Python for Machine Learning and Advanced Analytics
Who Should Attend?
IT PROFESSIONALS, DATA ANALYST and PROFESSIONALS with basic knowledge of programming
This course will introduce the learner to applied data analytics with Python, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and the introduction to the scikit learn toolkit.
- Identify difference between supervised (classification) and unsupervised (clustering) technique
- Identify which technique they need to apply for a particular dataset and need
- Engineer features to meet the machine learning needs
- Write python code to carry out an analysis
Module 1: Introduction
- Introduction to Machine Learning
- Introduction to Scikit Learn Package
- Regression vs Classification
Module 2: Supervised Learning
- K Nearest Neighbour (kNN)
- Naïve Bayes
- Logistic Regression
- Support Vector Machine (SVM)
- Decision Tree & Random Forest
- Hyperparameter Model Tuning,
Regularization Ridge and Lasso
Module 3: Unsupervised Learning
Module 4: Advanced Analytics
- Cross Validation
- Model Evaluation and Selection
- Select, Manipulate and Analyze Data
- Introduction to Ensemble Models
Drop us your entry if you are interested to join this course.
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