20 Hours Instructor Led Live Training | 20 Hours of Self Learning Labs | Five Assessments | CPE Credits | Certification on Completion
Machine learning is an integral part of many commercial applications and research projects today, in areas ranging from medical diagnosis and treatment to finding your friends on social networks. Many people think that machine learning can only be applied by large companies with extensive research teams. In this module, we want to show you how easy it can be to build machine learning solutions yourself, and how to best go about it. You can build your own AI system for multiple applications across domains. The applications of machine learning are endless and, with the amount of data available today, mostly limited by your imagination.
What you will learn
- What and Whys of ML, Steps and Approaches to ML, Types of Machine Learning, Evaluations Metrics, Exploratory Data Analysis, Data preparation for ML- missing values, outliers, feature engineering
- Regression and Classification Models- Logistic, KNN, Naïve Bayes, Neural Networks, Ensemble Models
- Model Evaluations- Trade-off – Bias vs variance, Confusion Matrix, ROC curve
- Model Performance Optimization, boosting methods, including AdaBoost and XGBoost, Hyperparameter tunning, Model testing and validation, Overfitting vs underfitting,
- Unsupervised Learning, Clustering – K-Means and Hierarchical, PCA, Recommendation systems, Rule based – Popularity, Association rule mining, APRIORI