16
Lessons
32h
Duration
English
Language
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OBJECTIVE:
- Practicing Machine Learning Algorithms
Course features:
- Practical hands on
- Lab sessions
- Training by experienced faculty
PRE-REQUISITES:
- Good knowledge of Python Programming and Statistics
Learning Path
- Machine Learning in Nutshell
- Supervised and Unsupervised Learning
- ML Applications
- Evaluating ML techniques
- Uses of Machine Learning
2 hours
- Feature engineering and Data Pre-processing: Data Preparation, Feature creation, Data cleaning & transformation
- Data Validation & Modelling
- Feature selection Techniques
- Dimensionality reduction
2 hours
- Principal Component analysis (PCA)
- Clustering
- Hierarchical Clustering &K means
- Distance Measure and Data Preparation – Scaling & Weighting
2 hours
- Evaluation and Profiling of Clusters
- Hierarchical Clustering
- Clustering Case Study
2 hours
- Decision Trees
- Classification and Regression Trees
2 hours
- Bayesian analysis and Naïve Bayes classifier
- Assigning probabilities and calculating results
2 hours
- Discriminant Analysis (Linear and Quadratic)
- K-Nearest Neighbors Algorithm
2 hours
- Concept of Model Ensembling
- Random forest, Gradient boosting Machines, Model Stacking
2 hours
- Association rules mining
- Apriori and FP-growth algorithms
2 hours
- Linear Regression
- Logistic Regression
- Polynomial Regression
- Stepwise Regression
- Ridge Regression
- Lasso Regression
- ElasticNet Regression
2 hours
- Support vector Machines
- Basic classification principle of SVM
- Linear and Nonlinear classification (Polynomial and Radial)
2 hours
- Auto-correlation (ACF & PACF)
- Auto-regression
- Auto-regressive Models
- Moving Average Models
- ARMA &ARIMA
2 hours
- ML in Real Time
- Algorithm Performance Metrics
- ROC and AOC
- Confusion Metrix
- F1 Score
- MSE and MAE
2 hours
- Recommendation Systems
- Data Collection & Storage, Data Filtering
- Collaborative Filtering
- Factorization Methods
- Evaluation Metrics: Recall, Precision, RMSE, Mean Reciprocal Rank, MAP at K, NDCG
2 hours
- Anomaly detection
- Point, Contextual and Collective Anomaly
- Supervised and Unsupervised anomaly detection
2 hours
- DBSCAN Clustering
2 hours
- Usage of ML algorithms, Algorithm performance metrics (confusion matrix sensitivity, Specificity, ROC, AOC, F1score, Precision, Recall, MSE, MAE)
- Credit Card Fraud Analysis
- Intrusion Detection system