Machine Learning Speciality Course Overview

Target Audience for Machine Learning Speciality

The Machine Learning Speciality course by Koenig Solutions is designed for professionals seeking advanced knowledge in Data Science and machine learning techniques.

• Data Scientists
• Machine Learning Engineers
• Data Analysts
• Software Developers interested in ML
• IT Professionals looking to transition into data roles
• Statisticians aiming to implement ML models
• Business Analysts seeking to understand data-driven decision-making
• Research Scientists
• Graduate students in computer science/Data Science fields
• AI Enthusiasts
• Product Managers wanting to leverage ML in product development
• Technical Managers leading data-driven projects

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

Course Learning Outcomes Introduction:

In the Machine Learning Speciality course, students will gain comprehensive knowledge and skills in Data Science and machine learning, from fundamentals to advanced techniques, including hands-on experience with real-world applications.

Learning Objectives and Outcomes:

• Understand the necessity of Data Science and machine learning in solving complex problems and enhancing decision-making.
• Differentiate between descriptive, predictive, and prescriptive analytics and their applications.
• Master the Data Science project lifecycle, from conception to deployment.
• Acquire the essential skills required for a data scientist role, including statistical knowledge and programming expertise.
• Explore various types of machine learning such as supervised, unsupervised, and reinforcement learning.
• Gain proficiency in Python and its libraries for data analysis, visualization, and machine learning model building.
• Conduct exploratory data analysis (EDA) and apply various data preprocessing techniques, including handling missing values and categorical data.
• Develop and evaluate machine learning models using regression and classification techniques, understanding key concepts like overfitting and model selection.
• Implement feature selection, dimensionality reduction, and understand their impact on model performance.
• Apply cross-validation and hyperparameter tuning to optimize model performance, and gain hands-on experience with these techniques.
• Dive into Deep Learning, construct neural networks using Keras and TensorFlow, and comprehend essential concepts like activation functions and optimization algorithms.
• Understand clustering techniques, the process of forming clusters, and methods like the Elbow method for determining the optimal number of clusters.