# Machine Learning for Software Engineers (Adaptilab)

Data Modeling with scikit-learn - Cross-Validation Learn about K-Fold cross-validation and why it’s used. Data Modeling with scikit-learn - Logistic Regression Implement logistic regression for classification tasks. Clustering with scikit-learn - Mean Shift Clustering Use mean shift clustering to determine the optimal number of clusters. Data Preprocessing with scikit-learn - Normalizing Data Learn about data normalization and implement a normalization function. Gradient Boosting with XGBoost - Storing Boosters Save and load Booster objects using XGBoost binary files. Deep Learning with TensorFlow - Softmax Use the softmax function to convert a neural network from binary to multiclass classification. View the lesson [here](https://www.educative.io/collection/page/6083138522447872/5629499534213120/6500697489014784). Data Analysis with pandas - Indexing Understand how DataFrame values can be accessed via indexing. Gradient Boosting with XGBoost - XGBoost Regressor Create an XGBoost regressor object. Deep Learning with Keras - Sequential Model Learn how a neural network is built in Keras. View the lesson [here](https://www.educative.io/collection/page/6083138522447872/5629499534213120/5225451066753024). Clustering with scikit-learn - Feature Clustering Use agglomerative clustering for feature dimensionality reduction. Data Preprocessing with scikit-learn - Standardizing Data Learn about data standardization and implement it with scikit-learn. Data Modeling with scikit-learn - Exhaustive Tuning Use exhaustive grid search techniques for hyperparameter tuning. Clustering with scikit-learn - DBSCAN Learn about the DBSCAN clustering algorithm. Deep Learning with Keras - Model Configuration Configure the Keras model for training. View the lesson [here](https://www.educative.io/collection/page/6083138522447872/5629499534213120/6551204391813120). Data Preprocessing with scikit-learn - Data Range Create a function to compress data into a specific range of values. Gradient Boosting with XGBoost - Model Persistence Save and load XGBoost models using joblib. Deep Learning with TensorFlow - Logits Dive into the inner layers of a neural network and understand the importance of logits. View the lesson [here](https://www.educative.io/collection/page/6083138522447872/5629499534213120/5673385510043648). Deep Learning with TensorFlow - Optimization Learn about loss functions and optimizing neural network weights. View the lesson [here](https://www.educative.io/collection/page/6083138522447872/5629499534213120/5150194068881408). Data Analysis with pandas - File I/O Read from and write to different types of files in pandas. Data Analysis with pandas - To NumPy Understand how DataFrames can be converted to 2-D NumPy arrays. Data Analysis with pandas - Series Learn about the pandas Series object for 1-D data. Data Modeling with scikit-learn - Bayesian Regression Learn about Bayesian regression techniques. Data Manipulation with NumPy - Random Generate numbers and arrays from different random distributions. Data Preprocessing with scikit-learn - Labeled Data Separate the PCA components of a dataset by class. Gradient Boosting with XGBoost - Hyperparameter Tuning Apply grid search cross-validation to XGBoost models. Data Analysis with pandas - DataFrame Learn about the pandas DataFrame object for 2-D data. Data Analysis with pandas - Sorting Sort DataFrames based on their column features. Clustering with scikit-learn - Quiz Have questions about Quiz? Go for it! Clustering with scikit-learn - K-Means Clustering Learn about the K-Means clustering algorithm and how it works. Data Manipulation with NumPy - Indexing Index into NumPy arrays to extract data and array slices. Clustering with scikit-learn - Cosine Similarity Learn about the cosine similarity metric and how it’s used. Data Analysis with pandas - Plotting Learn how to plot DataFrames using the pyplot API from Matplotlib. Data Preprocessing with scikit-learn - Data Imputation Learn about data imputation and the various methods to accomplish it. Clustering with scikit-learn - Introduction An overview of unsupervised learning and clustering. Data Analysis with pandas - Features Learn about the different feature types that can be part of a dataset. Data Analysis with pandas - Grouping Learn how DataFrames can be grouped based on particular columns. Data Modeling with scikit-learn - LASSO Regression Apply regularization with LASSO regression. Data Preprocessing with scikit-learn - PCA Learn about PCA and why it’s useful for data preprocessing. Data Modeling with scikit-learn - Ridge Regression Understand the need for regularization in linear regression. Data Manipulation with NumPy - Introduction An overview of data processing and the NumPy library. Data Modeling with scikit-learn - Linear Regression Learn about basic linear regression and how it’s used. Data Analysis with pandas - Combining Combine multiple DataFrames through concatenation and merging. Data Analysis with pandas - Metrics Use pandas to obtain statistical metrics for data. Clustering with scikit-learn - Nearest Neighbors Understand the purpose of finding nearest neighbors for data points. Data Modeling with scikit-learn - Applying CV to Decision Trees Apply K-Fold cross-validation to decision trees. Data Analysis with pandas - Quiz Have questions about Quiz? Go for it! Deep Learning with TensorFlow - Multiclass Understand the differences between binary and multiclass classification. View the lesson [here](https://www.educative.io/collection/page/6083138522447872/5629499534213120/4795694659403776). Gradient Boosting with XGBoost - Cross-Validation Use cross-validation to evaluate parameters for XGBoost. Data Manipulation with NumPy - Filtering Filter NumPy data for specific values. Data Manipulation with NumPy - Statistics Learn how to apply statistical metrics to NumPy data. Data Preprocessing with scikit-learn - Robust Scaling Understand how outliers can affect data and implement robust scaling. Deep Learning with Keras - Model Execution Learn how to train, evaluate, and make predictions with a Keras model. View the lesson [here](https://www.educative.io/collection/page/6083138522447872/5629499534213120/4793497078988800). Data Modeling with scikit-learn - Training and Testing Separate a dataset into training and testing sets. Deep Learning with Keras - Model Output Complete a multilayer perceptron model in Keras. View the lesson [here](https://www.educative.io/collection/page/6083138522447872/5629499534213120/6200871962542080). Clustering with scikit-learn - Hierarchical Clustering Learn about hierarchical clustering via the agglomerative approach. Data Modeling with scikit-learn - Quiz Have questions about Quiz? Go for it! Gradient Boosting with XGBoost - XGBoost Classifier Create an XGBoost classifier object. Data Analysis with pandas - Filtering Filter DataFrames for values that fit certain conditions. Deep Learning with TensorFlow - Quiz Have questions about Quiz? Go for it! View the lesson [here](https://www.educative.io/collection/page/6083138522447872/5629499534213120/4971899245297664). Deep Learning with TensorFlow - Model Initialization Learn about the input and output layers of a neural network. View the lesson [here](https://www.educative.io/collection/page/6083138522447872/5629499534213120/5754903989321728). Deep Learning with TensorFlow - Training Initialize and train a TensorFlow neural network using actual training data. View the lesson [here](https://www.educative.io/collection/page/6083138522447872/5629499534213120/5730192894984192). Data Modeling with scikit-learn - Introduction An overview of the main models used in scikit-learn. Deep Learning with TensorFlow - Introduction An overview of the multilayer perceptron neural network and deep learning in TensorFlow. View the lesson [here](https://www.educative.io/courses/machine-learning-for-software-engineers/NE5LpPrWrKv). Deep Learning with Keras - Quiz Have questions about Quiz? Go for it! View the lesson [here](https://www.educative.io/collection/page/6083138522447872/5629499534213120/6287990907207680). Data Modeling with scikit-learn - Evaluating Models Learn how to evaluate classification and regression models. Gradient Boosting with XGBoost - Introduction An intro to XGBoost and gradient boosted decision trees. What you'll learn from this course - Overview Have questions about Overview? Go for it! Data Manipulation with NumPy - NumPy Arrays Learn about NumPy arrays and how they’re used. Data Manipulation with NumPy - Saving Data Learn how to save and load NumPy data. Data Manipulation with NumPy - NumPy Basics Perform basic operations to create and modify NumPy arrays. Data Manipulation with NumPy - Aggregation Use aggregation techniques to combine NumPy data and arrays. Data Manipulation with NumPy - Quiz Have questions about Quiz? Go for it! Data Modeling with scikit-learn - Decision Trees Learn about decision trees and how they’re used. Gradient Boosting with XGBoost - XGBoost Basics Learn about the basics of using XGBoost. Deep Learning with TensorFlow - Linear Limitations An overview of the limitations of a single layer perceptron model. View the lesson [here](https://www.educative.io/collection/page/6083138522447872/5629499534213120/5634536736030720). Gradient Boosting with XGBoost - Feature Importance Learn about feature importance in making model predictions. Gradient Boosting with XGBoost - Quiz Have questions about Quiz? Go for it! Deep Learning with Keras - Introduction An overview of the Keras API and how it compares to TensorFlow. View the lesson [here](https://www.educative.io/collection/page/6083138522447872/5629499534213120/5846602390437888). Deep Learning with Keras - Course Conclusion Have questions about Course Conclusion? Go for it! View the lesson [here](https://www.educative.io/collection/page/6083138522447872/5629499534213120/4539084066258944). Deep Learning with TensorFlow - Metrics Discover the most commonly used metrics for evaluating a neural network. View the lesson [here](https://www.educative.io/collection/page/6083138522447872/5629499534213120/5678807906254848). Deep Learning with TensorFlow - Evaluation Evaluate a fully trained neural network using the model accuracy as the evaluation metric. View the lesson [here](https://www.educative.io/collection/page/6083138522447872/5629499534213120/5310004745732096). Data Manipulation with NumPy - Math Understand how arithmetic and linear algebra work in NumPy. Data Analysis with pandas - Introduction An overview of data analysis with pandas. Clustering with scikit-learn - Evaluating Clusters Learn how to evaluate the performance of clustering algorithms. Data Preprocessing with scikit-learn - Introduction An overview of industry data science and the scikit-learn API. Data Preprocessing with scikit-learn - Quiz Have questions about Quiz? Go for it! Deep Learning with TensorFlow - Hidden Layer An overview of the limitations of a single layer perceptron model. View the lesson [here](https://www.educative.io/collection/page/6083138522447872/5629499534213120/6084189277388800).