MACHINE LEARNING WITH PYTHON JNTU-K MCA Third Sem

Syllabus

Course Code: MCA3101

Machine Learning with Python

UNIT-I

Introduction to Machine Learning with Python: Introduction to Machine Learning, Basic Terminology, Types of Machine Learning and Applications, Using Python for Machine Learning: Installing Python and Packages From the Python Package Index, Introduction to NumPy, SciPy, Matplotlib and Scikit-Learn, Tiny Application of Machine Learning.

UNIT-II

Supervised Learning: Types of Supervised Learning, Supervised Machine Learning Algorithms: k-Nearest Neighbors, Regression Models, Naive Bayes Classifiers, Decision Trees, Ensembles of Decision Trees, Kernelized Support Vector Machines, Uncertainty Estimates From Classifiers.

UNIT-III

Building Good Training Datasets: Dealing with Missing Data, Handling Categorical Data, Partitioning a Data Set into Separate Training and Test Datasets, Bringing Features Onto the Same Scale, Selecting Meaningful Features, Assessing Feature Importance with Random Forests. Compressing Data via Dimensionality Reduction: Unsupervised Dimensionality Reduction via PCA, Supervised Data Compression via Linear Discriminant Analysis

UNIT-IV

Learning Best Practices for Model Evaluation and Hyperparameter Tuning: Streamlining Workflows with Pipelines, Using k-Fold Cross Validation to Assess Model Performance, Debugging Algorithms with Learning and Validation Curves, Fine Tuning Machine Learning Models via Grid Search, Looking at Different Performance Evaluation Metrics. Combining Different Models for Ensemble Learning: Learning with Ensembles, Combining Classifiers via Majority Vote, Bagging-Building An Ensemble of Classifiers From Bootstrap Samples, Leveraging Weak Learners via Adaptive Boosting

UNIT-V

Working with Text Data (Data Visualization): Types of Data Represented As Strings, Example Application: Sentiment Analysis of Movie Reviews, Representing Text Data As a Bag of Words, Stop Words, Rescaling the Data with tf-idf, Investigating Model Coefficients, Approaching a Machine Learning Problem, Testing Production Systems, Ranking, Recommender Systems and Other Kinds of Learning.