Fundamental of Machine Learning
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Follow on Instagram for UpdatesFundamental of Machine Learning Syllabus — Unit-wise Content
The official GTU curriculum for Fundamental of Machine Learning (subject code DI05041041) as prescribed for Nano · Semester 5 divides the course into 6 units, covering 34 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (DI05041041)Unit 1 Introduction to machine learning
- 1.1 Describe basic concept of machine learning and its applications 1.1.1 Overview of Human Learning and Machine Learning 1.1.2 Types of Machine Learning 1.1.3 Applications of Machine Learning 1.1.4 Tools and Technology for Machine Learning
Unit 2 Preparing to Model
- 2.1 Describe different types of Machine learning Activities. 2.1.1 Machine Learning activities 2.2 Explain types of data and data preprocessing 2.2.1 Types of data in Machine Learning 2.2.2 Structures of data 2.2.3 Data quality and remediation 2.2.4 Data Pre-Processing - Dimensionality reduction
- Feature subset selection
Unit 3 Modeling and Evaluation
- 3.1 Selecting a machine learning model 3.1.1 Selecting a Model - Predictive/Descriptive 3.2 Train the model for supervised learning 3.2.1 Training a Model for supervised learning: Holdout method
- K-fold Cross-validation method 3.3 Evaluate the prepared model 3.3.1 Model representation and interpretability 3.3.2 Evaluating performance of a model Confusion Matrix 3.3.3 Improving Performance of a model Program Name: Diploma Engineering Level: Diploma Branch: Automation & Robotics Subject Code: DI05041041 Subject Name: Fundamental of Machine Learning
Unit 4 Supervised Learning - Classification and Regression
- 4.1 Describe supervised learning 4.1.1 Introduction to supervised learning. 4.1.2 Classification Model 4.1.3 Learning steps 4.2 Explain classification Algorithms. 4.2.1 Classification Algorithms - k-Nearest Neighbor (KNN)
- Support Vector Machines 4.3 Explain Regression 4.3.1 Regression - Simple linear regression
- Multiple linear regression
- Logistic regression
Unit 5 Unsupervised Learning
- 5.1 Explain unsupervised learning 5.1.1 Supervised vs
- Unsupervised Learning 5.1.2 Applications of unsupervised learning 5.2 Describe Clustering 5.2.1 Clustering K-means Clustering algorithm 5.3 Describe pattern finding using association rule 5.3.1 Finding pattern using Association Rule
- Apriori algorithm
Unit 6 Python libraries for Machine learning
- 6.1 Demonstrate the application of existing Python libraries for machine learning tasks
- with emphasis on Pandas. 6.1.1 Data structures: Series
- DataFrame 6.1.2 Data loading and storage: read_csv()
- to_csv() 6.1.3 Data selection and filtering: loc[]
- iloc[] 6.1.4 Data cleaning: isnull()
- fillna() 6.1.5 Data aggregation: groupby()
- pivot_table() 6.2 Demonstrate the application of existing Python libraries for machine learning tasks
- with emphasis on Numpy 6.2.1 Array creation: array()
- linspace() 6.2.2 Array operations: reshape()
- transpose() 6.2.3 Mathematical functions: mean()
- sum() 6.2.4 Linear algebra: linalg.inv()
- linalg.eig() 6.3 Demonstrate the application of existing Python libraries for Program Name: Diploma Engineering Level: Diploma Branch: Automation & Robotics Subject Code: DI05041041 Subject Name: Fundamental of Machine Learning machine learning tasks
- with emphasis on Matplotlib 6.3.1 Basic plotting: plot()
- bar() 6.3.2 Histogram and pie charts: hist()
- pie() 6.3.3 Customization: title()
- legend() 6.3.4 Subplots: subplot()
- subplots() 6.4 Demonstrate the application of existing Python libraries for machine learning tasks
- with emphasis on Scikit-learn 6.4.1 Data preprocessing: train_test_split()
- LabelEncoder() 6.4.2 Classification algorithms: LogisticRegression()
- SVC() 6.4.3 Regression algorithms: LinearRegression()
- 6.4.4 Model evaluation: accuracy_score()
- Clustering: KMeans()
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