Course Curriculum
Python Refresher, NumPy & Pandas+
- Basic Syntax, Lists, Tuples, Lambda Functions, Map/Filter/Reduce
- NumPy – Introduction, Arrays, array operations
- Pandas Series vs Data Frames, loading data (csv, xlsx, json)
- Data pre-processing techniques with Pandas
- Seaborn & Matplotlib – Line, Dist, Join, Scatter, Count plots, Heatmap, 3D plotting
Web Scraping & Exploratory Data Analysis+
- Web scraping with BeautifulSoup and Requests
- Converting scraped data to a dataset
- Introduction to EDA – data collection, understanding data
- Filling missing values, Feature Engineering techniques
- Working with text data, Preparing data for ML algorithms
Supervised Learning+
- Regression – Simple, Multiple, Polynomial and Non-Linear
- Random Forest Regressor, Decision Tree Regressor, SVM Regressor
- Logistic Regression with Log Loss, Naive Bayes classifier
- SVM, k-NN Algorithm with Euclidean Distance
- Decision Tree (Gini and Entropy), Random Forest Classifiers
- Boosting – AdaBoost, Gradient Boosting, XGBOOST
Unsupervised Learning & Recommendation Systems+
- K-means, K-medoid, Hierarchical/Agglomerative Clustering
- DBSCAN, PCA (Principal Component Analysis)
- Collaborative & Content-Based Recommendation Systems
Model Optimization, Pipelines & Projects+
- Model Selection, Overfitting, Underfitting, Bias-Variance Trade-off
- Hyperparameter Optimization using Randomized Search CV
- ML Pipelines, RMSE, Accuracy Score, K-Fold Cross Validation
- Basic: Breast Cancer Prediction, Spam Mail Detection, Diabetes Detection
- Intermediate: Movie Recommendation System, Face Detection, Customer Segmentation
- Advanced: Churn Prediction, California Houses Price, Emotion Recognition