Machine Learning with Python

• What is ML?
• History and evolution of ML
• ML applications and use cases
• Introduction to Python for ML

• Eigenvalues and Eigenvectors:
  • Understanding eigenvalues and eigenvectors
  • Applications in machine learning
• Vectors:
  • Vector operations and properties
  • Use in data representation
• t-SNE:
  • t-Distributed Stochastic Neighbor Embedding
  • Visualization of high-dimensional data
• Data Representation and Preprocessing:
  • Data cleaning and normalization
  • Feature scaling and transformation
• Principal Component Analysis (PCA):
  • Dimensionality reduction
  • Applications and implementation
• Maths for ML:
  • Linear algebra and calculus
  • Optimization techniques

• Introduction to NumPy
• Array operations
• Mathematical functions
• Broadcasting and vectorization

• Introduction to Pandas
• Data structures: Series and DataFrames
• Data manipulation and analysis
• Handling missing data
• Merging, joining, and concatenating

• Maths and Statistics for ML:
  • Descriptive statistics
  • Probability theory
• Hypothesis Testing and Null Hypothesis:
  • Formulating and testing hypotheses
  • Null hypothesis and alternative hypothesis
• AB Testing Procedure:
  • Designing and conducting A/B tests
  • Analyzing results
• One-Hot Encoding:
  • Encoding categorical variables
  • Implementation in Python
• P-values:
  • Understanding p-values
  • Statistical significance

• Matplotlib:
  • Basic plotting
  • Customizing plots
  • Subplots and layouts
• Seaborn:
  • Statistical plots
  • Advanced visualization techniques
  • Integrating with Pandas

• Understanding the importance of EDA
• Summary statistics and visualizations
• Identifying patterns and anomalies
• Correlation and covariance analysis
• Case studies and hands-on projects

• Supervised Learning:
  • Linear regression
  • Logistic regression
  • Decision trees and random forests
  • Support vector machines (SVM)
• Unsupervised Learning:
  • K-means clustering
  • Hierarchical clustering
  • Association rule learning
• Reinforcement Learning:
  • Markov decision processes
  • Q-learning and deep Q-learning
• Neural Networks and Deep Learning:
  • Introduction to neural networks
  • Convolutional neural networks (CNN)
  • Recurrent neural networks (RNN)
  • Deep learning frameworks (e.g., TensorFlow, Keras)
• Model Evaluation and Selection:
  • Cross-validation
  • Hyperparameter tuning
  • Performance metrics

Materials Needed:

• Computer with Python installed
• Text editor or IDE (e.g., Jupyter Notebook, PyCharm)
• Internet access for downloading libraries and datasets

Prerequisites:

• Basic programming skills in Python
• Familiarity with basic concepts in mathematics and statistics (recommended but not mandatory)