered to prepare models for real-world applications.
Learn about Automated Machine Learning (AutoML) and its role in streamlining the machine learning workflow. Explore popular AutoML tools like Auto-sklearn, H2O.ai, and Google Cloud AutoML. Discover how AutoML can automate feature engineering tasks and automatically select the best model while fine-tuning hyperparameters to achieve optimal performance.
Lean to implement real-world advanced analytics projects using Python and machine learning, from data cleaning and preprocessing to model building and evaluation. By working on advanced use cases and applications of machine learning in various domains, gain valuable hands-on experience and strengthen their data analytics and modeling skills.
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