Mastering AI: Comprehensive Machine Learning Solutions From Scratch to Expert
Unlock the Power of AI: Dive Deep into Machine Learning Across Various Domains & Master Real-World Applications.
Delve into the dynamic realm of machine learning, a cornerstone of the modern AI-driven universe. As industries worldwide harness its unmatched potential—from robotics to finance, and self-driving cars to healthcare—this course empowers you to stay ahead of the curve.
Embark on a transformative journey as you:
With Python as your tool of choice, this course is brimming with real-life problem-solving exercises, utilizing an expansive range of machine learning algorithms. Whether you're a beginner seeking foundational knowledge or a professional aiming to upgrade your skill set, every module has been meticulously crafted to cater to your learning needs. Let's embark on this journey of AI mastery together!
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
This course is designed to accommodate both beginners and experts. While prior knowledge of Python and basic ML concepts is beneficial, it's not mandatory. We provide foundational lessons to ensure everyone can follow along.
Yes, each section has real-world projects, exercises, and practical implementations to ensure you grasp and apply the concepts effectively.
Of course! Once enrolled, you have lifetime access to the course material, including any future updates.