Location:Main Road, Bangalore


Machine Learning With Scikit Learn In 7 Hours




Machine Learning Online Training


Graduates and Technology Aspirants


Online and Classroom Sessions


Week Days and Week Ends

Duration :

1.5  hrs in weekdays and 3hrs during Weekend

Machine Learning What will you learn?

•Basic to Advance concepts of Machine Learning
•Master the most important aspects of Machine Learning.
•Learn how to write high-quality code using Machine Learning.
•How to store and handle file upload in Machine Learning.
•Learn Machine Learning from Scratch with Demos and Practical examples.
•The best way to learn modern Machine Learning step-by-step from scratch.
•Learn how to implement the all the functionalities of a Machine Learning.
•Learn Machine Learning from Scratch and Achieve Highest Knowledge with Practical Examples
•Amazing Step by Step Guide for Beginners to Learn Machine Learning Language Quick and Simple!

machine learning with scikit learn in 7 hours Course Highlights

•Real-world skills + project portfolio
•Training by Industry expert professionals
•Real time live project training and Guidance
•Regular Brush-up Sessions of the previous classes
•Facility of Lab on cloud available (based on booking)
•Access to a huge closet containing information about Hadoop
•We provide one to one mentorship for the students and Working Professionals
•Very in depth course material with Real Time Scenarios for each topic with its Solutions for Online Trainings.

Who are eligible for Machine Learning

•C#.net developer, Manual Testing, Automation Testing, Android Development, Android Tester, Software Testing, PHP
•embedded platform software engineers, embedded multimedia developer, Middleware Developers, Android Middleware, device driver developers, c, c++, linux
•Java Programmer, Ui Designer, Web Developer, Web Designer, Automation Testing, graphic designer visualiser, java script frameworks, PHP
•scala, React.js, Backend Developers, Frontend Developers, Fullstack Developers, Ui/ux Designers, Test Engineering, Site Reliability Engineer, Machine Learning
•Software Engineer, Software Developer, Business Analyst, manager, Delivery Manager, Team Lead, .Net Framework, Java Framework, Mobile Application Development


•Fundamentals of Machine Learning with scikitlearn
•The Course Overview
•Machine Types and Learning Methods
•Data Formats
•Statistical Learning Approaches
•Elements of Information Theory
•Splitting Datasets
•Managing Data
•Data Scaling and Normalization
•Principal Component Analysis
•Linear Models and Its Example
•Linear Regression with scikitlearn
•Ridge Lasso and ElasticNet
•Regression Types
•Logistic Regression
•Stochastic Gradient Descent Algorithms
•Finding the Optimal Hyperparameters
•Classification Metrics
•ROC Curve
•Bayes Theorem
•Naive Bayes in scikitlearn
•scikitlearn Implementation
•Controlled Support Vector Machines
•Binary Decision Trees
•Decision Tree Classification with scikitlearn
•Ensemble Learning
•Clustering Basics
•DBSCAN and Spectral Clustering
•Evaluation Methods Based on the Ground Truth
•Agglomerative Clustering
•Implementing Agglomerative Clustering
•Connectivity Constraints
•UserBased Systems
•ContentBased Systems
•Test your knowledge
•Learn Machine Learning in Hours
•Operation of an Unsupervised Machine Learning Algorithm
•Operation of a Supervised Machine Learning Algorithm
•Avoid Overfitting and Splitting Data into Training and Testing Sets
•Data Cleaning Conversion and Preprocessing
•Using PCA to Easily Explore and Visualize Data
•What Does the Unsupervised KMeans Clustering Algorithm Do
•Example Problem
•Data Preparation and Processing
•Implementing KMeans Clustering
•Improving Performance and Hyperparameter Fitting
•Operation of the KNearestNeighbor Classification Algorithm
•Implementing KNearestNeighbor Classification
•Operation of the Support Vector Machine Classification Algorithm
•Implementing Support Vector Machine Classification
•Operation of the Support Vector Machine Regression Algorithm
•Implementing Support Vector Machine Regression
•Operation of the Gradient Boosting Algorithm
•Implementing Gradient Boosting Classification
•RealWorld Machine Learning Projects with ScikitLearn
•Exploring the Dataset and Identifying the Problem
•Multiple Linear Regression
•Implementing the Solution
•Evaluating and Improving the Model
•Analyzing the Results
•Decision Trees and Random Forest
•Feature Analysis and Engineering
•Analyze the Results
•Support Vector Machines
•KMeans Clustering
•Hierarchical Clustering
•Fundamentals of Machine Learning with scikit-learn
•Linear Regression with scikit-learn
•Ridge, Lasso, and ElasticNet
•Bayes’ Theorem
•Naive Bayes’ in scikit-learn
•scikit-learn Implementation
•Decision Tree Classification with scikit-learn
•User-Based Systems
•Content-Based Systems
•Learn Machine Learning in 3 Hours
•Data Cleaning, Conversion, and Preprocessing
•What Does the Unsupervised K-Means Clustering Algorithm Do?
•Implementing K-Means Clustering
•Operation of the K-Nearest-Neighbor Classification Algorithm
•Implementing K-Nearest-Neighbor Classification
•Real-World Machine Learning Projects with Scikit-Learn
•K-Means Clustering