20 Sections
20 Lessons
10 Weeks
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Introduction to Python for Data Science
1
1.1
Python attracts learners interested in Python-based ML.
105 Minutes
Basic Python for Data Science
1
2.1
Python Control Statements, Loops, and Functions and Complete Guide for Beginners with Examples
99 Minutes
Stats
1
3.1
Statistics and Probability for Machine Learning – Complete Beginner’s Guide with Examples
91 Minutes
Probability & Probability Distribution Function
1
4.1
Empirical Formula, Percentiles, and Probability Distributions in Statistics for Data Science & Machine Learning
102 Minutes
Normal Distribution, z score & confidence interval
1
5.1
Data Analysis, Event Modeling & Machine Learning Applications Using Statistics and Software Tools
101 Minutes
Hypothesis Testing z test
1
6.1
Hypothesis Testing and Z-test and More
97 Minutes
Hypothesis Testing t test, chi square test & ANNOVA
1
7.1
Hypothesis testing, Statistical tests like chi-square test (G test) and Z-test
104 Minutes
Central Limit Theorem
1
8.1
Learn Data Science Fundamentals: CLT, Hypothesis Testing, RFM Analysis, and Correlation in ML
37 Minutes
Pandas for Data Science
1
9.1
Pandas for Beginners: DataFrames, Indexing, GroupBy & Real-World Analysis
76 Minutes
Data Visualization for Data Science
1
10.1
Data Analysis and Pattern Detection Explained with Real-Life Examples, Tech Tools, and Cultural Insights
88 Minutes
Machine Learning Workflow
1
11.1
Machine Learning: Concepts, Applications, and Real-World Use Cases and More
77 Minutes
Feature Engineering Part I
1
12.1
Data Analysis and Machine Learning for Beginners: Techniques, Tips & Real-Life Applications and More
84 Minutes
Feature Engineering Part II
1
13.1
Machine Learning Preprocessing: Scaling, Normalization, and Practical Knowledge and More
60 Minutes
Linear Regression Mathematical Intuition
1
14.1
Machine Learning Explained: Classification, Modeling & Real-World Applications and More
62 Minutes
Linear Regression Implementation
1
15.1
Machine Learning Models & Real-World Impact: Education, Healthcare & Beyond
73 Minutes
Logistic Regression Loss Function Part 1
1
16.1
Logistic Regression & Classification: Real-Life Machine Learning Applications Explained
38 Minutes
Logistic Regression Loss Function 2
1
17.1
Retail Data Analysis with Logistic Regression: Model Building & Performance Insights
19 Minutes
KNN Classifier
1
18.1
Understanding KNN Algorithm: Euclidean Distance, Cross-Validation, and Imbalanced Data Handling
62 Minutes
Performance Metrics
1
19.1
Model Evaluation with Confusion Matrix & Accuracy: A Guide to Logistic Regression Performance
52 Minutes
SVM
1
20.1
Machine Learning Algorithms Guide: SVM, Logistic Regression, Model Tuning & Optimization
65 Minutes
Data Science
Curriculum
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