Portfolio Project: Microsoft Malware Detection Project

What you'll be able to do

• Frame Data Science Problem Statement
• Extensive Exploratory Data Analysis
• Feature Engineering Strategies
• Statistical Concepts and Implementation
• Model Evaluation and Improvement
• Advanced Machine Learning Algorithms
• Domain based data science strategies
• Derive high business impact insights

1. 01Overview4 lessons · 11 min

   1. Course Overview3:35
   2. How to Get Queries Resolved1:00
   3. Download Resources1:00
   4. Problem DescriptionPreview5:01
2. 02Setup new environment2 lessons · 14 min

   1. Python Setup - Local Installation9:47
   2. Python Setup - Google Colab4:33
3. 03Understanding the data4 lessons · 32 min

   1. Data OverviewPreview11:52
   2. Optimize Memory UsagePreview11:06
   3. Understand The DataPreview8:22
   4. Course Review1:00
4. 04Preprocess the data1 lesson · 7 min

   1. Data Preprocessing for EDA7:19
5. 05Perform EDA ( Exploratory Data Analysis)4 lessons · 33 min

   1. Need for EDA4:47
   2. Exploratory Data Analysis Part 114:27
   3. Exploratory Data Analysis Part 213:15
   4. Course Review1:00
6. 06Statistical Significance Tests5 lessons · 56 min

   1. Chi Squared Test Theory and Maths17:47
   2. Chi Square Test and Odds Ratio Demo9:10
   3. ANOVA Concept- Intuition10:07
   4. ANOVA maths12:40
   5. ANOVA Demo6:31
7. 07Engineer new features and process data5 lessons · 30 min

   1. Feature Engineering - Domain Specific7:53
   2. Feature Encoding Approaches8:11
   3. Feature Encoding Demo7:40
   4. Data Processing for Model Building5:28
   5. Course Review1:00
8. 08Evaluation methods for classification6 lessons · 45 min

   1. Confusion Matrix and Evaluation Metrics10:06
   2. Concordance and Discordance9:18
   3. ROC Curve9:22
   4. Precision Recall Curve2:54
   5. Evaluation Metrics Demo5:13
   6. Capture Rates and Gains7:55
9. 09Machine Learning Models5 lessons · 42 min

   1. Decision Trees and Improvements17:12
   2. Random Forests8:52
   3. XGBoost7:26
   4. LightGBM7:39
   5. Course Review1:00
10. 10Model improvement and interpretation3 lessons · 20 min

    1. Tuning Hyperparameters7:21
    2. Feature Importance11:06
    3. Feature Importance Demo1:46
11. 11Summary2 lessons · 2 min

    1. Final Words1:28
    2. Course Review1:00

What you need

• Basics of Python
• Foundational knowledge of Data Science
• High school maths

Who this course is for

• Professionals in the field of data science
• Professionals looking for a robust, structured Python learning program
• Software or data engineers interested in quantitative analysis
• Professionals working with large datasets
• Data analysts, economists, researchers

Your instructor

Prabhakaran S

Principal Data Scientist

My name is Selva, and I am super excited to mentor you on this course!

I head the Data Science team for a global Fortune 500 company and over the last 10 years of my data science experience I've deployed 20+ global products. I'm also the Founder and Chief Author of Machine Learning Plus, which has over 4M annual readers.

I specialize in covering the in-depth intuition and maths of any concept or algorithm. And based on my existing student requests, I've put up the series of courses and projects with detailed explanations, just like on-the-job experience. Hope you love it!

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