Advanced Machine Learning with Deep Learning

Machine Learning & Analytics

Course Description

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.

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.

60 Hours

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

Day 1

  • 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

Day 2

  • Classes & Objects
  • Vector and List in R
  • Hands-on

Day 3

  • Matrix & Factor in R
  • Hands-on

Day 4

  • 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

Day 5

  • 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

Day 6

  • 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

Day 7

  • 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

Day 8

  • 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

Day 9

  • 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

Day 10

  • 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

Day 11

  • 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

Day 12

  • 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

Day 13

  • 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

Day 14

  • 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

Day 15

  • 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

Day 16

  • 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

Day 17

  • Convolutional Neural Network
  • Convolution, Polling, Flattening
  • Use Case: Image classification using CNN
  • Hands-on with Sample data

Day 18

  • Case Study – Predict Customer Churn

Day 19

  • Case Study – Canada Crime Analysis

Day 20

  • Summary & QA

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