Complete Machine Learning Curriculum for 8 Weeks...🔥🔥
Tags: beginners, python, datascience, machinelearning
Published: 2023-05-23 11:56:58

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


![Machine Learning](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/8tyfe5qooicehg1ajvjn.png)



**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.