Machine Learning Certification Training Using Python is a stepping stone for a new journey in a field of computer science. This course includes learning about python ecosystem, methods of ML, data loading, data with statistics and visualization, data feature selection, ML algorithms based on Classification, Regression, KNN and Clustering.
Jul 11 | Sat,Sun (9 Weeks) Weekend Batch | Filling Fast 03:00 PM 05:00 PM |
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StepLeaf’s Machine Learning Certification Training using Python Course helps you to extract meaningful raw data to solve complex problems quickly. The key focus of this course is to understand and implement various ML algorithms based on Classification, Regression, KNN and Clustering. The algorithms include Logistic Regression, SVM, Decision Trees, Naive Bayes, Random Forest, Linear Regression, K-Means, Mean Shift, Hierarchical Clustering, Performance Metrics, Automatic Workflow and improve performance of ML models
Course Objective
Mastering a technology is an art of talking with machines. At the end of the course, you will be mastered in the following topics:
Who should take up this Certification Course?
StepLeaf’s Machine Learning Training Course is mainly preferred for Analytics Manager, Software Developers, Business Analytics, Integration specialists, Information Architects and Python Professionals.
What are the prerequisites for this course?
Little knowledge in the following topics will explode your learning into a masterpiece
Python, bigdata, hadoop, machinelearning, randomforest, decisiontree, naïvebayes, datascience, dataextraction, dataanalysispipeline, matrix, pca, lda, gridsearch, randomsearch, k-meansclustering, reinforcementlearning, plotacf, pacf, tsaforecasting
Goal: Get an introduction to Data Science in this Module and see how Data Science helps to analyze large and unstructured data with different tools.
Objectives: At the end of this Module, you should be able to:
Topics:
Hands-On:
Hands-On:
Topics:
Structure your learning and get a certificate to prove it.
What are the system requirements for our Machine Learning Certification Training using Python?
The practical training is done in a Cloud Lab environment. This environment already has the required software in it.
How will I execute my practicals?
The Case Studies are executed using Jupyter Notebook in Cloud Lab. The necessary instruction will be given by our StepLeaf instructor to execute all the assignments.
What are the Case studies for this course?
There are totally 40 case studies as a part of this training. Given below are few of them.
Case Study 1
An Online Hotel booking application wants to create a recommendation of optimal suggestions for the users to book a hotel. Predict the hotel cluster for the user to book a room in a hotel using multi-class classification problems, build SVM and decision tree.
Case Study 2
In an Online Public Library users are requested to search for their individual choice of book. Using the ML model suggests that users read some more books based on his past purchase and refer to similar books read by other users. Help the library to find the error in their approach and build a profitable application.
Case Study 3
Do an end-to-end case study using time series analysis and forecasting with ML using Python. Extract meaningful statistics and find the insight of the data to predict future value with observed values.
Case Study 4
A construction company had a problem with its clients based on the quality of the building being constructed. Do an analysis and figure out all the different department in constructing a building and discover the problem and efficiency in each department which hinders the quality. Implement a proper solution to the problem
What are the projects for this course?
There are totally 5 projects as a part of this training. Given below is one of them.
Description
1. Download and install Python SciPy
2. Load dataset
3. Create 6 Machine Learning models, find the best with accuracy
Online learning is a mixture of live tutoring and recorded videos. It helps you to complete on your own time and give much flexibility to the students. Finally, you can say that it just fits your needs.