Post Graduate Program on Applied Data Science with Deep Learning and Specialisation (TEKS-RISE)

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Curriculum

21 Sections
146 Lessons
21 Weeks

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Week 1: Introduction to Data Science
6
- 1.0
  Introduction to Applied Data Science
- 1.1
  Domains, Business and Data
- 1.2
  Use Cases and Problem Statement understanding
- 1.3
  Data Science Project Life Cycle
- 1.4
  Exploratory and Colloborative Programming
- 1.5
  Quiz
Week 1-2: Collecting and Processing Data
7
- 2.0
  Advanced Data Structures and Manipulation using Python
- 2.1
  RDBMS and writing SQL
- 2.2
  Working with Files
- 2.3
  Processing Big Data
- 2.4
  Experiment Design and Analysis
- 2.5
  Data Analysis using Excel
- 2.6
  Quiz
Week 2-3: Data Science Concepts
7
- 3.0
  Supervised and Unsupervised Learning
- 3.1
  Predictive Modelling / Machine Learning
- 3.2
  Types of Algorithms
- 3.3
  Scenario-Algorithm Selection
- 3.4
  Model Training Selection
- 3.5
  Cost Functions
- 3.6
  Quiz
Week 4: Statistical Techniques
5
- 4.0
  Descriptive Statistics
- 4.1
  Inferential Statistics
- 4.2
  Probability Theory
- 4.3
  Tests of Significance
- 4.4
  Quiz
Week 4-5: Exploratory Data Analysis
9
- 5.0
  Statistical Techniques
- 5.1
  Linear Algebra
- 5.2
  Visual Exploration
- 5.3
  Data Mining
- 5.4
  Feature Engineering
- 5.5
  Machine Learning Pipelines
- 5.6
  Causal Inference
- 5.7
  Quiz
- 5.8
  EDA Project Assignment
Week 5-6: Predictive Modelling / Machine Learning 1 - Linear Regression
8
- 6.0
  Solving Prediction Problems through Linear Regression
- 6.1
  Data Exploration
- 6.2
  Pre-processing and Feature Engineering
- 6.3
  Creating an LR model
- 6.4
  Optimizing Models through RFE and VIF methods
- 6.5
  Testing LR Assignments
- 6.6
  LR Project Assignment
- 6.7
  Quiz
Week 6-7: Predictive Modelling / Machine Learning 2 - Logistic Regression
8
- 7.0
  Estimating Probabilities
- 7.1
  Logistic Regression Cost Functions
- 7.2
  Softmax Regression
- 7.3
  Performance Matrix
- 7.4
  Performance Matrix
- 7.5
  Optimizing Logistic Regression
- 7.6
  Quiz
- 7.7
  Logistic Regression Project Assignment
Week 7-8: Predictive Modelling / Machine Learning 4 - Decision Tree and Random Forest
8
- 8.0
  Introduction to Decision Trees
- 8.1
  Gini Index and Entropy
- 8.2
  Measuring Performance
- 8.3
  Introduction to Random Forest
- 8.4
  Random Forest Process Steps
- 8.5
  Model Performance and Tuning
- 8.6
  Quiz
- 8.7
  Random Forest Project Assignment
Week 8: Predictive Modelling / Machine Learning 5 - Dimensionality Reduction
9
- 9.0
  Introduction to PCA(Predictive Component Analysis)
- 9.1
  PCA Process Steps
- 9.2
  Data Standardization
- 9.3
  Finding Covariance Matrix of our Dataset
- 9.4
  Eigen Vectors and Eigen Values
- 9.5
  Recast Data using new PCAs
- 9.6
  Expained Variance Ration and Screen Plot
- 9.7
  Quiz
- 9.8
  PCA Project Assignment
Week 9: Predictive Modelling / Machine Learning 6 - Advanced Classifier: SVM
6
- 10.0
  Introduction to Support Vector Machine
- 10.1
  Linear SVM Classifications
- 10.2
