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Machine Learning Training in Hyderabad

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Advanced Machine Learning

Tools Covered

R Programming Language, Python, Jupyter, Spark, H2O, AzureML


  • Understanding of data science
  • Passion for new technologies

What you will learn

  • Machine Learning Tools In demand by MNCs
  • Machine Learning Methods with real world case studies

Course Content

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

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