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Machine Learning with scikit-learn Training

Scikit-learn Online Training includes understanding of  free software Machine Learning library used for Python Programming Language.

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Duration: 2 Days

Course fee:$399.00 (₹0)

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Product Description

Scikit-learn, one of the foremost and often used Machine Learning software libraries particularly used for the Python Language. Machine Learning with scikit-learn Training primarily focuses on learning and leading real-world problems occuring in the ML Applications. Training includes the implementation of k-nearest neighbors, random forest, logistic regression and artificial neural networks ML Models. Scikit-learn installation can be done easily using various download managers like pip on different Operating Systems like Windows, Ubuntu, MacOS, Anaconda. In this training, the developers can develop various examples using k-Nearest Neighbors with features extractions on standardized terminologies. In addition to it, Naive Bayes introductory information is included in the ML with scikit-learn training. On the completion of the training, the aspirants will grab the opportunity to understand the complete scenario for scikit-learn to be used for Machine Learning Platforms.

Objectives

  • Understanding the core concept like bias and variance
  • Feature extraction from categorical variables, images and text
  • Understanding Documents and images using logistic regression methods

Advantages

  • Discovering hidden data structure in data using K-means clustering
  • Evaluation of performance of ML systems in common tasks
  • Integration of Computer Science and statistics for building smart and efficient models

 

Additional Information

Day 1

1. Basic Overview of Machine Learning

– Introduction to Machine Learning
– Understanding Machine Learning Tasks
– Concept of Training Data, Testing Data and Data Validation
– Understanding Bias and variance
– Basic Overview of scikit-learn

2. Installation of scikit-learn

– Installation using pip
– Installation on Windows Machine
– Installation on Ubuntu
– Installation on MacOS
– Installation of Anaconda
– Installation Verification Process

3. Basic Installation of Pandas, Pillow, NLTK and matplotlib

4. Basic Overview of Simple Linear Regression

5. Classification Scenario with k-Nearest neighbors

6. Regression Scenario with k-Nearest Neighbors

– Lazy Learning and Non-parametric models
– KNN Classification methods
– KNN Regression Methods

7. Features Extraction Concepts

– Features extraction from categorical variables
– More on standardized features
– Features extraction from text
– Features extraction from images

Day 2

1. Migration from Simple to Multiple Linear Regression

– Understanding Multiple linear regression
– Understanding Polynomial Regression
– Concept of Regularization
– Apply for Linear Regression
– Introducing Gradient Descent

2. Migration from Linear to Logistic Regression

– Using Binary Classification with Logistic Regression
– Concept of Spam Filtering
– Using Tuning Models with Grid Search
– Understanding Multi-class Classification
– Understanding Multi-label Classification

3. Basic overview of Naiye Bayes

4. Understanding Nonlinear Classification and Regression with Decision Trees

– Understanding Decision Trees
– Concept on Training Decision Trees
– Using Decision Trees with scikit-learn

5. Migration from Decision Trees to Random Forest

6. Understanding the concept of perceptron

7. Migration from Perception to Support Vector Machines

– Using Kernels and kernel trick
– Understanding Maximum Margin Classification and Support Vectors
– Characters Classification in scikit-learn

8. Migration from Perception to Artificial Neural Networks

– Overview of Nonlinear decision boundaries
– Using Feed-forward and feedback ANNs
– Understanding Multi-layer perceptrons
– Concept of Training multi-layer perceptrons

9. Understanding the concept of K-means

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