Machine Learning is arguably the most transformative and in-demand skill in tech today. This 11+ hours, step-by-step course is your complete guide to truly mastering ML concepts.
You’ll gain practical, hands-on experience with Python’s core libraries: NumPy for numerical computing, Pandas for robust data manipulation, Matplotlib for powerful visualizations, and Scikit-learn to effectively build and evaluate various ML models.
📺 Course Timestamps & Outline:
00:00:00 Course Introduction
🎓 SECTION 1: Intro to Machine Learning
00:02:06 What is Machine Learning?
00:08:56 Why use Machine Learning?
00:14:25 AI vs. ML vs. DL
00:22:29 Types of Machine Learning Systems
00:23:29 Supervised Learning
00:25:44 Unsupervised Learning
00:27:45 Semi-Supervised Learning
00:29:52 Reinforcement Learning
00:32:26 Batch Learning
00:35:09 Online Learning
00:38:34 Instance-Based Learning
00:44:40 Model-Based Learning
00:47:56 Main Challenges of Machine Learning
🔢 SECTION 2: NumPy (Numerical Computing)
00:56:09 Introduction to NumPy
01:10:15 Creating NumPy Arrays
01:43:57 Indexing and Slicing Arrays
02:05:31 Element-wise Operations
02:19:06 Broadcasting
02:29:22 Advanced Array Manipulations
02:38:47 Copies and Views
02:44:17 Structured Arrays
02:50:50 Linear Algebra Operations
02:57:16 Random Number Generation
03:03:21 Practice Questions
📊 SECTION 3: Pandas (Data Manipulation & Analysis)
03:18:01 Introduction to Pandas
03:29:20 Creating Series
03:36:20 Indexing and Slicing in Series
03:41:28 Handling Missing Data in Series
03:48:01 Series Attributes and Methods
04:06:17 Creating DataFrames
04:16:34 Indexing and Slicing in DataFrames
04:27:02 Handling Missing Data in DataFrames
04:30:55 Sorting, Filtering, and Merging Data
04:51:55 Data Cleaning Techniques
05:02:12 Data Transformation
05:13:14 Group Operations
05:24:56 Time Series Analysis
05:29:48 Performance Optimization
📈 SECTION 4: Matplotlib (Data Visualization)
05:38:46 Introduction to Matplotlib
05:41:10 Scatter Plot
05:48:01 Line Plot
05:53:07 Bar Plot
05:57:43 Histogram
06:05:11 Pie Chart
06:11:53 Titles and Labels
06:16:27 Ticks and Tick Labels
06:20:14 Colors and Markers
06:23:22 Figure and Axes
06:29:59 Subplots
06:37:29 Legend
06:43:44 Text, Annotation, and Lines
06:57:37 Exporting Plots
🤖 SECTION 5: Scikit-learn (Building ML Models)
07:02:58 Essential ML Terminology
07:16:23 Machine Learning Workflow in Practice
07:32:25 Introduction to Scikit-learn
07:37:04 Scikit-learn API Conventions
07:43:03 SimpleImputer (Handling Missing Data)
08:02:00 Encoding Categorical Data
08:08:47 OrdinalEncoder
08:19:31 OneHotEncoder
08:31:22 Feature Scaling
08:37:14 Standardization
08:45:56 Normalization
08:49:50 Model Selection Basics
08:56:16 Linear Regression
09:22:41 Logistic Regression
09:51:42 K-Nearest Neighbors (KNeighborsClassifier & KNeighborsRegressor)
10:03:09 Support Vector Machines (SVC & SVR)
10:49:10 K-Means Clustering
11:11:57 Understanding Overfitting and Underfitting
11:21:41 Pipelines (Streamlining Workflow)
Resources:
👉 Github: https://github.com/bytequation/ml-notebook
💬 Got questions? Drop them in the comments below, and I’ll be happy to help!
#machinelearning #python #datascience #numpy #pandas #algorithm #ml #ai #coding #tutorial
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