Welcome to datasciencegurus.com! We are thrilled to introduce you to our introductory course, “Machine Learning for Beginners”. We aim to help you develop a solid understanding of the fundamental concepts of Machine Learning and build the skills needed to apply these concepts in real-world scenarios.
1) Course Introduction
Module 1: Introduction to Machine Learning
1.1 What is Machine Learning?
1.2 Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
1.3 The Importance of Machine Learning in Today’s World
1.4 Overview of the Machine Learning Process
Module 2: Basic Concepts in Machine Learning
2.1 Features and Labels
2.2 Training and Test Data
2.3 Loss Functions and Optimisation
2.4 Bias-Variance Trade-off
2) Exploring Different Models
Module 3: Supervised Learning
3.1 Linear Regression
3.2 Logistic Regression
3.3 Decision Trees
3.4 Support Vector Machines
3.5 Random Forests
3.6 K-Nearest Neighbours
3.7 Evaluation Metrics for Supervised Learning
Module 4: Unsupervised Learning
4.1 Introduction to Unsupervised Learning
4.2 Clustering: K-Means, Hierarchical, DBSCAN
4.3 Dimensionality Reduction: Principal Component Analysis (PCA)
4.4 Anomaly Detection
4.5 Evaluation Metrics for Unsupervised Learning
Module 5: Reinforcement Learning
5.1 Introduction to Reinforcement Learning
5.2 Concepts: Agents, Environments, Actions, Rewards
5.3 Exploration vs Exploitation
5.4 Markov Decision Process
5.5 Q-Learning and Deep Q Networks
3) Practical Applications and Project Work
Module 6: Tools for Machine Learning
6.1 Introduction to Python for Machine Learning
6.2 Working with Data: NumPy, Pandas
6.3 Data Visualisation: Matplotlib, Seaborn
6.4 Machine Learning Libraries: Scikit-learn, Tensorflow, Keras
6.5 Working with Jupyter Notebooks
Module 7: Real-World Applications of Machine Learning
7.1 Machine Learning in Healthcare
7.2 Machine Learning in Finance
7.3 Machine Learning in Marketing
7.4 Machine Learning in Transportation
7.5 Machine Learning in Social Media
Module 8: Project Work
8.1 Project Brief and Objective
8.2 Data Exploration and Pre-processing
8.3 Model Selection and Training
8.4 Model Evaluation and Fine-tuning
8.5 Presentation of Results
4) Course Conclusion
Module 9: The Future of Machine Learning
9.1 The Role of Machine Learning in Big Data
9.2 Machine Learning and Artificial Intelligence
9.3 Ethics in Machine Learning
9.4 Job Opportunities in Machine Learning
9.5 Future Trends in Machine Learning
