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Practical Supervised And Unsupervised Learning With Python




Python IT Institute


Freshers and Career Changers


Online and Classroom Sessions


Week Days and Week Ends

Duration :

1.5  hrs in weekdays and 3hrs during Weekend

Python What will you learn?

•Learn how to code in Python.
•How to implement Python on different Platforms.
•Implement Python in your apps and integrate it.
•How to store and handle file upload in Python.
•Learn Everything you need to know about Basic Python
•You can learn Python to code like a pro!
•Learn A to Z of Python from Basic to ADVANCE level.
•Learn all the topics from Python from the basics to advanced topics
•Gain the ability to adapt to any coding language with the concepts of Python

practical supervised and unsupervised learning with python Training Highlights

•We are Known for High-Quality Training
•Exercises and handouts after every session
•Software & others tools installation Guidance
•We enage Experienced trainers for Quality Training
•Fast track and Sunday Batches available on request
•Courseware includes reference material to maximize learning.
•Training time :  Week Day / Week End – Any Day Any Time – Students can come and study
•Very in depth course material with Real Time Scenarios for each topic with its Solutions for Online Trainings.

Who are eligible for Python

•Artificial Intelligence, Data Science, Block Chain, Iot, Cloud Computing, Ux Design, Mobile Application Development, Natural Language Processing, Business
•embedded platform software engineers, embedded multimedia developer, Middleware Developers, Android Middleware, device driver developers, c, c++, linux
•Ms Crm, Guidewire, Sdm, Sde2, Qae, Sdet, Jbpm, Ext Js, Windows Admin, Full Stack, Aem, Spark, Hadoop, Big Data, Data Engineer, Azure, Cloud, Opentextnetworking, Test Cases, Automation Testing, perl, python, Protocol Testing, http, l4, l7, dns, tcp, ip, smtp, Cloud Computing, l3, l2, pig
•Xml Publisher, Php Developer, Android Application Development, Html Tagging, E-publishing, Software Development


Hands-On Unsupervised Learning with Python
•The Course Overview
•Benefits of Unsupervised Learning
•How Market Basket Analysis Works
•How Market Basket Analysis Works (Continued)
•The Apriori Algorithm – Preparing the Data
•Understanding and Implementing the Apriori Algorithm
•Finding Association Rules
•Visualizing and Interpreting Association Rules
•Unsupervised Learning and the Curse of Dimensionality
•Approaches to Dimensionality Reduction
•The Key Ideas Behind PCA
•The Key Ideas Behind PCA (Continued)
•The Linear Algebra Behind PCA
•The Linear Algebra Behind PCA (Continued)
•PCA in Practice
•PCA in Practice (Continued)
•Clustering – Key Concepts
•Clustering Algorithm in Practice
•Evaluate Clustering Results
•Case Study – K-Means and Wholesale Data
•Case Study – K-Means and Wholesale Data (Continued)
•Test Your Knowledge
•Hands-on Supervised Machine Learning with Python
•Getting Our Machine Learning Environment Setup
•Supervised Learning
•Hill Climbing and Loss Functions
•Model Evaluation and Data Splitting
•Introduction to Parametric Models and Linear Regression
•Implementing Linear Regression from Scratch
•Introduction to Logistic Regression Models
•Implementing Logistic Regression from Scratch
•Parametric Models –Pros/Cons
•The Bias/Variance Trade-off
•Introduction to Non-Parametric Models and Decision Trees
•Decision Trees
•Implementing a Decision Tree from Scratch
•Various Clustering Methods
•Implementing K-Nearest Neighbors from Scratch
•Non-Parametric Models –Pros/Cons
•Recommender Systems and an Introduction to Collaborative Filtering
•Matrix Factorization
•Matrix Factorization in Python
•Content-Based Filtering
•Neural Networks and Deep Learning
•Neural Networks
•Use Transfer Learning
•Supervised and Unsupervised Learning with Python
•Artificial Intelligence and Its Need
•Applications and Branches of AI
•Defining Intelligence Using Turing Test
•Making Machines Think Like Humans
•General Problem Solver
•Building an Intelligent Agent
•Installing Python 3 and Packages
•Loading Data
•Supervised Versus Unsupervised Learning
•What is Classification?
•Preprocessing Data
•Label Encoding
•Logistic Regression and Naïve Bayes Classifier
•Confusion Matrix
•Support Vector Machines
•Classifying Income Data
•What is Regression?
•Building a Single and Multivariable Regressor
•Estimating Housing Prices
•What is Ensemble Learning?
•What Are Decision Trees
•What are Random and Extremely Random Forests?
•Dealing with Class Imbalance
•Finding Optimal Training Parameters
•Computing Relative Feature Importance
•Predicting Traffic
•Clustering Data with K-Means Algorithm
•Estimating the Number of Clusters
•Estimating the Quality of Clustering
•Building a Classifier
•Segmenting the Market
•Creating a Training Pipeline
•Extracting the Nearest Neighbors
•Building a K-Nearest Neighbors Classifier
•Computing similarity scores
•Finding Similar Users
•Building a Movie Recommendation System