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Data Science and Machine Learning- Instructor Led Course

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    Anshu Pandey
  • 2698 (Registered)
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Data Science and Machine Learning- Instructor Led Course

This course is part of specialization programs

This course can be applied to multiple Specializations or Professional programs. Completing this course will count towards your learning in any of the following programs

Pre-skills

A learner should have completed Data Analytics with Python Course 

 

Course Curriculum


Module 1 – Linear Regression – 5 Hours

  • The conceptual idea of linear regression
  • Predictive Equation
  • Cost function formation
  • Gradient Descent Algorithm
  • OLS approach for Linear Regression
  • Multivariate Regression Model
  • Correlation Analysis – Analyzing the dependence of variables
  • Apply Data Transformations
  • Overfitting 
  • L1 & L2 Regularization
  • Identify Multicollinearity in Data Treatment on Data
  • Identify Heteroscedasticity Modelling of Data
  • Variable Significance Identification
  • Model Significance Test
  • R2, MAPE, RMSE
  • Project: Predictive Analysis using Linear Regression 

 

Module 2 – Logistic Regression – 6 Hours

  • Classification Problem Analysis
  • Variable and Model Significance
  • Sigmoid Function
  • Cost Function Formation
  • Mathematical Modelling 
  • Model Parameter Significance Evaluation
  • implementing logistic regression using sklearn
  • Performance analysis for classification problem
  • Confusion Matrix Analysis
  • Accuracy, recall, precision and F1 Score
  • Specificity and Sensitivity
  • Drawing the ROC Curve
  • AUC for ROC 
  • Classification Report Analysis
  • Estimating the Classification Model
  • Project: Predictive Analysis using Logistic Regression

 

Module 3 – KNN & Decision Tree – 4 Hours

K Nearest Neighbour

  • Understanding the KNN
  • Distance metrics
  • KNN for Regression & classification
  • implementing KNN using Python
  • Case Study on KNN
  • handling overfitting and underfitting with KNN

Decision Tree

  • Forming Decision Tree
  • Components of Decision Tree
  • Mathematics of Decision Tree
  • Entropy Approach
  • Gini Entropy Approach
  • Variance – Decision Tree for Regression
  • Decision Tree Evaluation
  • Overfitting of Decision Tree
  • Handling overfitting using hyperparameters
  • Hyperparameters tuning using gridsearch
  • VIsualizing Decision Tree using graphviz

 

Module 4 – SVM & Ensemble Learning – 5 Hours

Support Vector Machines

  • Concept and Working Principle
  • Mathematical Modelling
  • Optimization Function Formation
  • Slack Variable
  • The Kernel Method and Nonlinear Hyperplanes
  • Use Cases
  • Programming SVM using Python
  • Project – Character recognition using SVM

Ensemble Learning

  • Concept of Ensemble Learning 
  • Bagging and Boosting
  • Bagging – Random Forest
  • Random Forest for Classification
  • Random Forest for Regression
  • Boosting – Gradient Boosting Trees
  • Boosting – Adaboost
  • Boosting – XGBoost
  • Stacking

 

Module 6 – Unsupervised Learning – 5 Hours

Clustering 

  • Application of clustering
  • Hierarchical Clustering
  • K Means Clustering
  • Use Cases for K Means Clustering
  • Programming for K Means using Python
  • Customer segmentation using KMeans
  • Cluster Size Optimization vs Definition Optimization
  • Projects & Case Studies

Dimensionality Reduction – PCA

  • Dimensionality Reduction, Data Compression
  • Curse of dimensionality
  • Multicollinearity
  • Factor Analysis
  • Concept and Mathematical modelling
  • Use Cases
  • Programming using Python

 

Module 7 – Capstone Project

  • Working Final Project
  • Splitting the final Project into phases
  • Working on structuring project
  • Do’s and Don’ts with Machine Learning

 

Course Features


  • More than 80% hands-on session
  • Project-oriented learning
  • Cloud-based LMS
  • Experienced Trainer
  • Masterclass for Expert
  • Standard reading materials
  • Study resources
  • Graded Quizzes
  • Real-time case study-based projects
  • Discussion Forum

 

Machine Requirement


  1. Windows Machine (Windows 7 or Above) /Linux Machine
  2. Only 64 Bit
  3. 8 GB RAM 
  4. NVIDIA Graphics Card (Recommended)

 

FeeINR 15,000/- including all Tax and Services
Duration25 Hours Hand On Training + 10 Hours Project Work
Mode of TrainingOnline Live Instructor-Led Class

 

Upcoming Batch Schedule From – 20th Aug 2020, 6:30 PM to 8 PM, IST (Monday to Friday )

Ask for one to one online meeting with our technical expert.

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Instructors

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    A Technical expert and a passionate trainer has expertise in the field of Artificial Intelligence, Machine Learning and IoT, he has a proven work record of delivering more than 100+ workshops and Technical Training in various technologies and domains at the top MNCs, premier organizations including IITs, NITs and other premier educational organizations. He has delivered 50+ corporate Training to clients from India and abroad. Skills- Programming Languages: Python, Arduino, R, MATLAB Scripting Languages: HTML, JS, CSS AI Skills: Machine Learning – Linear & Logistic Regression, Clustering, Artificial Neural Networks, SVM, Genetic Algorithm, Fuzzy Logic, Natural Language Processing, PCA, CNN, LSTM AI, ML Tools & Platforms: Python, Tensorflow, Keras, Azure ML, AWS, Apache Spark Hardware: Arduino, Raspberry Pi, AVR, Intel Edison, Xbee, LoRaWAN IoT Platforms: AWS IoT, Thingworx, ThingsBoard, IBM Watson IoT, ThingSpeak, myDevices, Twilio, node-red, Ubidots, PubNub, IoT Gateway

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