Machine Learning Training in Hyderabad

Machine Learning with R and Python

Section 1

• 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

Section 2

• 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

Section 3

• 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

Section 4

• Programming with TENSORFLOW
• Implementation of all above models with TENSORFLOW