Machine Learning for Software Engineers

Machine Learning for Software Engineers

Machine Learning for Software Engineers
Machine Learning for Software Engineers

Machine Learning for Software Engineers

If you're a software engineer looking to add Machine Learning to your skillset, this is the place to start.

This course will teach you to write useful code and create impactful Machine Learning applications immediately. From the start, you'll be given all the tools that you need to create industry-level machine learning projects.

Rather than reading through dense theory, you’ll learn practical skills and gain actionable insights. Topics covered include data analysis/visualization, feature engineering, supervised learning, unsupervised learning, and deep learning.

All topics are taught with industry standard frameworks: NumPy, pandas, scikit-learn, XGBoost, TensorFlow, and Keras.

Basic knowledge about Python is a prerequisite to this course.

This course was created by AdaptiLab, a company specializing in evaluating, sourcing, and upskilling enterprise machine learning talent. It is built in collaboration with industry machine learning experts from Google, Microsoft, Amazon, and Apple.

Contents

1. What you'll learn from this course

  • Overview
2. Data Manipulation with NumPy
  • Introduction
  • NumPy Arrays
  • NumPy Basics
  • Math
  • Random
  • Indexing
  • Filtering
  • Statistics
  • Aggregation
  • Saving Data
  • Quiz
3. Data Analysis with pandas
  • Introduction
  • Series
  • DataFrame
  • Combining
  • Indexing
  • File I/O
  • Grouping
  • Features
  • Filtering
  • Sorting
  • Metrics
  • Plotting
  • To NumPy
  • Quiz
4. Data Preprocessing with scikit-learn
  • Introduction
  • Standardizing Data
  • Data Range
  • Robust Scaling
  • Normalizing Data
  • Data Imputation
  • PCA
  • Labeled Data
  • Quiz
5. Data Modeling with scikit-learn
  • Introduction
  • Linear Regression
  • Ridge Regression
  • LASSO Regression
  • Bayesian Regression
  • Logistic Regression
  • Decision Trees
  • Training and Testing
  • Cross-Validation
  • Applying CV to Decision Trees
  • Evaluating Models
  • Exhaustive Tuning
  • Quiz
6. Clustering with scikit-learn
  • Introduction
  • Cosine Similarity
  • Nearest Neighbors
  • K-Means Clustering
  • Hierarchical Clustering
  • Mean Shift Clustering
  • DBSCAN
  • Evaluating Clusters
  • Feature Clustering
  • Quiz
7. Gradient Boosting with XGBoost
  • Introduction
  • XGBoost Basics
  • Cross-Validation
  • Storing Boosters
  • XGBoost Classifier
  • XGBoost Regressor
  • Feature Importance
  • Hyperparameter Tuning
  • Model Persistence
  • Quiz
8. Deep Learning with TensorFlow
  • Introduction
  • Model Initialization
  • Logits
  • Metrics
  • Optimization
  • Training
  • Evaluation
  • Linear Limitations
  • Hidden Layer
  • Multiclass
  • Softmax
  • Quiz
9. Deep Learning with Keras
  • Introduction
  • Sequential Model
  • Model Output
  • Model Configuration
  • Model Execution
  • Quiz
  • Course Conclusion
https://www.educative.io/courses/machine-learning-for-software-engineers?aff=xDzJ

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