Explore AI

Lecture 1 Welcome

Lecture 2 Preprocessing Data Using Different Techniques

Lecture 3 Label Encoding

Lecture 4 Building a Linear Regressor

Lecture 5 Regression Accuracy and Model Persistence

Lecture 6 Building a Ridge Regressor

Lecture 7 Building a Polynomial Regressor

Lecture 8 Estimating housing prices

Lecture 9 Computing relative importance of features

Lecture 10 Estimating bicycle demand distribution

Lecture 11 Building a Simple Classifier

Lecture 12 Building a Logistic Regression Classifier

Lecture 13 Building a Naive Bayes’ Classifier

Lecture 14 Splitting the Dataset for Training and Testing

Lecture 15 Evaluating the Accuracy Using Cross-Validation

Lecture 16 Visualizing the Confusion Matrix and Extracting the Performance Report

Lecture 17 Evaluating Cars based on Their Characteristics

Lecture 18 Extracting Validation Curves

Lecture 19 Extracting Learning Curves

Lecture 20 Extracting the Income Bracket

Lecture 21 Building a Linear Classifier Using Support Vector Machine

Lecture 22 Building Nonlinear Classifier Using SVMs

Lecture 23 Tackling Class Imbalance

Lecture 24 Extracting Confidence Measurements

Lecture 25 Finding Optimal Hyper-Parameters

Lecture 26 Building an Event Predictor

Lecture 27 Estimating Traffic

Lecture 28 Clustering Data Using the k-means Algorithm

Lecture 29 Compressing an Image Using Vector Quantization

Lecture 30 Building a Mean Shift Clustering

Lecture 31 Grouping Data Using Agglomerative Clustering

Lecture 32 Evaluating the Performance of Clustering Algorithms

Lecture 33 Automatically Estimating the Number of Clusters Using DBSCAN

Lecture 34 Finding Patterns in Stock Market Data

Lecture 35 Building a Customer Segmentation Model

Lecture 36 Building Function Composition for Data Processing

Lecture 37 Building Machine Learning Pipelines

Lecture 38 Finding the Nearest Neighbors

Lecture 39 Constructing a k-nearest Neighbors Classifier

Lecture 40 Constructing a k-nearest Neighbors Regressor

Lecture 41 Computing the Euclidean Distance Score

Lecture 42 Computing the Pearson Correlation Score

Lecture 43 Finding Similar Users in a Dataset

Lecture 44 Generating Movie Recommendations

Lecture 45 Preprocessing Data Using Tokenization

Lecture 46 Stemming Text Data

Lecture 47 Converting Text to Its Base Form Using Lemmatization

Lecture 48 Dividing Text Using Chunking

Lecture 49 Building a Bag-of-Words Model

Lecture 50 Building a Text Classifier

Lecture 51 Identifying the Gender

Lecture 52 Analyzing the Sentiment of a Sentence

Lecture 53 Identifying Patterns in Text Using Topic Modelling