My-Learning-Tracks

Python Machine Learning Tracks

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