Python Machine Learning Tracks road map for computer science students, which including the following main topics:
Machine Learning Tracks
Course | Chapter | H | Videos | Exercises |
---|---|---|---|---|
AI Fundamentals | Introduction to AI | 4 | 14 | 49 |
Supervised Learning | ||||
Unsupervised Learning | ||||
Deep Learning & Beyond | ||||
Software Engineering for Data Scientists in Python | Software Engineering & Data Science | 4 | 15 | 51 |
Writing a Python Module | ||||
Utilizing Classes | ||||
Maintainability | ||||
Preprocessing for Machine Learning in Python | Introduction to Data Preprocessing | 4 | 20 | 62 |
Standardizing Data | ||||
Feature Engineering | ||||
Selecting features for modeling | ||||
Putting it all together | ||||
Linear Classifiers in Python | Applying logistic regression and SVM | 4 | 13 | 44 |
Loss functions | ||||
Logistic regression | ||||
Support Vector Machines | ||||
Unsupervised Learning in Python | Clustering for dataset exploration | 4 | 13 | 52 |
Visualization with hierarchical clustering and t-SNE | ||||
Decorrelating your data and dimension reduction | ||||
Discovering interpretable features | ||||
Supervised Learning with scikit-learn | Classification | 4 | 17 | 54 |
Regression | ||||
Fine-tuning your model | ||||
Preprocessing and pipelines | ||||
Machine Learning with Tree-Based Models in Python | Classification and Regression Trees | 5 | 15 | 57 |
The Bias-Variance Tradeoff | ||||
Bagging and Random Forests | ||||
Boosting | ||||
Model Tuning | ||||
Introduction to Predictive Analytics in Python | Building Logistic Regression Models | 4 | 14 | 52 |
Forward stepwise variable selection for logistic regression | ||||
Explaining model performance to business | ||||
Interpreting and explaining models | ||||
Dimensionality Reduction in Python | Exploring high dimensional data | 4 | 16 | 58 |
Feature selection I, selecting for feature information | ||||
Feature selection II, selecting for model accuracy | ||||
Feature extraction | ||||
Designing Machine Learning Workflows in Python | The Standard Workflow | 4 | 16 | 51 |
The Human in the Loop | ||||
Model Lifecycle Management | ||||
Unsupervised Workflows | ||||
Case Study: School Budgeting with Machine Learning in Python | Exploring the raw data | 4 | 15 | 51 |
Creating a simple first model | ||||
Improving your model | ||||
Learning from the experts | ||||
Machine Learning for Time Series Data in Python | Time Series and Machine Learning Primer | 4 | 13 | 53 |
Time Series as Inputs to a Model | ||||
Predicting Time Series Data | ||||
Validating and Inspecting Time Series Models | ||||
Machine Learning for Marketing in Python | Machine learning for marketing basics | 4 | 16 | 53 |
Churn prediction and drivers | ||||
Customer Lifetime Value (CLV) prediction | ||||
Customer segmentation | ||||
Human Resources Analytics: Predicting Employee Churn in Python | Introduction to HR Analytics | 4 | 14 | 44 |
Predicting employee turnover | ||||
Evaluating the turnover prediction model | ||||
Choosing the best turnover prediction model | ||||
Machine Learning for Finance in Python | Preparing data and a linear model | 4 | 15 | 59 |
Machine learning tree methods | ||||
Neural networks and KNN | ||||
Machine learning with modern portfolio theory | ||||
Extreme Gradient Boosting with XGBoost | Classification with XGBoost | 4 | 16 | 49 |
Regression with XGBoost | ||||
Fine-tuning your XGBoost model | ||||
Using XGBoost in pipelines | ||||
Parallel Programming with Dask in Python | Working with Big Data | 4 | 17 | 58 |
Working with Dask Arrays | ||||
Working with Dask DataFrames | ||||
Working with Dask Bags for Unstructured Data | ||||
Case Study: Analyzing Flight Delays | ||||
Fraud Detection in Python | Introduction and preparing your data | 4 | 16 | 57 |
Fraud detection using labeled data | ||||
Fraud detection using unlabeled data | ||||
Fraud detection using text | ||||
Cluster Analysis in Python | Introduction to Clustering | 4 | 14 | 46 |
Hierarchical Clustering | ||||
K-Means Clustering | ||||
Clustering in Real World | ||||
Model Validation in Python | Basic Modeling in scikit-learn | 4 | 15 | 47 |
Validation Basics | ||||
Cross Validation | ||||
Selecting the best model with Hyperparameter tuning. | ||||
Hyperparameter Tuning in Python | Hyperparameters and Parameters | 4 | 13 | 44 |
Grid search | ||||
Random Search | ||||
Informed Search | ||||
Ensemble Methods in Python | Combining Multiple Models | 4 | 15 | 52 |