  Non-Linear SVM Classification
- 10.3
  Polynomial Kernel Trick
- 10.4
  Quiz
- 10.5
  SVM Project Assignment
Week 9-10: Predictive Modelling / Machine Learning 7 - Clustering using K-Means
8
- 11.0
  Unsupervised Learning
- 11.1
  Introduction to PCA
- 11.2
  PCA Process Steps
- 11.3
  Centroid Optimizing / Convergence
- 11.4
  Other Considerations
- 11.5
  Optimizing Number of Clusters
- 11.6
  Quiz
- 11.7
  K-Means Project Assignment
Week 11: Neural Networks and Deep Learning
8
- 12.0
  Neural Nets
- 12.1
  Perceptron and MLP (Multi Layered Perceptron)
- 12.2
  Introduction to TensorFlow
- 12.3
  Introduction to Keras
- 12.4
  Classification MLPs
- 12.5
  Regression MLP
- 12.6
  Quiz
- 12.7
  Deep Neural Network Project Assignment
Week 12: CNN and Image Processing
8
- 13.0
  CNN Architectures
- 13.1
  Training a CNN
- 13.2
  Image Pre-processing Techniques
- 13.3
  Transfer Learning with CNN
- 13.4
  TensorFlow Implementation of CNN
- 13.5
  Object Detection
- 13.6
  Quiz
- 13.7
  Object Detection Project Assignment
Week 13: RNN and LSTM
7
- 14.0
  RNN Architectures
- 14.1
  Training an RNN
- 14.2
  Unstable Gradients Challenge
- 14.3
  LSTM Architecture
- 14.4
  Training and Predicting a Time Series
- 14.5
  Quiz
- 14.6
  LSTM Project Assignment
Week 14: Natural Language Processing with RNN and Attention Models
7
- 15.0
  Generating Text using a RNN
- 15.1
  Bidirectional RNN
- 15.2
  Attention Mechanism
- 15.3
  Encoder-Decoder Models
- 15.4
  Natural Machine Translation using an Encoder-Decoder Model
- 15.5
  Quiz
- 15.6
  NLP Project Assignment
Week 15: ML-Ops using Google Cloud Platform
7
- 16.0
  Understanding ML Lifecycle
- 16.1
  Creating Docker Containers for ML Deployments
- 16.2
  Manage Kubernetes Deployments
- 16.3
  Setting up AI Pipelines
- 16.4
  Training, Tuning and Serving a Model
- 16.5
  Kubeflow Pipelines
- 16.6
  CI/CD on KubeFlow Pipelines
Week 14-17: Capstone Project
3
- 17.0
  Choice and Briefing of Capstone Projects
- 17.1
  Research, Analysis and Methodology
- 17.2
  Presentation Techniques – Paper and Visual
Week 16: Deep Learning Product Development
5
- 18.0
  Current AI Trends
- 18.1
  Start-ups and Products in Data Science/AI/ML
- 18.2
  Upcoming Genre of Products
- 18.3
  Understanding AI Products Development Lifecycle
- 18.4
  Deep Learning Market
Specialisation Options
0
Option 1: Tableau
11
- 20.0
  Create various Charts and Graphs
- 20.1
  Create Dashboards
- 20.2
  Create Data Hierarchies
- 20.3
  Story-telling
- 20.4
  Working with Parameters
- 20.5
  Creating Data Extracts
- 20.6
  Applying Filters
- 20.7
  Calculated Fields
- 20.8
  Mapping in Tableau
- 20.9
  Advanced Charts
- 20.10
  Publish to Tableau Online
Option 2: R Language
9
- 21.0
  Fundamentals of R
- 21.1
  Vectors
- 21.2
  Functions
- 21.3
  Packages
- 21.4
  Matrix
- 21.5
  DataFrames
- 21.6
  Visualisations using GGPlot2
- 21.7
  Creating a Linear Regression Model using R
- 21.8
  Creating a Logistic Regression Model using R

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Logistic Regression Project Assignment

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