Data Science & Machine Learning

Making Data-Driven Decisions
Program Overview
Key Highlights
  • 1400+ Hours of Learning
  • Designed for Professionals
  • 15+ Case Studies and Assignments
  • 6 Practical Hands-on Capstone Projects
  • Live Coding Classes & Profile Building Workshops
  • No Cost EMI Options available
  • Dedicated Student Success Mentor
  • 3 Electives to customize your learning path
  • Learning management system
  • Daily Doubt Resolution Sessions
  • Career Mentorship Sessions(1:1)
  • High Performance Coaching(1:1)
  • Exclusive Job opportunities
  • AI Powered Profile Builder
  • Personalised Industry Session
  • Creation of portfolio website on Github to boost the learners’ career persona
Tool Covered for Data Science & Machine Learning
K Keras
Tensor Flow
Scikit Learn
And More....
Data Science is the field which deals with large data and convert it into valuable information with the help of scientific principles and advanced analytical techniques where as machine learning is a subfield of data science and in which the systems can learn from data, identify patterns and make decisions with minimal human involvement.
Skills You Will Learn
  1. Coding Skills & Programming
  2. Marketing Strategies
  3. Business Finance
  4. Foundations of Data Science/ Python for Data Science
  5. Spectral Clustering, Components, and Embeddings
  6. Predictive Analytics
Module 1 : Foundations of Data Science/ Python for Data Science
  • Numpy
  • Pandas
  • Data Visualization
  1. Stats for Data Science
  • Descriptive Statistics
  • Inferential Statistics
Module 2 : Making Sense of Unstructured Data Introduction
  • What is unsupervised learning, and why is it challenging?
  • Examples of unsupervised learning
  1. Clustering
  • What is Clustering?
  • When to use Clustering
  • K-means Preliminaries
  • The K-means algorithm
  • How to evaluate Clustering
  • Beyond K-means: What really makes a Cluster?
  • Beyond K-means: Other notions of distance
  • Beyond K-means: Data and pre-processing
  • Beyond K-means: Big data and Nonparametric Bayes
  • Beyond Clustering
  1. Spectral Clustering, Components, and Embeddings
  • What if we do not have features to describe the data or not all are meaningful?
  • Finding the principal components in data and applications
  • The magic of Eigenvectors I
  • Clustering in Graphs and Networks
  • Features from graphs: The magic of Eigenvectors II
  • Spectral Clustering
  • Modularity Clustering
  • Embeddings: New features and their meaning
Module 3 : Regression and Prediction
MasterClass on Regression and Prediction
  1. Classical Linear and Non-Linear Regression and Extensions
  • Linear Regression with one and several variables
  • Linear Regression for prediction
  • Linear Regression for causal inference
  • Logistic and other types of Non-Linear Regression
  1. Modern Regression with High-Dimensional Data
  • Making good predictions with high-dimensional data; avoiding overfitting by validation and cross-validation
  • Regularization by Lasso, Ridge, and their modifications
  • Regression Trees, Random Forest, Boosted Trees
  1. The Use of Modern Regression for Causal Inference
  • Randomized control trials
  • Observational studies with confounding
Module 4 : Classification and Hypothesis Testing
  • What are anomalies? What is fraud? What are spams?
  • Binary Classification: False Positive/Negative, Precision/Recall, F1-Score
  • Logistic and Probit Regression: Statistical Binary Classification
  • Hypothesis Testing: Ratio Test and Neyman-Pearson p-values: Confidence Support Vector Machine: Non-statistical Classifier
  • Perceptron: Simple Classifier with elegant interpretation
Module 5 : Deep Learning
  • What is Image Classification? Introduce ImageNet and show examples
  • Classification using a single linear threshold (perceptron)
  • Hierarchical representations
  • Fitting parameters using back-propagation
  • Non-convex functions
  • How interpretable are its features?
  • Manipulating Deep Nets (Ostrich Example)
  • Transfer Learning
  • Other applications I: Speech Recognition
  • Other applications II: Natural Language Processing
Module 6 : Recommendation Systems
  1. Recommendations and Ranking
  • What does a recommendation system do?
  • What is the Recommendation Prediction Problem? What data do we have?
  • Using population averages
  • Using population comparisons and ranking
  1. Collaborative Filtering
  • Personalization using collaborative filtering using similar users
  • Personalization using collaborative filtering using similar items
  • Personalization using collaborative filtering using similar users and items
  1. Personalized Recommendations
  • Personalization using comparisons, rankings, and users-items
  • Hidden Markov Model/Neural Nets, bipartite graph, and graphical model
  • Using side-information
  • 20 questions and active learning
  • Building a system: algorithmic and system challenges
Module 7 : Networking and Graphical Models
  1. Introduction
  • Introduction to networks
  • Examples of networks
  • Representation of networks
  1. Networks
  • Centrality measures: degree, eigenvector, and page-rank
  • Closeness and betweenness centrality Degree distribution, clustering, and small world
  • Network models: Erdos-Renyi, configuration model, preferential attachment
  • Stochastic models on networks for the spread of viruses or ideas
  • Influence maximization
  1. Graphical Models
  • Undirected graphical models
  • Ising and Gaussian models
  • Learning graphical models from data
  • Directed graphical models
  • V-structures, “explaining away,” and learning directed graphical models
  • Inference in graphical models: marginals and message passing
  • Hidden Markov Model (HMM) Kalman Filter
Module 8 : Predictive Analytics
  • Predictive Modeling for Temporal Data
  • Prediction engineering
  1. Feature Engineering
  • Introduction
  • Feature types
  • Deep Feature Synthesis: primitives and algorithms
  • Deep Feature Synthesis: stacking
Duration - 6 Months
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