Overview

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Machine learning is one of the most sought-after skills in today’s data-driven world, and this course provides the perfect balance between theory and application. You’ll explore how Python can be leveraged to build, evaluate, and deploy machine learning models effectively across various domains. Through this course, you’ll gain hands-on experience with practical tools and techniques to improve your ability to design, train, and optimize predictive models. You’ll learn how to apply advanced methods in areas such as deep learning, computer vision, and natural language processing to achieve measurable, real-world outcomes. What sets this course apart is its focus on bridging theoretical foundations with practical, implementation-based exercises. You’ll work on real-world case studies using TensorFlow and PyTorch, ensuring that the skills you acquire are immediately applicable in professional settings. This course is ideal for data scientists, ML engineers, and Python developers aiming to strengthen their expertise in applied machine learning. A working knowledge of Python and basic data analysis concepts will help you get the most out of this course.

Syllabus

Getting Started with Machine Learning and Python

In this section, we explore foundational machine learning concepts, data preprocessing, and model combination techniques using Python, emphasizing practical applications and model accuracy.

Building a Movie Recommendation Engine with Naïve Bayes

In this section, we explore binary classification using Bayes to build a movie recommendation system, evaluate model performance, and apply cross-validation for refinement

Predicting Online Ad Click-Through with Tree-Based Algorithms

In this section, we explore tree-based algorithms for predicting ad click-through rates, focusing on decision trees, random forests, and gradient-boosted trees with practical implementations using scikit-learn and XGBoost.

Predicting Online Ad Click-Through with Logistic Regression

In this section, we cover logistic regression, including encoding, training, regularization, and TensorFlow implementation for ad click prediction.

Predicting Stock Prices with Regression Algorithms

In this section, we explore regression techniques for stock price prediction, focusing on feature engineering, linear regression, and model evaluation for data-driven financial decisions.

Predicting Stock Prices with Artificial Neural Networks

In this section, we cover building and optimizing neural networks for stock price prediction using activation functions, dropout, and early stopping.

Mining the 20 Newsgroups Dataset with Text Analysis Techniques

In this section, we explore text analysis techniques using NLP, focusing on preprocessing, visualizing newsgroups data with t-SNE, and applying unsupervised learning to unstructured data.

Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling

In this section, we explore clustering and topic modeling to uncover hidden structures in text data. Techniques like k-means and NMF/LDA reveal underlying themes and groupings for practical data analysis.

Recognizing Faces with Support Vector Machine

In this section, we explore SVM for face recognition, analyze hyperplane separation in high-dimensional data, and apply PCA to enhance image classification performance.

Machine Learning Best Practices

In this section, we explore 21 machine learning best practices, focusing on data preparation, model selection, and continuous monitoring to ensure effective real-world implementations.

Categorizing Images of Clothing with Convolutional Neural Networks

In this section, we explore CNNs for clothing image classification, focusing on building blocks, model design, and data augmentation techniques to enhance performance.

Making Predictions with Sequences Using Recurrent Neural Networks

In this section, we explore RNNs and LSTMs for sequence prediction, focusing on training models to handle time-dependent data and generate text with practical applications.

Advancing Language Understanding and Generation with the Transformer Models

In this section, we explore Transformer models, focusing on self-attention mechanisms and their application in NLP tasks like sentiment analysis and text generation using BERT and GPT.