Machine Learning

Welcome to Realcare Tech Mark's Online Machine Learning Course, a comprehensive program designed to equip you with the knowledge and skills needed to thrive in the dynamic field of machine learning. Whether you're a beginner or an intermediate learner, this course will guide you through the fundamentals and advanced concepts, empowering you to build and deploy machine learning models effectively.

Curriculum

11 Sections
34 Lessons
0 Quizzes
0m Duration

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Introduction to Machine Learning

3 Lessons0 Quizzes

Overview of Machine Learning
Types of Machine Learning
Applications of Machine Learning

Data Preprocessing

3 Lessons0 Quizzes

Data Cleaning and Imputation
Feature Scaling and Normalization
Handling Categorical Data

Supervised Learning

4 Lessons0 Quizzes

Linear Regression
Decision Trees and Random Forests
Support Vector Machines
Ensemble Learning

Unsupervised Learning

3 Lessons0 Quizzes

Clustering Algorithms
Dimensionality Reduction
Association Rule Mining

Model Evaluation and Validation

3 Lessons0 Quizzes

Training and Testing Sets
Cross-Validation
Performance Metrics

Feature Engineering

3 Lessons0 Quizzes

Importance of Feature Engineering
Techniques for Feature Selection
Transformative Feature Creation

Deep Learning

3 Lessons0 Quizzes

Neural Networks
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)

Natural Language Processing (NLP)

3 Lessons0 Quizzes

Basics of NLP
Sentiment Analysis
Text Classification

Reinforcement Learning

3 Lessons0 Quizzes

Introduction to Reinforcement Learning
Markov Decision Processes
Q-learning

Model Deployment

3 Lessons0 Quizzes

Preparing Models for Deployment
Scalability and Reliability Considerations
Monitoring and Maintenance

Ethical Considerations in Machine Learning

3 Lessons0 Quizzes

Bias and Fairness
Privacy Concerns
Responsible AI Practices

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Last Updated: March 15, 2024

This course includes

Understanding ML foundations
Preparing data for ML tasks
Deploying ML models
Its applications, and the different types of machine learning algorithms.
Data Preprocessing: Learn essential techniques for cleaning, transforming, and preparing data for machine learning tasks.
Supervised Learning: Explore supervised learning algorithms, including linear regression, decision trees, support vector machines, and ensemble methods.
Unsupervised Learning: Dive into unsupervised learning techniques such as clustering, dimensionality reduction, and association rule mining.
Model Evaluation and Validation: Master the art of evaluating and validating machine learning models using metrics like accuracy, precision, recall, and F1 score.
Feature Engineering: Understand the importance of feature engineering in improving model performance and efficiency.
Deep Learning: Delve into the world of deep learning, covering neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).
Natural Language Processing (NLP): Explore how machine learning is applied to process and understand human language, including sentiment analysis and text classification.
Reinforcement Learning: Gain insights into reinforcement learning principles and applications, including Markov decision processes and Q-learning.
Model Deployment: Learn how to deploy machine learning models into production environments, ensuring scalability and reliability.
Ethical Considerations in Machine Learning: Understand the ethical implications of machine learning, including bias, fairness, and privacy concerns.