Bagging | ||||
Boosting | ||||
Stacking | ||||
Practicing Machine Learning Interview Questions in Python | Data Pre-processing and Visualization | 4 | 16 | 60 |
Supervised Learning | ||||
Unsupervised Learning | ||||
Model Selection and Evaluation | ||||
Analyzing IoT Data in Python | Accessing IoT Data | 4 | 16 | 53 |
Processing IoT data | ||||
Analyzing IoT data | ||||
Machine learning for IoT |
Deep Learning Tracks
Course | Chapter | H | Videos | Exercises |
---|---|---|---|---|
Introduction to Deep Learning in Python | Basics of deep learning and neural networks | 4 | 17 | 50 |
Optimizing a neural network with backward propagation | ||||
Building deep learning models with keras | ||||
Fine-tuning keras models | ||||
Introduction to Deep Learning with Keras | Introducing Keras | 4 | 15 | 59 |
Going Deeper | ||||
Improving Your Model Performance | ||||
Advanced Model Architectures | ||||
Introduction to Deep Learning with PyTorch | Introduction to PyTorch | 4 | 17 | 53 |
Artificial Neural Networks | ||||
Convolutional Neural Networks (CNNs) | ||||
Using Convolutional Neural Networks | ||||
Introduction to TensorFlow in Python | Introduction to TensorFlow | 4 | 15 | 51 |
Linear models | ||||
Neural Networks | ||||
High Level APIs | ||||
Recurrent Neural Networks for Language Modeling in Python | Recurrent Neural Networks and Keras | 4 | 16 | 54 |
RNN Architecture | ||||
Multi-class classification | ||||
Sequence to Sequence Models | ||||
Predicting CTR with Machine Learning in Python | Introduction to CTR and Basic Techniques | 4 | 15 | 57 |
Exploratory CTR Data Analysis | ||||
Model Applications and Improvements | ||||
Deep Learning | ||||
Image Processing in Python | Introducing Image Processing and scikit-image | 4 | 16 | 54 |
Filters, Contrast, Transformation and Morphology | ||||
Image restoration, Noise, Segmentation and Contours | ||||
Advanced Operations, Detecting Faces and Features | ||||
Image Processing with Keras in Python | Image Processing With Neural Networks | 4 | 13 | 45 |
Using Convolutions | ||||
Going Deeper | ||||
Understanding and Improving Deep Convolutional Networks | ||||
Biomedical Image Analysis in Python | Exploration | 4 | 15 | 54 |
Masks and Filters | ||||
Measurement | ||||
Image Comparison | ||||
Advanced Deep Learning with Keras | The Keras Functional API | 4 | 13 | 46 |
Two Input Networks Using Categorical Embeddings, Shared Layers, and Merge Layers | ||||
Multiple Inputs: 3 Inputs (and Beyond!) | ||||
Multiple Outputs |
Natural Language Processing Tracks
Course | Chapter | H | Videos | Exercises |
---|---|---|---|---|
Introduction to Natural Language Processing in Python | Regular expressions & word tokenization | 4 | 15 | 51 |
Simple topic identification | ||||
Named-entity recognition | ||||
Building a "fake news" classifier | ||||
Regular Expressions in Python | Basic Concepts of String Manipulation | 4 | 15 | 54 |
Formatting Strings | ||||
Regular Expressions for Pattern Matching | ||||
Advanced Regular Expression Concepts | ||||
Sentiment Analysis in Python | Sentiment Analysis Nuts and Bolts | 4 | 16 | 60 |
Numeric Features from Reviews | ||||
More on Numeric Vectors: Transforming Tweets | ||||
Let's Predict the Sentiment | ||||
Natural Language Generation in Python | Introduction to sequential data | 4 | 13 | 52 |
Write like Shakespeare | ||||
Translate words to a different language | ||||
Autocomplete your sentences | ||||
Feature Engineering for NLP in Python | Basic features and readability scores | 4 | 15 | 52 |
Text preprocessing, POS tagging and NER | ||||
N-Gram models | ||||
TF-IDF and similarity scores | ||||
Machine Translation in Python | Introduction to machine translation | 4 | 16 | 58 |
Implementing an encoder decoder model with Keras | ||||
Training and generating translations | ||||
Teacher Forcing and word embeddings | ||||
Spoken Language Processing in Python | Introduction to Spoken Language Processing with Python | 4 | 14 | 53 |
Using the Python SpeechRecognition library | ||||
Manipulating Audio Files with PyDub | ||||
Processing text transcribed from spoken language | ||||
Building Chatbots in Python | Chatbots 101 | 4 | 15 | 49 |
Understanding natural language | ||||
Building a virtual assistant | ||||
Dialogue | ||||
Advanced NLP with spaCy | Finding words, phrases, names and concepts | 5 | 15 | 55 |
Large-scale data analysis with spaCy | ||||
Processing Pipelines | ||||
Training a neural network model |