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INTRODUCTION TO MACHINE
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4350702
Code: 4350702
Credits: 5
Semester 5
Auto
Introduction to Machine Learning
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Introduction to Machine Learning Syllabus — Unit-wise Content
The official GTU curriculum for Introduction to Machine Learning (subject code 4350702) as prescribed for Auto · Semester 5 divides the course into 5 units, covering 28 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 (4350702)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 Supervised Machine Learning Unsupervised Machine Learning Reinforcement Learning.
- 1.1.3. Applications of Machine Learning
- 1.1.4. Tools and Technology for Machine Learning
Unit 2 Python libraries suitable for Machine Learning
- 2.1. Develop Program using Python Libraries
- 2.1. Numpy Creating Array: array() Accessing Array: by referring to its index number Stacking & Splitting: stack(), array_split() Maths Functions: add(), subtract(), multiply(), divide(), power(), mod() Statistics Functions: amin(), amax(), mean(), median(), std(), var(), average(), ptp()
- 2.2. Pandas Series: Series() Dataframes: DataFrames() Read CSV File: read_csv() Cleaning Empty Cells: dropna() Cleaning Wrong Data: drop() Removing Duplicates: duplicated() Pandas Plotting: plot()
- 2.3. Matplotlib Pyplot.plot: plot() Show: show() Labels: xlabel(), ylabel() Grid: grid() Bars: bar() Histogram: hist() Subplot: subplot() pie chart: pie() Save the plotted images into pdf: savefig()
- 2.4. sklearn Key concepts and features Steps to Build a Model in Sklearn: Loading a Dataset- Introduction to Machine Learning Course Code: 4350702 read_csv(), train_test_split- train_test_split()
Unit 3 Preparing to Model and Preprocess ing
- 3.1. Describe different types of Machine learning Activities
- 3.2. Explain Data preprocessing
- 3.1.1. Machine Learning activities Preparing to Model Learning: Data Partition-k- fold cross validation, Model Selection Performance Evaluation: confusion matrix Performance Improvement: Ensemble
- 3.2.1. Types of Data Qualitative/Categorical Data: Nominal, Ordinal Quantitative/Numeric Data: Interval, Ratio
- 3.2.2. Data quality and remediation Handling outliers Handling missing values
- 3.2.3. Data Pre-Processing Dimensionality reduction Feature subset selection: Filter, Wrapper, Hybrid, Embedded
Unit 4 Supervised Machine Learning Models
- 4.1. Define Supervised Learning
- 4.2. List types of Supervised Learning, Describe K- Nearest Neighbour and Simple linear regression
- 4.3. Advantage and disadvantage of supervised machine learning
- 4.1.1. Introduction of Supervised Learning Brief explanation of Supervised Machine Learning Working of Supervised Machine learning Real world Applications/Examples of Supervised Machine learning Steps in Supervised Machine learning
- 4.2.1. Types of Supervised Learning ● Classification: Define Classification, list types of classification, list types of Machine learning classification algorithms (list linear models, nonlinear models), list use cases of classification algorithms. K- Nearest Neighbour (K-NN) : Working of K- NN, Need of KNN algorithm, steps of working of K-NN, Select value of K, advantage and disadvantage of K-NN Introduction to Machine Learning Course Code: 4350702 algorithm ● Regression: Define Regression analysis, list types of regression analysis, list real world examples of regression analysis Linear regression: List types of linear regression, mathematical equation of linear regression, diagram of linear regression line (positive, negative) Simple linear regression : (Description, objective, demonstrate example of salary prediction using python) (Steps: Prepare dataset, split data set into training and testing set, visualize training data set and testing data set, i.e. plot it, initialize the training set and fitting it using training set, Predict) list applications of linear regression
- 4.3.1. Advantage and disadvantage of supervised machine learning Introduction to Machine Learning Course Code: 4350702
Unit 5 Unsupervised Machine Learning Models
- 5.1. Define Unsupervised Learning
- 5.2. List types of Unsupervised Learning
- 5.3. Differentiate Supervised and Unsupervised Learning
- 5.1.1. Introduction of Unsupervised Learning Brief explanation of unsupervised Machine Learning Need of unsupervised learning Working of unsupervised learning Real world examples of unsupervised Learning List unsupervised learning algorithms
- 5.2.1. Types of Unsupervised Learning Clustering: Definition, list clustering methods, list real world applications/examples (fruits, vegetables, computer devices (input and output etc)), ● Association: Definition, list association methods, list real world applications/examples ● Advantage and Disadvantage of unsupervised learning algorithm
- 5.3.1. Differentiate Supervised and Unsupervised Learning Note: The UOs need to be formulated at the ‘Application Level’ and above of Revised Bloom’s Taxonomy’ to accelerate the attainment of the COs and the competency.
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