Course Content
Introduction to Machine Learning and Data Preprocessing
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1.1 Understanding Machine Learning
01:25:00
Types of machine learning: Supervised, Unsupervised, and Reinforcement Learning.
04:00:00
1.2 Real-World Applications
1.3 Python and Java for Machine Learning
Setting up the development environment for both languages.
1.4 Data Preprocessing Techniques
Handling missing data: Techniques in Python (Pandas, NumPy) and Java (Weka).
Data encoding: Label encoding and one-hot encoding.
Feature scaling: Standardization and normalization methods.
Splitting data into training and testing sets.
Module 2: Core Machine Learning Algorithms
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Linear Regression
Logistic Regression
Decision Trees
Random Forests
Unsupervised Learning Algorithms : Clustering
Principal Component Analysis (PCA)
Anomaly Detection
Machine Learning A-Z: Hands-On Python and java
About Lesson

Why Python and Java?

  • Python and Java are top choices for machine learning and AI development due to their versatility, extensive libraries, and strong community support.
  • Python is favored for its simplicity, readability, and vast array of machine learning libraries like TensorFlow, PyTorch, and Scikit-learn. It’s ideal for rapid prototyping and data analysis.
  • Java offers robustness, scalability, and performance, making it suitable for large-scale applications. It’s widely used in enterprise-level AI solutions, with strong support for tools like Weka and Deeplearning4j.
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