Machine Learning Plus | Complete University Access Curriculum

Go to Website | View Plans

Part 1: Data Science Programming Expert

Course 1: Foundations of Machine Learning

Course 2: Python Programming

Course 3: NumPy for Data Science

Course 4: Pandas for Data Science

Course 5: Linux Command Line

Course 6: SQL for Data Science - Level I

Course 7: SQL for Data Science - Level II

Course 8: SQL for Data Science - Level III

Course 9: SQL for Data Science - Window Functions

Part 2: Machine Learning Expert

Course 1: Linear Algebra for Machine Learning

Course 2: Statistics for Data Science

Course 3: Data Pre - processing and EDA

Course 4: Linear Regression and Regularisation

Course 5: Classification: Logistic Regression

Course 6: Imbalanced Classification

Course 7: Supervised ML Algorithms

Course 8: Ensemble Learning

Part 3: MLOps Expert

Course 1: Launch ML App in AWS EC2

Course 2: Launch ML App in AWS Lambda

Course 3: Launch ML App in AWS Sagemaker, Tutorials

Course 4: PySpark for Data Science - Fundamentals

Course 5: PySpark for Data Science - II: Statistics for Big Data

Course 6: PySpark for Data Science - III : Data Cleaning and Analysis Methods

Course 7: PySpark for Data Science - IV : Machine Learning

Course 8: Pyspark for Data Science - V :ML Pipelines

Part 4: Time Series Forecasting Expert

Course 1: Introduction to Time Series Analysis

Course 2: Time Series Analysis-I (Beginners)

Course 3: Time Series Analysis-II (Intermediate)

Course 4: Time Series Forecasting Part 1 - Statistical Models

Course 5: Time Series Forecasting Part 2 - ARIMA Modeling and Tests

Course 6: Time Series Forecasting Part 3 - Vector Auto Regression Methods

Course 7: Time Series Analysis - III: Singular Spectrum Analysis

Course 8: Feature Engineering for Time Series Projects - I

Course 9: Feature Engineering for Time Series Projects - II

Part 5: Industry Data Science Projects Expert

Course 1: Portfolio Project: Estimating Customer Lifetime Value

Course 2: Portfolio Project: Microsoft Malware Detection Project

Course 3: Portfolio Project: Credit Card Fraud Detection

Course 4: Portfolio Project: Restaurant Visitor Forecasting for Recruit

Course 5: Portfolio Project: Optimizing marketing spend using Market Mix Modeling

Course 6: Portfolio Project: Predict Rating given Amazon Product Reviews using NLP

Course 7: Portfolio Project: Uplift modeling: Estimating incremental impact of Marketing Campaigns

Course 8: Portfolio Project: Uplift modeling Part 2: Modelling Strategies

Course 9: Portfolio Project: Survival Analysis: Predicting Time to Event in real world applications

Course 10: Portfolio Project: Survival Analysis Part 2: Predicting Time to Event for Lungs Cancer Patients

Course 11: Attribution Models in Marketing

Course 12: Dynamic Pricing using Multi Armed Bandit (Reinforcement Learning)

Course 13: Reinforcement learning for Online Ad Serving with Multi Armed Bandits

Course 14: MLFlow in Action: Hands on guide to ML experiments

Part 5: Deep Learning Expert

Course 1: Introduction to Deep Learning

Course 2: Foundations of Deep Learning in Python

Course 3: Applied Deep Learning with PyTorch

Course 4: Detecting Defects in Steel Sheets with Computer Vision

Course 5: Text Generation using Language Models with LSTM

Course 6: Classifying Sentiment of Reviews using BERT (NLP)

Part 6: Supplimentary Courses

Course 1: Base R Programming

Course 2: Dplyr for Data Wrangling

Course 3: Wrangling data with Data Table

Course 4: GGPlot2 visualization for Data Analysis

Course 5: Statistical Foundation for ML in R

Course 6: Regression Modeling in R

Course 7: Spacy for NLP

Course 8: Caret Package in R