Machine Learning Essentials Course Overview

Machine Learning Essentials Course Overview

The Machine Learning Essentials course is a comprehensive program designed for learners to gain a strong foundation in machine learning (ML). It covers the ML landscape, applications, and delves into different algorithms and models, including both supervised and unsupervised learning. With a practical approach, the course offers hands-on experience through labs in environments like Jupyter notebooks or R-Studio.

Participants will learn crucial concepts such as statistics, covariance, correlation, and error analysis, alongside combating overfitting and underfitting. The course emphasizes feature engineering, data preparation, and visualization to enhance model accuracy. It explores various predictive models, including linear regression, logistic regression, SVM, decision trees, random forests, and Naive Bayes. Clustering with K-Means, dimensionality reduction with PCA, and recommendation systems using collaborative filtering are also key components. Through real-world use cases, learners will apply these concepts, culminating in a final workshop that solidifies their machine learning expertise.

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  • Live Online Training (Duration : 24 Hours)
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  • Live Online Training (Duration : 24 Hours)
  • Per Participant

♱ Excluding VAT/GST

Classroom Training price is on request

You can request classroom training in any city on any date by Requesting More Information

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  • Certificate of Completion
  • Official Coursebook



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Course Prerequisites

To successfully undertake the Machine Learning Essentials course, it is recommended that participants have the following prerequisites:

  • Basic understanding of programming concepts (preferably in Python or R, as they are commonly used in machine learning).
  • Familiarity with high school level mathematics, including algebra and basic statistics (mean, median, mode, standard deviation).
  • Logical reasoning and problem-solving skills.
  • Basic knowledge of data handling and manipulation techniques.
  • Willingness to learn and explore new tools and technologies.

Please note that while these prerequisites are aimed at ensuring a smooth learning experience, the course is designed to be accessible to a wide range of learners with varying levels of prior knowledge. Our instructors provide comprehensive guidance and support throughout the training to help all participants achieve their learning objectives.

Target Audience for Machine Learning Essentials

Koenig Solutions' Machine Learning Essentials course provides foundational knowledge for understanding and applying ML algorithms in real-world scenarios.

  • Data Scientists
  • Machine Learning Engineers
  • Data Analysts
  • Software Developers interested in AI/ML
  • IT Professionals seeking career advancement in Machine Learning
  • Business Analysts wanting to apply ML for data-driven insights
  • Researchers and Academicians in the field of computer science
  • Graduates aiming to enter the AI/ML job market
  • Product Managers looking to incorporate ML features in their products
  • Technical Managers overseeing data-driven projects
  • Statisticians seeking to upgrade their analytical toolset with ML techniques

Learning Objectives - What you will Learn in this Machine Learning Essentials?

Introduction to Machine Learning Essentials Course Learning Outcomes:

Gain practical insights into machine learning with hands-on experience in algorithms, models, and tools to analyze, predict, and visualize data effectively.

Learning Objectives and Outcomes:

  • Understand the scope and impact of machine learning algorithms and applications across various industries.
  • Acquire knowledge of different machine learning models, including supervised and unsupervised learning.
  • Develop proficiency in using Jupyter notebooks or R-Studio for implementing machine learning algorithms.
  • Grasp foundational statistics concepts essential for machine learning, such as covariance and correlation.
  • Learn techniques to prevent overfitting and underfitting and understand the importance of cross-validation and bootstrapping methods.
  • Master the skill of feature engineering to prepare and enhance data for better machine learning model performance.
  • Build, run, and evaluate linear and logistic regression models and apply them to real-world scenarios like house pricing and credit card applications.
  • Explore advanced classification techniques, including Support Vector Machines (SVM), Decision Trees, Random Forests, and Naive Bayes, and apply them to practical use cases.
  • Understand clustering methods, specifically K-Means, to segment data and evaluate clustering performance.
  • Get introduced to dimensionality reduction techniques, such as Principal Component Analysis (PCA), and recommendation systems using collaborative filtering.