Machine Learning with R and Python
- Introduction to Supervised Learning
- Introduction to unsupervised learning
- Introduction to reinforcement learning
- Machine Learning versus Rule-based programming
- Understanding What Machine Learning can do using the Tasks Framework
- Creating Machine-Learning Models with Python and scikit learn.
- Types of datasets used in Machine Learning.
- Life Cycle of Machine Learning
- Dealing with Missing Values – An example
- Standardization and Normalization to Deal with Variables with Different Scales
- Types of scaling techniques
- Eliminating Duplicate Entries
- Learning Rules to Classify Objects?
- Understanding Logistic Regression
- Applying Logistic Regression to The Iris classification Task
- Closing Our First Machine Learning Pipeline with a Simple Model Evaluator
- Creating Formulas that predict the Future – A House Price Example
- Understanding Linear Regression
- Applying Linear Regression to the Boston House Price Task
- Evaluating Numerical Predictions with Least Squares
- Gradient Descent Algorithm
- Batch Gradient Descent
- Stochastic Gradient Descent algorithm
- Exploring Unsupervised Learning and Its Usefulness
- Finding Groups Automatically with k-means clustering
- Reducing The Number of variables in your data with PCA
- Smooth out your Histograms with kernel Density Estimation
- Decision Trees Classifier
- Decision Tree Regressor
- Random Forest Classifier
- Random Forest Regressor
- Automatic Feature Engineering with Support Vector Machines
- Deal with Nonlinear Relationships with Polynomial Regression
- Reduce the number of Learned Rules with Regularization
- Using Feature Scaling to Standardize Data
- Implementing Feature Engineering with Logistic Regression
- Extracting Data with Feature Selection and Interaction
- Combining all Together
- Build Model Based on Real-world Problems
- Support Vector machines
- Implementing kNN on the Data set
- Decision Tree as Predictive Model
- Dimensionality Reduction techniques
- Combining all Together
- Random Forest for Classification
- Gradient Boosting Trees and Bayes Optimization
- CatBoost to Handle Categorical Data
- Implement Blending
- Implement Stacking
- Memory-Based Collaborative Filtering
- Item-to-Item Recommendation with kNN
- Applying Matrix Factorization on Datasets
- Word batch for Real-world Problem
- Validation Dataset Tuning.
- Regularizing model to avoid over fitting
- Adversarial Validation
- Perform metric Selection on real Data.
- Tune a linear model to predict House prices
- Tune an SVM to predict a politician’s Party Based on their Voting Record
- Splitting your datasets into train, test and validate
- Persist Models by Saving Them to Disk
- Transform your variable length Features into One-Hot Vectors
- Finding the most important Features in your classifier
- Predicting Multiple Targets with the Same Dataset
- Retrieving the Best Estimators after Grid Search
- Extracting Decision Tree Rules from Scikit-learning
- Finding out which features are important in Random Forest Model
- Classifying with SVMs, when your data has unbalanced classes
- Computing True/False Positives/Negatives after in scikit-learn
- Labelling Dimensions with Original Feature Names after PCA
- Clustering Text Documents with Scikit-learn k-means
- Listing Word Frequency in a Corpus Using Only scikit-learn
- Polynomial Kernel Regression Using Pipelines
- Visualize outputs over two dimensions using Numpy’s Meshgrid
- Drawing out a Decision Tree Trained in scikit-learn
- Clarify your Histogram by Labeling each Bin
- Centralizing Your Color legend when you have multiple subplots
- Programming with TENSORFLOW
- Implementation of all above models with TENSORFLOW