Markovdata > Tracks > Enterprise Machine Learning Track

Enterprise Machine Learning Track

Abstract The ultimate guide to career transformation—go from novice to professional! In this class, you will learn the different techniques of machine learning and how to apply the data science life cycle on data sets. You will also get the practical knowledge of implementing the most effective methods by yourself. Moreover, you will get both […]

Instructor
Markov Data
12 students enrolled

Abstract

The ultimate guide to career transformation—go from novice to professional! In this class, you will learn the different techniques of machine learning and how to apply the data science life cycle on data sets. You will also get the practical knowledge of implementing the most effective methods by yourself. Moreover, you will get both the theoretical underpinnings of learning along with the practical know-how needed to strongly apply these methodologies and techniques to new problems.

Instructor

Prof. Hazem Shatila, Virginia Tech University, USA
Eng. Ahmed Yehia, Markov
Eng. Amr Helal, Data Scientist, Markov

Duration

5 Months

Sessions

Sundays & Wednesdays, 7:00 to 9:30PM

Location

Live Streaming (on-line)

Prerequisites

None

1- Python Programming and Statistics (30hrs)

Module 1: Python Setup

1
Introduction
  • Introduction to programming
  • Different programming languages
  • Why python?
  • Python applications
2
Getting Started with Python
  • Python setup
  • Python IDEs
  • Python environment manager
  • Anaconda distribution
3
Python Environment
  • Modules and packages
  • Virtual environments
  • Package installation

Module 2: Python Fundamentals

1
Python Basics
  • General Syntax
  • Python objects
  • Data Types
2
Python Data Structures
  • List
  • Tuple
  • Set
  • Dictionary
3
Python Operators
  • Mathematical operators
  • Comparison operators
  • Logical operators

Module 3: Python Advanced

1
Python Work Flow
  • Functions and methods
  • Conditional statements
  • Loops
  • Python flow control
  • Exception handling
  • Loading python files
2
Python Packages and Modules
  • NumPy
  • Pandas
  • Matplotlib
  • Seaborn
  • Datetime
3
Python Functions
  • User-defined functions
  • Nested functions
  • Python decorators
  • Python generators
  • Classes and objects

Module 4: Statistics and Probability

1
Descriptive Statistics
  • Introduction
  • Sampling Techniques
  • Data Types
  • Quantitative and Categorical Data
  • Measure of Central Tendency
  • Measure of Spread
  • Measure of Distribution
  • Skewness and Outliers
2
Probability
  • Introduction to Probability
  • Probability Laws
  • Probability Distribution
  • Gaussian Distribution
  • Sampling Distribution
  • Central Limit Theorem
3
Numerical Scaling
  • Normalization
  • Standardization
  • Log Function

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

2- Machine Learning and Python (35hrs)

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

Module 2: Data Wrangling and Pre-processing

1
Data Mining
2
Data Wrangling
  • Data Collection
  • Data Assessment
  • Data Cleansing
3
Data Loading
4
Data Preparation
5
Statistical Analysis
  • Data Correlation
  • Data Distribution
  • Data Outliers
6
Exploratory Data Analysis
  • Histogram
  • Box Plot
  • Scatter Plot
  • Count Plot
  • Bar Chart
  • Univariate Exploration
  • Bivariate Exploration
7
Story Telling and Dashboards
  • Explanatory Data Analysis
  • Data and Business Insights
  • Creating Dashboards
8
Data Transformation
  • Numerical Data Scaling
  • Categorical Data Transformation
  • Training, Testing, and Validation Data

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
  • Linear Regression
  • Linear Regression with Multiple Variables
  • Polynomial Regression
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
  • Logistic Regression
  • Decision Tree Classifier
  • K-Nearest Neighbor
13
Evaluating Classification Models Performance
14
Unsupervised Learning Techniques
15
Clustering for Machine Learning
  • K-mean Clustering
  • Hierarchical Clustering
16
Evaluation Clustering Models Performance

Module 4: Class Projects

1
Project 4: Automotive Price Prediction
2
Project 5: Fraud Detection
3
Project 6: Mall Customer Segmentation

3- Enterprise Machine Learning Techniques (35hrs)

Module 1: Enterprise Machine Learning Regression

1
Advanced Regression for Machine Learning
  • Ridge Regression
  • Lasso Regression
  • Decision Tree Regression
  • Random Forest Regression
2
Regression Algorithms Evaluation Techniques
3
Regularization Parameter
4
Tuning Regression Hyper Parameters

Module 2: Enterprise Machine Learning Classification

1
Advanced Classification for Machine Learning
  • Random Forest Classifier
  • Naïve Bayes
  • Support Vector Machines
2
Classification Algorithms Evaluation Techniques
3
Imbalanced Classification
4
Over Sampling and Under Sampling
  • SMOTE
  • Near Miss
5
Evaluating Imbalanced Classification Algorithms
  • Recall
  • Precision
  • F1-Score

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
  • Agglomerative Clustering
  • Divisive Clustering
  • DBSCAN Clustering
5
Clustering Algorithms Evaluation Techniques
6
Dimensionality Reduction
7
Principle Component Analysis

Module 4: Machine Learning Implementation Techniques

1
Ensemble Techniques in Machine Learning
  • Voting
  • Bagging
  • Boosting
  • XGBoost
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

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

4- Final Project

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