Introduction to Machine Learning

• What is Machine Learning?
• Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
• The Machine Learning Pipeline: Data Collection, Preparation, Modeling, Evaluation, Deployment
• Key Concepts: Overfitting, Underfitting, Bias-Variance Trade-off

Python Programming for Machine Learning

• Python Basics: Variables, data types, control flow, functions
• NumPy and Pandas: Arrays, DataFrames, manipulation, and analysis
• Data Visualization: Matplotlib and Seaborn for histograms, scatter plots, box plots

Statistical Concepts

• Descriptive Statistics: mean, median, mode, variance, standard deviation
• Probability Theory: distributions (normal, binomial, Poisson), Bayes’ theorem
• Hypothesis Testing: t-tests, z-tests, chi-square tests, ANOVA

Supervised Learning

• Linear Regression: simple, multiple, and regularization techniques
• Logistic Regression: binary and multinomial
• Decision Trees: algorithms (ID3, C4.5, CART), pruning
• Random Forests: ensemble learning, bagging
• Support Vector Machines (SVM): linear and kernel-based
• Naive Bayes: Bayes’ theorem and text classification

Unsupervised Learning

• Clustering: K-means, Hierarchical, DBSCAN
• Dimensionality Reduction: PCA, t-SNE

Model Evaluation and Deployment

• Evaluation Metrics: Confusion matrix, Accuracy, Precision, Recall, F1-score
• ROC and AUC curves
• Model Tuning: Cross-validation, Grid and Random search
• Deployment: Flask, Streamlit, REST APIs