Machine Learning: From Fundamentals to Industry Applications
Course Curriculum
Module 1: Build a Strong Foundation in Machine Learning Math
Learn the mathematical concepts that underlie machine learning, including linear algebra, calculus, and probability theory, and apply them to real-world problems
Derive Linear Regression from Scratch
Implement linear regression using only matrix operations and gradient descent
Implement Gradient Descent for Logistic Regression
Write a Python function to perform gradient descent for logistic regression
Visualize High-Dimensional Data with PCA
Use principal component analysis (PCA) to reduce the dimensionality of a dataset and visualize the results
Solve a Regression Problem with Regularization
Apply L1 and L2 regularization to a regression problem and compare the results
Deploy a Simple Machine Learning Model
Use a cloud platform to deploy a simple machine learning model and make predictions
📝 Recall - Module 1
Test your understanding of the mathematical foundations of machine learning
Module 2: Design and Implement Supervised Learning Algorithms
Learn to design and implement supervised learning algorithms, including decision trees, random forests, and support vector machines
Build a Decision Tree from Scratch
Implement a decision tree algorithm using only basic programming concepts
Train a Random Forest Model
Use a random forest algorithm to classify a dataset and tune the hyperparameters
Implement Support Vector Machines
Write a Python function to implement a support vector machine (SVM) algorithm
Compare the Performance of Supervised Learning Algorithms
Evaluate the performance of different supervised learning algorithms on a dataset
Optimize Hyperparameters with Grid Search
Use grid search to optimize the hyperparameters of a supervised learning algorithm
📝 Recall - Module 2
Test your understanding of supervised learning algorithms
Module 3: Develop Unsupervised Learning and Deep Learning Skills
Learn to apply unsupervised learning and deep learning techniques, including clustering, dimensionality reduction, and convolutional neural networks
Apply K-Means Clustering to a Dataset
Use k-means clustering to segment a dataset and visualize the results
Implement Autoencoders for Dimensionality Reduction
Write a Python function to implement an autoencoder for dimensionality reduction
Build a Convolutional Neural Network from Scratch
Implement a convolutional neural network (CNN) using only basic programming concepts
Train a Recurrent Neural Network
Use a recurrent neural network (RNN) to predict a time series dataset
Fine-Tune a Pre-Trained Deep Learning Model
Use transfer learning to fine-tune a pre-trained deep learning model on a new dataset
📝 Recall - Module 3
Test your understanding of unsupervised learning and deep learning techniques
Module 4: Work with Natural Language Processing and Computer Vision
Learn to apply machine learning techniques to natural language processing and computer vision tasks, including text classification, sentiment analysis, and object detection
Implement Text Classification with Naive Bayes
Use a naive Bayes algorithm to classify text data and evaluate the performance
Build a Sentiment Analysis Model
Use a machine learning algorithm to predict the sentiment of text data
Detect Objects with YOLO
Use the YOLO algorithm to detect objects in images and evaluate the performance
Apply Transfer Learning to Computer Vision Tasks
Use pre-trained models to perform computer vision tasks, including image classification and object detection
Develop a Chatbot with Natural Language Processing
Use natural language processing techniques to develop a chatbot that can respond to user input
📝 Recall - Module 4
Test your understanding of natural language processing and computer vision techniques
Module 5: Deploy and Maintain Machine Learning Models in Production
Learn to deploy and maintain machine learning models in production, including model serving, monitoring, and updating
Deploy a Machine Learning Model with TensorFlow Serving
Use TensorFlow Serving to deploy a machine learning model and make predictions
Monitor and Update a Machine Learning Model
Use metrics and logging to monitor the performance of a machine learning model and update it as needed
Implement Model Explainability Techniques
Use techniques, including feature importance and partial dependence plots, to explain the predictions of a machine learning model
Develop a Machine Learning Pipeline with scikit-learn
Use scikit-learn to develop a machine learning pipeline that includes data preprocessing, model training, and model evaluation
Optimize the Performance of a Machine Learning Model
Use techniques, including hyperparameter tuning and model pruning, to optimize the performance of a machine learning model
📝 Recall - Module 5
Test your understanding of deploying and maintaining machine learning models in production