Markovdata
Enterprise Machine Learning Track
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Course sections

Section 1
1- Python Programming and Statistics (30hrs)
Section 2
Module 1: Python Setup
1
Introduction
2
Getting Started with Python
3
Python Environment
Section 3
Module 2: Python Fundamentals
1
Python Basics
2
Python Data Structures
3
Python Operators
Section 4
Module 3: Python Advanced
1
Python Work Flow
2
Python Packages and Modules
3
Python Functions
Section 5
Module 4: Statistics and Probability
1
Descriptive Statistics
2
Probability
3
Numerical Scaling
Section 6
Module 5: Class Projects
1
Project 1: Explore USA Bike Share Data
2
Project 2: Explore Diamond Pricing Data
3
Project 3: Explore Movies Data
Section 7
2- Machine Learning and Python (35hrs)
Section 8
Module 1: Introduction to AI and ML
1
Intro to Artificial Intelligence
2
Intro to Machine Learning and Deep Learning
3
Data Science vs Data Analysis
4
Different Data Roles
5
Analytic Approaches
6
AI Systems Pipeline
7
Applications of (Artificial Intelligence-Machine Learning-Deep Learning)
8
AI New Technologies
9
Practical AI Use Cases
Section 9
Module 2: Data Wrangling and Pre-processing
1
Data Mining
2
Data Wrangling
3
Data Loading
4
Data Preparation
5
Statistical Analysis
6
Exploratory Data Analysis
7
Story Telling and Dashboards
8
Data Transformation
Section 10
Module 3: Machine Learning
1
Machine Learning Definition
2
Types of Machine Learning
3
Supervised and Unsupervised Learning
4
Supervised Learning Techniques
5
Regression for Machine Learning
6
Cost Function
7
Optimization Function
8
Evaluating Regression Models Performance
9
Overfitting and Underfitting
10
Bias and Variance
11
Regularization
12
Classification for Machine Learning
13
Evaluating Classification Models Performance
14
Unsupervised Learning Techniques
15
Clustering for Machine Learning
16
Evaluation Clustering Models Performance
Section 11
Module 4: Class Projects
1
Project 4: Automotive Price Prediction
2
Project 5: Fraud Detection
3
Project 6: Mall Customer Segmentation
Section 12
3- Enterprise Machine Learning Techniques (35hrs)
Section 13
Module 1: Enterprise Machine Learning Regression
1
Advanced Regression for Machine Learning
2
Regression Algorithms Evaluation Techniques
3
Regularization Parameter
4
Tuning Regression Hyper Parameters
Section 14
Module 2: Enterprise Machine Learning Classification
1
Advanced Classification for Machine Learning
2
Classification Algorithms Evaluation Techniques
3
Imbalanced Classification
4
Over Sampling and Under Sampling
5
Evaluating Imbalanced Classification Algorithms
Section 15
Module 3: Enterprise Unsupervised Machine Learning
1
Frequent Item Sets
2
Association Rules & Market Basket Analysis
3
Apriori Algorithm
4
Advanced Clustering for Machine Learning
5
Clustering Algorithms Evaluation Techniques
6
Dimensionality Reduction
7
Principle Component Analysis
Section 16
Module 4: Machine Learning Implementation Techniques
1
Ensemble Techniques in Machine Learning
2
Creating Machine Learning Development Pipeline
3
Automatic Hyper Parameters Tuning
4
Cross Validation
5
Grid Search
6
Model Structure MLFlow
7
Model Development AutoML
8
Model Deployment MLOps
Section 17
Module 5: Class Projects
1
Project 7: Predict Price of Airline Tickets
2
Project 8: Predict Price of King County Houses
3
Project 9: Titanic Survival Status Prediction
4
Project 10: Charity Donors Prediction
5
Project 11: Market Basket Analysis
6
Project 12: Clustering Cities Neighborhoods
Section 18
4- Final Project

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