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:

  • Delve into a wide array of classification algorithms, exploring their potential in predicting income brackets.
  • Harness predictive modeling to offer tangible solutions to real-world challenges.
  • Uncover the mysteries of market segmentation with unsupervised learning.
  • Engage with rich data visualization methodologies, bringing your data narratives to life.
  • Build sophisticated recommendation systems that can match industry giants.
  • Analyze and model text, speech, and image data with finesse and precision.
  • Dive deep into the world of neural networks, laying the foundation for advanced AI systems.

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!

Course curriculum

    1. Lecture 1 Welcome

    2. Lecture 2 Preprocessing Data Using Different Techniques

    3. Lecture 3 Label Encoding

    4. Lecture 4 Building a Linear Regressor

    5. Lecture 5 Regression Accuracy and Model Persistence

    6. Lecture 6 Building a Ridge Regressor

    7. Lecture 7 Building a Polynomial Regressor

    8. Lecture 8 Estimating housing prices

    9. Lecture 9 Computing relative importance of features

    10. Lecture 10 Estimating bicycle demand distribution

    1. Lecture 11 Building a Simple Classifier

    2. Lecture 12 Building a Logistic Regression Classifier

    3. Lecture 13 Building a Naive Bayes’ Classifier

    4. Lecture 14 Splitting the Dataset for Training and Testing

    5. Lecture 15 Evaluating the Accuracy Using Cross-Validation

    6. Lecture 16 Visualizing the Confusion Matrix and Extracting the Performance Report

    7. Lecture 17 Evaluating Cars based on Their Characteristics

    8. Lecture 18 Extracting Validation Curves

    9. Lecture 19 Extracting Learning Curves

    10. Lecture 20 Extracting the Income Bracket

    1. Lecture 21 Building a Linear Classifier Using Support Vector Machine

    2. Lecture 22 Building Nonlinear Classifier Using SVMs

    3. Lecture 23 Tackling Class Imbalance

    4. Lecture 24 Extracting Confidence Measurements

    5. Lecture 25 Finding Optimal Hyper-Parameters

    6. Lecture 26 Building an Event Predictor

    7. Lecture 27 Estimating Traffic

    1. Lecture 28 Clustering Data Using the k-means Algorithm

    2. Lecture 29 Compressing an Image Using Vector Quantization

    3. Lecture 30 Building a Mean Shift Clustering

    4. Lecture 31 Grouping Data Using Agglomerative Clustering

    5. Lecture 32 Evaluating the Performance of Clustering Algorithms

    6. Lecture 33 Automatically Estimating the Number of Clusters Using DBSCAN

    7. Lecture 34 Finding Patterns in Stock Market Data

    8. Lecture 35 Building a Customer Segmentation Model

    1. Lecture 36 Building Function Composition for Data Processing

    2. Lecture 37 Building Machine Learning Pipelines

    3. Lecture 38 Finding the Nearest Neighbors

    4. Lecture 39 Constructing a k-nearest Neighbors Classifier

    5. Lecture 40 Constructing a k-nearest Neighbors Regressor

    6. Lecture 41 Computing the Euclidean Distance Score

    7. Lecture 42 Computing the Pearson Correlation Score

    8. Lecture 43 Finding Similar Users in a Dataset

    9. Lecture 44 Generating Movie Recommendations

    1. Lecture 45 Preprocessing Data Using Tokenization

    2. Lecture 46 Stemming Text Data

    3. Lecture 47 Converting Text to Its Base Form Using Lemmatization

    4. Lecture 48 Dividing Text Using Chunking

    5. Lecture 49 Building a Bag-of-Words Model

    6. Lecture 50 Building a Text Classifier

    7. Lecture 51 Identifying the Gender

    8. Lecture 52 Analyzing the Sentiment of a Sentence

    9. Lecture 53 Identifying Patterns in Text Using Topic Modelling

About this course

  • $19.99
  • 98 lessons
  • 4.5 hours of video content

Discover your potential, starting today


  • What prior knowledge is required for this course?

    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.

  • Are there hands-on projects?

    Yes, each section has real-world projects, exercises, and practical implementations to ensure you grasp and apply the concepts effectively.

  • Can I access the course material after completion?

    Of course! Once enrolled, you have lifetime access to the course material, including any future updates.

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