Complete Machine Learning Curriculum for 8 Weeks...🔥🔥

Muggalla Rahul — Tue, 23 May 2023 11:56:58 +0000

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

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