Data Science with Deep Learning

Data Science with Deep Learning Course Details

Module 1: Python for Data Analysis & Data Science

• Introduction to Python for Data Analysis (variables, data types, operations, applications)
• Working with Libraries: NumPy, Pandas, Matplotlib
• Data manipulation with Pandas DataFrames, visualization basics
• Data cleaning and preprocessing: handling missing data, duplicates, normalization
• Exploratory Data Analysis (EDA): descriptive statistics and visualization, correlation and covariance

Module 2: Data Analysis Projects

• Importing and exploring datasets (CSV, Excel, SQL)
• Dealing with missing values and outliers, feature engineering
• Advanced Data Visualization with Seaborn, Plotly
• Statistical analysis and hypothesis testing (t-tests, etc.)
• End-to-end real-world data analysis project

Module 3: Machine Learning

• Supervised algorithms: Decision Trees & Random Forest (Titanic dataset)
• Linear Regression (Boston Housing dataset)
• K-Nearest Neighbors (Breast Cancer dataset)
• Support Vector Machines (Heart Disease UCI dataset)
• Logistic Regression (Student Exam Scores dataset)

Module 4: Unsupervised Machine Learning

• K-Means Clustering (Wine dataset)
• Hierarchical Clustering (Penguins dataset)
• Principal Component Analysis (Iris dataset)
• Association Rule Mining, Apriori (Advertising dataset)
• Gaussian Mixture Model (Diabetes dataset)

Module 5: Deep Learning & AI

• Natural Language Processing (using CORD-19 dataset)
• Image Recognition & Computer Vision (COCO, ImageNet datasets)
• Object detection for autonomous vehicles
• Personalized recommender system for e-commerce

Datasets Used Across Modules

• Iris, Titanic, Boston Housing, Wine, Diabetes, Heart Disease, Breast Cancer, Penguins, Advertising, Student Exam Scores, CORD-19, COCO, ImageNet

Other Topics

• AI Modeling: NLP for Sentiment Analysis, Computer Vision for Object Detection, E-commerce Recommender Systems
• Capstone project: Build, evaluate, and deploy a model using real-world datasets

Duration: 45-50 Hours | Schedule: Mon-Wed-Fri (Tue to Sat Indian Calendar), 8AM–9AM IST