Complete Data Science DS Programming Specialization ML Specialization ML Ops Specialization Deep Learning Specialization Forecasting Specialization Industry Projects Specialization Supplementary Courses

The Complete Data Science Pathway

A Complete Roadmap of Courses to become Fundamentally Strong in ML and implement Industrial DS Projects.
Includes ALL specializations (incl DS project courses)

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Steps to Machine Learning Expert

Step 1

Data Science Programming Specialization

Learn complete programming skills for Data Science and practice real use cases.

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Foundations of Machine Learning

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Complete Python Programming

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Numpy for Data Science

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Pandas for Data Science

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SQL for Data Science - Level I (Basics)

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SQL for Data Science - Level II (Intermediate)

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SQL for Data Science - Level III (Advanced)

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SQL for Data Science - Window Functions

Step 2

Machine Learning Specialization

Master complete machine learning concepts, algorithms and applications.

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Linear Algebra for Machine Learning

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Actively Updating

Statistics for Data Science

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Data Pre-processing and EDA

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Linear Regression and Regularisation

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Classification:Logistic Regression

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Imbalanced Classification

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Supervised ML Algorithms

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Ensemble Learning

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Fixed and mixed effects modeling

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Recommendation Systems - Deep Dive

Explaining ML Models: Deep Dive

Step 3

ML Ops Specialization

Learn how to deploy models into actual applications and build your own AI product.

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ML Deployment in AWS EC2

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Deploy ML Models in AWS Lambda

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Deploy ML Models in AWS Sagemaker

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PySpark for Data Science - I: Fundamentals

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PySpark for Data Science - II: Statistics for Big Data

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PySpark for Data Science - III: Data Cleaning and Analysis

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PySpark for Data Science - IV: Machine Learning

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PySpark for Data Science - V: ML Pipelines

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MLFlow in Action: Hands on guide to ML experiments

Step 4

Time Series Forecasting Specialization

Become the expert in all time series models and solve real forecasting projects.

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Introduction to Time Series Analysis

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Time Series Analysis - I (Beginners)

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Time Series Analysis - II (Intermediate)

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Time Series Forecasting Part 1 - Statistical Models

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Time Series Forecasting Part 2 - ARIMA Modeling and Tests

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Time Series Forecasting Part 3 - Vector Auto Regression

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Time Series Forecasting III - Singular Spectrum Analysis (SSA)

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Feature Engineering for Time Series Projects - I

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Feature Engineering for Time Series Projects - II

Step 5

Industry Projects Specialization

Solve industry projects with real data and complexities, just like on-the-job Data Scientist.

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Estimating Customer Lifetime Value for Business

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Microsoft Malware Detection Project

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Credit Card Fraud Detection

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Restaurant Visitor Forecasting

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Optimizing Marketing Budget Spend with Market Mix Modelling

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Predict Rating given Amazon Product Reviews using NLP

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Uplift modeling: Estimating incremental impact of marketing campaigns

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Uplift modeling Part 2: Modeling Strategies

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Survival Analysis Part 1: Predicting Time to Event in real world applications

Survival Analysis Part 2: Predicting Time to Event for lungs cancer patients

Attribution Models in Marketing

Dynamic Pricing Using Multi Armed Bandit (Reinforcement Learning)

Reinforcement learning for Online Ad Serving with Multi Armed Bandits

Portfolio Optimization of volatile assets Efficient Frontier Python

Conjoint Analysis - Project Deep Dive

Step 6

Deep Learning Specialization

Master new age computer vision and natural language processing and build your own applications.

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Foundations of Deep Learning in Python

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Foundations of Deep Learning Part-2

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Applied Deep Learning with PyTorch

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Detecting Defects in Steel Sheets with ComputerVision

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Project Text Generation using Language Models with LSTM

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Project Classifying Sentiment of Reviews using BERT NLP

Step 7

Supplementary Courses

Learn how to make the data ready for ML model building and drive actionable business insights from the data using extensive EDA.

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Spacy for NLP

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Base R Programming

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Dplyr for data wrangling

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Wrangling data with Data Table

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GGPlot2 visualization for data analysis

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Statistical Foundations for ML in R

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Regression Model in R

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Caret package in R

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