skilldevelopment.cpe@lpu.co.in

What you'll learn

Course Description : This course introduces the fundamentals of Exploratory Data Analysis (EDA) and basic Machine Learning techniques. Students learn how to understand, clean, visualize, and interpret data, followed by applying simple ML models for prediction and classification tasks. Learning Outcomes : Understand key EDA concepts and perform data cleaning and visualization. Identify patterns, trends, and relationships within datasets. Apply basic ML algorithms like regression, classification, and clustering. Evaluate model performance using essential metrics. Build simple, end-to-end data-driven solutions.

  • Overview of Data Science Workflow; Types of Data; Role of EDA & ML Tools Introduction: Python, Jupyter, NumPy, Pandas, Matplotlib, Seaborn Data Types & Formats, Importing Datasets Handling Missing Values & Detecting Outliers Data Cleaning & Preprocessing: Transformations
  • Feature Scaling: Normalization & Standardization Encoding Categorical Variables (Label, One-Hot) Data Splitting (Train/Test) Function and Array in Kotlin Bivariate Plots: Scatter, Pairplot
  • Correlation Analysis & Heatmaps Visualizing Distributions & Patterns "Feature Engineering Concepts " Feature Selection & Intro to PCA EDA Mini Case Study – Hands-on-CA 1
  • What is ML? Supervised vs. Unsupervised Bias–Variance, Overfitting & Underfitting Regression: Linear Regression Classification: Logistic Regression Classification: KNN Algorithm
  • Decision Trees – Concepts Random Forest – Concepts Evaluation Metrics: Accuracy, Precision, Recall, F1 Confusion Matrix & Interpretation Unsupervised ML: K-Means Clustering
  • PCA – Dimensionality Reduction (Intro) Model Training Pipeline & Cross-Validation Hyperparameter Tuning – Introduction ML Mini Project: Build a Model-CA 2 Results Interpretation & Wrap-Up

Avatar
Dr.Vikas
Associate Professor

Dr. Vikas Attri, MCA, M.Tech (IT), is an experienced academic and industry professional with more than 15 years of expertise in advanced technologies. His areas of specialization include Advanced Python, Data Analytics, Machine Learning, Deep Learning, and Digital Marketing. He has contributed significantly through teaching, research, and technical guidance, demonstrating strong proficiency in designing data-driven solutions and delivering high-quality academic outcomes.