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AI: The Complete Real-World Machine Learning Solutions Course - Beginner To Advanced!
Section 1 Welcome
Lecture 1 Welcome (3:02)
Lecture 2 Preprocessing Data Using Different Techniques (6:20)
Lecture 3 Label Encoding (2:19)
Lecture 4 Building a Linear Regressor (4:19)
Lecture 5 Regression Accuracy and Model Persistence (3:29)
Lecture 6 Building a Ridge Regressor (2:29)
Lecture 7 Building a Polynomial Regressor (2:21)
Lecture 8 Estimating housing prices (3:34)
Lecture 9 Computing relative importance of features (1:43)
Lecture 10 Estimating bicycle demand distribution (4:21)
Section 2 Constructing a Classifier
Lecture 11 Building a Simple Classifier (3:39)
Lecture 12 Building a Logistic Regression Classifier (4:41)
Lecture 13 Building a Naive Bayes’ Classifier (2:04)
Lecture 14 Splitting the Dataset for Training and Testing (1:15)
Lecture 15 Evaluating the Accuracy Using Cross-Validation (4:00)
Lecture 16 Visualizing the Confusion Matrix and Extracting the Performance Report (4:05)
Lecture 17 Evaluating Cars based on Their Characteristics (5:03)
Lecture 18 Extracting Validation Curves (2:40)
Lecture 19 Extracting Learning Curves (1:30)
Lecture 20 Extracting the Income Bracket (3:27)
Section 3 Predictive Modeling
Lecture 21 Building a Linear Classifier Using Support Vector Machine (4:21)
Lecture 22 Building Nonlinear Classifier Using SVMs (1:41)
Lecture 23 Tackling Class Imbalance (2:43)
Lecture 24 Extracting Confidence Measurements (2:25)
Lecture 25 Finding Optimal Hyper-Parameters (2:05)
Lecture 26 Building an Event Predictor (3:32)
Lecture 27 Estimating Traffic (2:27)
Section 4 Clustering with Unsupervised Learning
Lecture 28 Clustering Data Using the k-means Algorithm (2:54)
Lecture 29 Compressing an Image Using Vector Quantization (3:29)
Lecture 30 Building a Mean Shift Clustering (2:29)
Lecture 31 Grouping Data Using Agglomerative Clustering (2:57)
Lecture 32 Evaluating the Performance of Clustering Algorithms (2:47)
Lecture 33 Automatically Estimating the Number of Clusters Using DBSCAN (3:26)
Lecture 34 Finding Patterns in Stock Market Data (2:26)
Lecture 35 Building a Customer Segmentation Model (2:07)
Section 5 Building Recommendation Engines
Lecture 36 Building Function Composition for Data Processing (3:14)
Lecture 37 Building Machine Learning Pipelines (3:46)
Lecture 38 Finding the Nearest Neighbors (1:48)
Lecture 39 Constructing a k-nearest Neighbors Classifier (4:11)
Lecture 40 Constructing a k-nearest Neighbors Regressor (2:36)
Lecture 41 Computing the Euclidean Distance Score (2:00)
Lecture 42 Computing the Pearson Correlation Score (1:48)
Lecture 43 Finding Similar Users in a Dataset (1:27)
Lecture 44 Generating Movie Recommendations (2:27)
Section 6 Analyzing Text Data
Lecture 45 Preprocessing Data Using Tokenization (2:48)
Lecture 46 Stemming Text Data (2:14)
Lecture 47 Converting Text to Its Base Form Using Lemmatization (2:02)
Lecture 48 Dividing Text Using Chunking (1:55)
Lecture 49 Building a Bag-of-Words Model (2:50)
Lecture 50 Building a Text Classifier (4:33)
Lecture 51 Identifying the Gender (2:08)
Lecture 52 Analyzing the Sentiment of a Sentence (3:02)
Lecture 53 Identifying Patterns in Text Using Topic Modelling (4:37)
Section 7 Speech Recognition
Lecture 54 Reading and Plotting Audio Data (2:22)
Lecture 55 Transforming Audio Signals into the Frequency Domain (2:02)
Lecture 56 Generating Audio Signals with Custom Parameters (1:37)
Lecture 57 Synthesizing Music (2:03)
Lecture 58 Extracting Frequency Domain Features (1:58)
Lecture 59 Building Hidden Markov Models (2:12)
Lecture 60 Building a Speech Recognizer (3:01)
Section 8 Dissecting Time Series and Sequential Data
Lecture 61 Transforming Data into the Time Series Format (2:53)
Lecture 62 Slicing Time Series Data (1:23)
Lecture 63 Operating on Time Series Data (1:34)
Lecture 64 Extracting Statistics from Time Series (2:21)
Lecture 65 Building Hidden Markov Models for Sequential Data (4:04)
Lecture 66 Building Conditional Random Fields for Sequential Text Data (4:19)
Lecture 67 Analyzing Stock Market Data with Hidden Markov Models (2:07)
Section 9 Image Content Analysis
Lecture 68 Operating on Images Using OpenCV-Python (2:55)
Lecture 69 Detecting Edges (2:36)
Lecture 70 Histogram Equalization (2:21)
Lecture 71 Detecting Corners and SIFT Feature Points (3:37)
Lecture 72 Building a Star Feature Detector (1:27)
Lecture 73 Creating Features Using Visual Codebook and Vector Quantization (4:03)
Lecture 74 Training an Image Classifier Using Extremely Random Forests (2:20)
Lecture 75 Building an object recognizer (1:39)
Section 10 Biometric Face Recognition
Lecture 76 Capturing and Processing Video from a Webcam (1:47)
Lecture 77 Building a Face Detector using Haar Cascades (2:33)
Lecture 78 Building Eye and Nose Detectors (1:48)
Lecture 79 Performing Principal Component Analysis (2:10)
Lecture 80 Performing Kernel Principal Component Analysis (1:55)
Lecture 81 Performing Blind Source Separation (2:03)
Lecture 82 Building a Face Recognizer Using a Local Binary Patterns Histogram (3:57)
Section 11 Deep Neural Networks
Lecture 83 Building a Perceptron (2:27)
Lecture 84 Building a Single-Layer Neural Network (1:30)
Lecture 85 Building a deep neural network (2:11)
Lecture 86 Creating a Vector Quantizer (1:33)
Lecture 87 Building a Recurrent Neural Network for Sequential Data Analysis (2:16)
Lecture 88 Visualizing the Characters in an Optical Character Recognition Database (1:39)
Lecture 89 Building an Optical Character Recognizer Using Neural Networks (2:14)
Section 12 Visualizing Data
Lecture 90 Plotting 3D Scatter plots (2:32)
Lecture 91 Plotting Bubble Plots (1:05)
Lecture 92 Animating Bubble Plots (1:41)
Lecture 93 Drawing Pie Charts (1:23)
Lecture 94 Plotting Date-Formatted Time Series Data (1:21)
Lecture 95 Plotting Histograms (0:53)
Lecture 96 Visualizing Heat Maps (1:04)
Lecture 97 Animating Dynamic Signals (1:56)
Working Files
Working Files
Lecture 74 Training an Image Classifier Using Extremely Random Forests
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