Machine Learning Foundations: From Theory to Practice

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About Course

Course Description: This 5-section video lecture series provides a comprehensive introduction to Machine Learning (ML). Designed for beginners and intermediate learners, this course covers the end-to-end ML pipeline, from understanding core concepts and preprocessing data to training models and deploying them responsibly.

Target Audience:

  • Developers looking to transition into Data Science.

  • Students needing a practical refresher on ML concepts.

  • Technical Managers wanting to understand ML workflows.

Course Content

The Machine Learning Landscape
1.1 Learning Objectives By the end of this section, learners will be able to: Define Machine Learning and distinguish it from traditional programming. Differentiate between Supervised, Unsupervised, and Reinforcement Learning. Identify real-world applications of ML (e.g., recommendation engines, fraud detection). 1.2 Video Lecture Outline (Approx. 10-15 mins) Introduction (0:00 - 2:00): Hook: "How does Netflix know what you want to watch next?" Definition: ML is the science of getting computers to act without being explicitly programmed. Traditional Programming vs. ML (2:00 - 5:00): Visual comparison: Input + Rules = Output (Traditional) vs. Input + Output = Rules (ML). Types of Machine Learning (5:00 - 10:00): Supervised: Labeled data (Teacher student analogy). Unsupervised: Unlabeled data (Finding hidden patterns). Reinforcement: Reward/Penalty system (Training a dog/robot). The ML Workflow (10:00 - End): Brief overview of the 7 steps: Gathering data -> Preparation -> Model -> Training -> Evaluation -> Tuning -> Prediction. 1.3 LMS Assessment: Quiz Multiple Choice: Which type of machine learning involves training on labeled data? a) Unsupervised Learning b) Reinforcement Learning c) Supervised Learning (Correct) d) Clustering True/False: In traditional programming, the computer figures out the rules based on the data. (Answer: False)

  • Machine Learning
    00:08

Data Preprocessing & Exploration (EDA)
2.1 Learning Objectives By the end of this section, learners will be able to: Explain why data preprocessing is the most critical step in ML. Perform basic cleaning tasks: handling missing values and encoding categorical variables. Understand the importance of Feature Scaling. 2.2 Video Lecture Outline (Approx. 15-20 mins) The "Garbage In, Garbage Out" Principle (0:00 - 3:00): Why model accuracy depends 80% on data quality. Exploratory Data Analysis (EDA) (3:00 - 8:00): Using visualizations (Histograms, Scatter plots) to understand data distribution. Identifying outliers. Handling Missing Data (8:00 - 12:00): Dropping rows vs. Imputation (filling with mean/median). Data Transformation (12:00 - End): Categorical Encoding: One-Hot Encoding vs. Label Encoding (turning text into numbers). Feature Scaling: Standardization vs. Normalization (why different scales confuse algorithms like KNN). 2.3 LMS Assessment: Practical Exercise Scenario: You are given a dataset of housing prices, but the "GarageArea" column has 50 missing values and "Neighborhood" is text. Task: Submit a short paragraph describing how you would handle these two columns before feeding them into a model. 2.4 Additional Resources [Cheatsheet] Pandas Data Cleaning Cheatsheet. [Tool] Link to a Google Colab notebook with dirty data for practice.

Video Lecture Outline (Approx. 20-25 mins)
Regression vs. Classification (0:00 - 3:00): Regression: Predicting house price (continuous). Classification: Is this email spam? (Yes/No). Algorithm Deep Dive 1: Linear Regression (3:00 - 8:00): Drawing the "Line of Best Fit." Concept of residuals/error. Algorithm Deep Dive 2: Decision Trees (8:00 - 13:00): Visualizing a tree structure (If-Else logic). Pros: Easy to interpret. Cons: Prone to overfitting. Model Evaluation (13:00 - End): Split: Training set vs. Testing set (70/30 or 80/20). Metrics: Accuracy is not enough! Introduction to the Confusion Matrix (Precision/Recall). 3.3 LMS Assessment: Quiz Multiple Choice: You want to predict the exact temperature for tomorrow. Is this a classification or regression problem? a) Classification b) Regression (Correct) c) Clustering Matching: Match the metric to the task. RMSE -> Regression Accuracy -> Classification

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