Advanced Machine Learning with Deep Learning Training

Duration: 36 Hours
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Machines have been an integral part of our lives since the industrial revolution. They’ve become vastly more sophisticated, and integrated into our daily lives in this era of Industry 4.0. It’s imperative that we embrace the latest technologies and innovations, as professionals who lead the development and growth of sophistication. By keeping pace with our knowledge of disruptive emerging technologies – including ML, AI, blockchain, cloud computing, big data, and more. So, specialization course that covers an introduction to advanced Machine Learning with Deep Learning training.

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, 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. We also offer 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 and for 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.


  • 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, 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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  • 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.

Interested in this Course?

    Ready to recode your DNA for GenAI?
    Discover how Cognixia can help.

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