Machine Learning Basics Online Course
Machine Learning Basics Online Course
This Machine Learning Basics course provides a structured introduction to AI-driven learning models. The first part covers the fundamentals, including statistical learning, linear and logistic regression, classification, cross-validation, and techniques to enhance linear models for non-linear applications. The second part is hands-on, featuring practical labs on predicting fuel efficiency, logistic regression, decision trees, random forests, PCA for facial recognition (Eigenfaces), and ROC-AUC evaluation. By the end of the course, you will have the foundational skills and confidence to implement machine learning algorithms effectively.
Key Benefits
- Develop customized deep learning models to kickstart your career in data science.
- Gain expertise in building tailored models for various data science applications and projects.
- Acquire a strong foundation in the core principles of machine learning, enabling you to implement and optimize intelligent systems effectively.
Target Audience
This course is designed for individuals at various skill levels, including beginners in Python programming, machine learning, and data science. It is well-suited for aspiring data scientists, analysts, and researchers looking to build a strong foundation in machine learning concepts. While no prior experience is required, a basic understanding of Python programming and fundamental calculus can enhance the learning experience and comprehension of key topics.
Learning Objectives
- Gain a comprehensive understanding of the fundamentals of statistical learning.
- Develop a strong foundation in linear regression, classification, and supervised learning techniques.
- Learn the concepts of sampling methods and Bootstrap techniques in machine learning.
- Explore model selection strategies and the role of regularization in improving model performance.
- Understand tree-based algorithms, including decision trees and random forests, for predictive modeling.
- Engage in hands-on labs covering advanced deep learning techniques such as Multilayer Perceptron (MLP) and Recurrent Neural Networks (RNN).
Course Outline
The Machine Learning Basics Exam covers the following topics -
- Introduction to Machine Learning
- Fundamentals of Statistical Learning
- Understanding Linear Regression
- Techniques in Classification
- Sampling Methods and Bootstrap Techniques
- Approaches to Model Selection
- Advanced Non-Linear Modeling
- Tree-Based Algorithms: Part 1 and 2
- Linear Regression Models
- Logistic Regression Techniques
- Ridge Regression for Regularization
- Decision Trees for Predictive Modeling
- Random Forests for Ensemble Learning
- Support Vector Machines (SVM) for Classification
- Multilayer Perceptron (MLP) for Neural Networks
- Convolutional Neural Networks (CNN) for Deep Learning
- Principal Component Analysis (PCA) for Dimensionality Reduction
- Receiver Operating Characteristic (ROC) and Area Under the Curve (AUC) for Model Evaluation
