Here is the basic Machine Learning Course Curriculum for beginners of 8 weeks...😊😊

Week 1: Introduction to Machine Learning

• Understand the concept of Machine Learning and its applications.
• Differentiate between supervised, unsupervised, and reinforcement learning.
• Explore the typical workflow of a Machine Learning project.
• Set up Python and learn about essential libraries such as NumPy, Pandas, and Matplotlib.

Week 2: Exploratory Data Analysis and Data Pre-Processing

• Learn about Exploratory Data Analysis (EDA) techniques to gain insights from data.
• Handle missing values in datasets using various imputation methods.
• Perform feature scaling and normalization to ensure fair comparisons between variables.
• Deal with categorical variables by applying encoding techniques.
• Understand feature engineering and selection for better model performance.

Week 3: Supervised Learning: Regression

• Dive into regression analysis and its use for predicting continuous numerical values.
• Implement simple linear regression to model relationships between two variables.
• Extend to multiple linear regression to handle multiple predictors.
• Apply polynomial regression to capture non-linear relationships.
• Evaluate regression models using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared.

Week 4: Supervised Learning: Classification

• Explore classification algorithms used for predicting categorical outcomes.
• Learn logistic regression, a widely used classification algorithm.
• Implement the K-Nearest Neighbors (KNN) algorithm for both binary and multiclass classification.
• Understand decision trees and ensemble methods like Random Forests.
• Evaluate classification models using accuracy, precision, recall, and F1-score.
• Handle imbalanced datasets using techniques like oversampling and undersamplling.

Week 5: Supervised Learning: Support Vector Machines (SVM)

• Gain a solid understanding of Support Vector Machines (SVM), a powerful classification algorithm.
• Implement linear SVM for linearly separable data.
• Extend SVM to non-linear problems using kernel tricks.
• Tune SVM hyperparameters for optimal model performance.
• Apply SVM to multiclass classification problems.

Week 6: Unsupervised Learning: Clustering

• Learn about unsupervised learning and its applications.
• Implement K-Means Clustering for grouping similar data points.
• Understand hierarchical clustering techniques like Agglomerative and Divisive clustering.
• Explore Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for discovering clusters of arbitrary shapes.
• Evaluate clustering results using the Silhouette Coefficient.

Week 7: Unsupervised Learning: Dimensionality Reduction

• Understand the concept of dimensionality reduction and its importance.
• Implement Principal Component Analysis (PCA) for reducing high-dimensional data.
• Learn about t-Distributed Stochastic Neighbour Embedding (t-SNE) for visualizing high-dimensional data in lower dimensions.
• Explore Singular Value Decomposition (SVD) for feature extraction.
• Apply dimensionality reduction techniques to real-world datasets.

Week 8: Evaluation and Model Selection

• Learn techniques for evaluating model performance.
• Understand the importance of splitting data into training and testing sets.
• Implement various cross-validation techniques like K-fold Cross Validation.
• Perform grid search and hyperparameter tuning to optimize model performance.
• Learn about the bias-variance trade-off and strategies for model selection.
• Understand model persistence and deployment for real-world applications.