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Advanced Machine Learning with Deep Learning Training

Overview

Machines have been an integral part of our lives since the industrial revolution. As we enter the era of Industry 4.0, they’ve become vastly more sophisticated, and integrated within our daily lives. As the professionals who lead the development and growth of this sophistication, it’s imperative that we embrace the latest technologies and innovations by keeping pace with our knowledge of these disruptive emerging technologies – including machine learning, artificial intelligence, blockchain, cloud computing, big data, and more.

Adobe reports that the share of jobs requiring artificial intelligence has increased by 450% in the last five year, with 47% of digitally mature organizations having a defined AI strategy in place.

According to Monster.com, the three most in-demand skills are Machine Learning, Deep Learning and Natural Language Processing.

With modern technologies evolving rapidly, staying competitive means keeping pace with the latest skills and capabilities. Taking courses that incorporate advanced machine learning concepts with deep learning in one complete package is crucial to maintaining your skillsets and continuing to meet the demands of the industry.

Cognixia offers a comprehensive training package with a hands-on case study approach, enabling participants to explore the practical aspects of advanced level machine learning, artificial intelligence, and deep learning.

The course is a great fit for the career paths of IT professionals, electrical and electronic engineers, designers, and solution architects – as well as entrepreneurs who are keen to employ these technologies for their business. Cognixia highly recommends this course for professionals who work in the pharmaceuticals, real estate, sales, finance, designing, manufacturing, electrical, retail, and healthcare domains.

Schedule Classes

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Curriculum

  • Introduction to Artificial Intelligence & Machine Learning
  • Overview- AI Vs ML Vs Deep Learning
  • Overview- Subfields of Artificial Intelligence- Robotics, ML, NLP, Computer Vision
  • Applications of Machine Learning/AI
  • Difference b/w AI & Programmed Machine
  • R & R Studio Setup & Installation
  • Quick tour of R-Studio – Variables, Install, Plot, help, console, repository
  • Important Links to get datasets – Kaggle, data.gov etc
  • Classes & Objects
  • Vector and List in R
  • Hands-on
  • Matrix & Factor in R
  • Hands-on
  • Dataframe in R
  • Plotting using gggplot2 in R – Scatter plot, Box plot, Hist, Bar chart etc
  • N-Dimensional Array in R
  • Table function in R
  • Hands-on
  • Statistics in R – Mean, Median, Mode, Range, Variance, SD, Inter Quartile
  • Twitter- R Integration
  • Get data from MYSql using R
  • Get data from website using R
  • Hands-on
  • Steps involved in solving a Machine Learning Usecase
  • Data preprocessing/preparation in R
  • Missing data, Categorical data, Feature Scaling, Splitting data to test & train sets
  • Hands-on with sample data
  • Types of Machine Learning- Supervised & UnSupervised Machine Learning
  • Supervised Learning – Regression & Classification
  • UnSupervised Learning- Clustering
  • Regression Algorithm- Simple Linear Regression
  • UseCase: Create a Model to predict Salary from years of exp
  • Classification Algorithm- K Nearest Neighbour
  • UseCase: Create a Model to predict if a particular customer will purchase a product or not
  • Hands-on with Sample data
  • Clustering Algorithm- Kmeans
  • Elbow Method in Kmeans to predict optimal no. of Clusters
  • Clustering Algorithm- Hierarchical Clustering
  • Dendograms in Hierarchical Clustering to predict optimal no. of Cluster
  • UseCase: Using Kmeans & HC to extract patterns to analyse customer data based on spending score and income
  • Hands-on with Sample data
  • Logistics Regression
  • UseCase: Create a Model to predict if a particular customer will purchase a product or not
  • How to create and read ROC curve
  • How to check the accuracy of the Model using Confusion Matrix
  • Hands-on with Sample data
  • Random Forest using Decision Trees
  • Support Vector Machine for Classification
  • UseCase: Create a Model using Random Forest & SVM to predict if a particular customer will purchase a product or not
  • How to create and read ROC curve
  • How to check the accuracy of the Model using Confusion Matrix
  • Hands-on with Sample data
  • Polynomial Regression
  • UseCase: Create a Model to predict Salary from years of exp
  • UseCase: Satellite Image Classification using Random Forest. Create a Model to identify/classify different types of land re.g barren, forest, urban, river etc from a Satellite image
  • Hands-on with Sample data
  • Dimensionality Reduction
  • Feature Selection Vs Feature Extraction
  • Feature Selection using Backward Elimination technique
  • Feature Extraction using PCA
  • Hands-on with Sample data
  • How to tune/check accuracy of Model using P- Value, R Square, Adjusted R Square, CAP
  • Overview of NLP/Text Mining
  • Libraries in R for NLP/text mining – tm, Snowball, dplyr
  • Bag of words using R
  • Use Case: Restaurents Review System
  • Sentiment Analaysis using R
  • Usecase: Analyse twitter data for two teams to predict sentiments
  • Hands-on with Sample data
  • Overview of types of recommendation engines – Example Ecommerce, Netflix etc
  • Frequently bought items , User Based Collaborative Filtering
  • Libraries in R for recommendation – recommenderlab
  • Use Case: Analyse grocery store data to find out frequently bought together item
  • Use Case: Analyse jokes data to recommend best jokes to users
  • Hands-on with Sample data
  • Time Series data analysis in R
  • Components in time series – Trend, Seasonality
  • Arima Model Vs ETS Model
  • Use Case: Forecast Flight booking from Airline data
  • Sentiment Analysis using R
  • Hands-on with Sample data
  • Deep Learning Introduction
  • Limitations of ML and how Deep Learning comes to rescue
  • Biological Neural Network Vs Artificial Neural Network
  • Popular Frameworks of Deep Learning – Tensorflow, Keras
  • Understanding Deep Learning Terminologies – Input Layer, Hidden Layer, Output Layer, Activation Function, Cost Function, Back Propogation, Gradient Descent, Epoch, Learning Rate
  • Install Keras (using tensorflow)
  • Use Case: Create a model using ANN for boston housing data
  • Convolutional Neural Network
  • Convolution, Polling, Flattening
  • Use Case: Image classification using CNN
  • Hands-on with Sample data

Case Study – Predict Customer Churn

Case Study – Canada Crime Analysis

Summary & QA

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Course features

Course Duration
Course Duration

36 hours of live, online, instructor-led training

24x7 Support
24x7 Support

Technical & query support round the clock

Lifetime LMS Access
Lifetime LMS Access

Access all the materials on LMS anytime, anywhere

Price Match Gurantee
Price match Gurantee

Guranteed best price aligning with quality of deliverables

FAQs

  • Data engineers, technical business analysts, data scientists, Hadoop developers, and many more.
  • Entrepreneurs who are keen to build and deliver innovative solutions for their customers.

Yes, all our sessions are recorded. Therefore, if you ever miss a class, you will be able to view it on our LMS.

The course material is accessible for a lifetime, post-training

After you successfully complete the training program, you will be evaluated on parameters such as attendance in sessions, an objective examination, and other factors. Based on your overall performance, you will be certified by Cognixia.