Location:Main Road, Bangalore


Hands On Scikit Learn For Machine Learning




Machine Learning Training Insitute


Lateral Entry Professionals and Freshers


Online and Offline Classes


Week Days and Week Ends

Duration :

60 Days

Machine Learning Objectives

•Understand the concepts in Machine Learning
•Work with standard programming skills in Machine Learning.
•Become a professional Machine Learning Engineer by learning Machine LearningHow to apply the Machine Learning rules in different situations.
•Learn or brush up with the basics of Machine Learning
•Learn Machine Learning from Scratch, Start from basic to advanced level
•Learn the Ins and Outs of Machine Learning in few Hours
•Understand Machine Learning and how to use it to write styles programmatically in Machine Learning.
•Learn Machine Learning with hands-on coding exercises. Take your Machine Learning Skill to the next level

hands on scikit learn for machine learning Course Features

•Get job-ready for an in-demand career
•Course delivery through industry experts
•Learn Core concepts from Leading Instructors
•Best Opportunity To Both Learn And Work From Home
•Fast track and Sunday Batches available on request
•Collaboration With 500+ Clients for Placements and Knowledge Sessions
•Every class will be followed by practical assignments which aggregates to minimum 60 hours.
•We do Schedule the sessions based upon your comfort by our Highly Qualified Trainers and Real time Experts

Who are eligible for Machine Learning

•.Net, Asp.net, C#, Angular, React, .Net Developer, Ui, Ui Development, Single Page Application, Sql, Product Development
•Devops, Javascript, Aws, Amazon Ec2, Angularjs, Vuejs, React.js, Node.js, Ansible, Docker, Startup, Architectural Design, Machine Learning, Python, Cloud
•Javascript, Mysql, Hybrid Developer, Html5, Css3, Php, WordPress, WordPress Cms, Html, Css, Business Development, Sales, Email Marketing, Lead
•React.Js, Javascript, Ui Development, Css, Jquery, Web Development, User Interface Designing, Cloud, AWS, Java, Spring Framework, Cassandra, Docker, Python
•Web Application Developers, Java Developers, DBA LEAD, DBA Manager, Asset Control developer, embedded software engineer, oracle applications technical


•Scikit-learn is arguably the most popular Python library for Machine Learning today. Thousands of Data Scientists and Machine Learning practitioners use it for day to day tasks throughout a Machine Learning project’s life cycle. Due to its popularity and coverage of a wide variety of ML models and built-in utilities, jobs for Scikit-learn are in high demand, both in industry and academia.
•If you’re an aspiring machine learning engineer ready to take real-world projects head-on, Hands-on Scikit-Learn for Machine Learning will walk you through the most commonly used models, libraries, and utilities offered by Scikit-learn.
•By the end of the course, you will have a set of ML problem-solving tools in the form of code modules and utility functions based on Scikit-learn in one place, instead of spread over several books and courses, which you can easily use on real-world projects and data sets.
•All the code and supporting files for this course are available on Github
•About the Author
•Farhan Nazar Zaidi has 25 years’ experience in software architecture, big data engineering, and hands-on software development in a variety of languages and technologies. He is skilled in architecting and designing networked, distributed software systems and data analytics applications, and in designing enterprise-grade software systems.
•Farhan holds an MS in Computer Science from University of Southern California, Los Angeles, USA and a BS in Electrical Engineering from University of Engineering, Lahore, Pakistan. He has worked for several Silicon-Valley companies in the past in the US as a Senior Software Engineer, and also held key positions in the software industry in Pakistan. Farhan works as consultant, solutions developer, and in-person trainer on big data engineering, microservices, advanced analytics, and Machine Learning.
•Who this course is for:
•Getting Started with a Simple ML Model in Scikit-learn
•The Course Overview
•Course Objectives, Software Installation, and Setup
•Overview of Scikit-learn
•Scikit-learn Programming Workflow Example
•Applying a KNN Model on Cancer Dataset
•Improving the KNN Performance on Cancer Dataset
•Classification Models
•Linear and Logistic Regression
•Evaluating Classification Models
•Logistic Regression and Evaluation with Scikit-learn
•Decision Trees
•Bagging, Boosting, and Random Forests
•Applying Ensemble Methods with Scikit-learn
•Support Vector Machines
•Applying Support Vector Machines Classifier with Scikit-learn
•Multi-class Classification Example with Scikit-learn
•Supervised Machine Learning – Regression
•Downloading and Inspecting the Dataset
•Handling Categorical Features and Missing Values
•Creating Train and Test Sets and Finding Correlation
•Feature Scaling, Evaluating Regression Models, and Applying Linear Regression
•Regularization Techniques for Regression Analysis
•Applying Random Forest for Regression Analysis
•Multi-Layer Perceptron, Neural Networks, and Applying MLP with Scikit-learn
•Unsupervised Learning —Dimensionality Reduction
•Principle Component Analysis
•Applying PCA with Scikit-learn for Feature Reduction
•Applying PCA for a Regression Problem on a Large Dataset
•Nonlinear Methods of Feature Extraction – t-SNE and Isomap
•Applying Dimensionality Reduction Techniques to Images
•Unsupervised Learning – Clustering
•Applying k-means with Scikit-learn
•Agglomerative Clustering
•DBSCAN Clustering Algorithm
•Applying DBSCAN with Scikit-learn
•Improving ML Model Performance
•Handling Missing Values and Data Cleaning
•Handling Missing Values and Scaling Numerical Features
•Handling Outliers and Removing Distribution Skew
•Handling Outliers and Removing Distribution Skew (Continued)
•Deriving Additional Features
•Evaluating Different Models and Cross- Validation
•Model Selection Strategies
•Feature Engineering for Classification
•Model Selection Strategies for Credit Risk Assessment
•Creating Pipelines and Advanced Model Selection
•Creating Processing Pipelines with Scikit-learn
•Using Pipelines on Our Credit Risk Assessment Dataset
•Advanced Model Selection Techniques
•Practicing Pipelines with a Time-Series Dataset
•Handling Text Data with Scikit-learn
•Bag-of-Words Model and Sentiment Analysis
•Using Stop-Words and TF-IDF for Sentiment Analysis
•Using N-Grams to Improve Model Performance for Sentiment Analysis
•Using Stemming and Lemmatization for Sentiment Analysis
•Topic Modeling with TruncatedSVD and Latent Dirichlet Allocation