- 7 Sections
- 50 Lessons
- Lifetime
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- 1. Introduction to the Profession6
- 1.1B837 1.1 What Does a Machine Learning Engineer or Data Scientist Do?
- 1.2B837 1.2 Common Real-World ML Use Cases in Business
- 1.3B837 1.3 Career Paths and Where ML Specialists Work
- 1.4B837 1.4 A Day in the Life of an ML Engineer
- 1.5B837 1.5 How to Stay Motivated and Keep Learning
- 1.6B837 1. Quiz3 Questions
- 2. Python Fundamentals9
- 2.1B837 2.1 What You’ll Know by the End of This Module
- 2.2B837 2.2 Basic Data Types: Integers, Strings, Booleans
- 2.3B837 2.3 Control Flow: if, for, while
- 2.4B837 2.4 Collections: Lists, Dictionaries, Sets
- 2.5B837 2.5 Functions – the Basics
- 2.6B837 2.6 Decorators, Lambdas, and Generators
- 2.7B837 2.7 Reading and Writing Files
- 2.8B837 2.8 Introduction to Git and Shell (Command Line)
- 2.9B837 2. Quiz3 Questions
- 3. Math & Statistics Essentials12
- 3.1B837 3.1 Matrices – Concepts and Operations
- 3.2B837 3.2 Basics of Linear Algebra: Bases and Transformations
- 3.3B837 3.3 Matrix Decompositions
- 3.4B837 3.4 Differentiation and Function Optimization
- 3.5B837 3.5 Least Squares Method
- 3.6B837 3.6 Fundamentals of Probability Theory
- 3.7B837 3.7 Law of Large Numbers & Central Limit Theorem
- 3.8B837 3.8 Descriptive Statistics: Mean, Median, Variance
- 3.9B837 3.9 Hypothesis Testing and A/B Tests
- 3.10B837 3.10 Populations, Samples, and Confidence Intervals
- 3.11B837 3.11 Quantitative and Qualitative Data Types
- 3.12B837 3. Quiz3 Questions
- 4. Working with Data11
- 4.1B837 4.1 Introduction to NumPy
- 4.2B837 4.2 Arrays, Indexing, and Slicing
- 4.3B837 4.3 Intro to Pandas and DataFrames
- 4.4B837 4.4 Grouping, Filtering, Aggregating Data
- 4.5B837 4.5 Preparing a Dataset for Modeling
- 4.6B837 4.6 What Is a Database?
- 4.7B837 4.7 SQL Basics: Queries, Views, Filters
- 4.8B837 4.8 Joins, Nested Queries, Window Functions
- 4.9B837 4.9 Visualizing Data: matplotlib, seaborn, plotly
- 4.10B837 4.10 How to Interpret Charts and Graphs
- 4.11B837 4. Quiz3 Questions
- 5. Core Concepts in Machine Learning12
- 5.1B837 5.1 What Is Machine Learning?
- 5.2B837 5.2 Regression vs Classification
- 5.3B837 5.3 Linear Regression
- 5.4B837 5.4 K-Nearest Neighbors (KNN)
- 5.5B837 5.5 Logistic Regression
- 5.6B837 5.6 Decision Trees
- 5.7B837 5.7 Model Ensembles (Bagging, Boosting)
- 5.8B837 5.8 Evaluation Metrics: Accuracy, Precision, Recall, F1
- 5.9B837 5.9 Common Pitfalls: Overfitting and Underfitting
- 5.10B837 5.10 Feature Engineering: The Art of Crafting Input
- 5.11B837 5.11 Preprocessing: Cleaning, Encoding, Scaling
- 5.12B837 5. Quiz3 Questions
- 6. Becoming a Real-World ML Specialist6
- B837 FinalQuiz1