Data Science

Ready to embark on an exciting journey into the world of data? Our cutting-edge Data Scientist Course is designed to transform your curiosity into tangible skills and make you a master of data-driven decision-making!

  • Gain specific skills with specialised courses
  • We provide the guaranteed lowest price in the industry
  • Go beyond stats with Python
  • Learn how to generate valuable insights from data
  • Delivered by world-class instructors in nationwide luxury venues

Machine Learning

  • Walking with Python and R
  • Understanding Python and R
  • Machine Learning Techniques
  • Types of Learning
  • Supervised Learning
  • Unsupervised Learning
  • Advice for Applying Machine Learning
  • Machine Learning System Design
  • Regression
  • Simple Linear Regression
  • Multiple Linear Regression
  • Logistic Regression
  • Classification
  •  Decision Tree
  • Naive Bayes
  • KNN(K nearest Neighbor)
  • Random Forest
  • Naive Bayes
  • Support Vector Machines (SVM)
  •  Gradient Boosting algorithms
  • Predicting house prices: A case study in Regression
  • Linear Regression & Logistic: A Model-Based Approach
  • Regression fundamentals : Data and Models
  • Feature selection in Model building
  • Evaluating over fitting via training/test split
  • Training/ Test curves
  • Adding other features
  • Regression ML block diagram
  • Analyzing the sentiment of reviews: A case study in Classification
  • Classification fundamentals: Data and Models
  •  Types of classifications
  •  Feature selection in Model building
  • Linear classifiers
  • Decision boundaries
  •  Training and evaluating a classifier
  • False positives, false negatives, and confusion matrices
  •  Classification ML block diagram
  • Unsupervised Learning
  • Clustering
  • Recommendation
  • Deep Learning
  • Unsupervised Learning – Clustering
  •  Clustering System Overview
  •  Clustering fundamentals: Data and Models
  •  Types of clustering
  • K Means Clustering
  • Hierarchical Clustering
  • DB Scan Clustering
  • Difference between K Means and Hierarchical clustering
  •  Feature selection in Model building
  •  Clustering and similarity ML block diagram
  • Recommending Products
  • Recommender systems overview
  • Collaborative filtering
  • Understanding Collaborative Filtering and Support Vector Machine
  • Effect of popular items
  • Normalizing co-occurrence matrices and leveraging purchase histories
  • The matrix completion task
  • Recommendations from known user/item features
  • Recommender systems ML block diagram

Deep Learning and Artificial Intelligence

  • Installation of Tensor flow
  • Basics of Tensor flow with real-time project
  • Image classification with Tensor flow  real-time project
  • Speech to Text with Tensor flow real-time project
  • OCR with OpenCv with real-time project
  • Object detection with Tensor flow real-time project
  • Deep Learning Concepts like Neural Networks, AI
  • image captioning with Tensor flow real-time project
  • Deep Learning: Searching for Images
  • Searching for images: A case study in deep learning  
  • Learning very non-linear features with neural networks
  • Application of deep learning to computer vision
  • Deep learning performance
  • Demo of deep learning model on Image Net data
  • Deep learning ML block diagram
  • Deploying Tensorflow deep learning models in production.
  • NLTK
  • Topic Extraction.
  • NLTK Tokenizes
  • Word Stemming
  • Named entity Reorganization
  • POS Tagging
  • Sentiment Analysis
  • Google Cloud
  • Microsoft Azure Cloud
  • AI cloud
  • Model Building and Deployment in Microsoft Azure Cloud
  • Model Building and Deployment in H2O.AI Cloud
  • Deep Learning Model Building and Deployment in Google Cloud

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