Machine Learning Training in Hyderabad

Become master in data scientist with our Prime Training.
874 ratings
4.6/5
Course Duration

60 Days

Training Options

Classroom / Online

machine learning course

Course Overview

Advanced Machine Learning

Tools Covered

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

Pre-requisites

  • 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

5 Reasons to Join

Syllabus

Customized syllabus based on industry needs.

Real time

Real time industry experienced trainers.

Limited

Limited students per batch.

Resume

Resume building Interview preparation.

Placement

Dedicated placement support.

Training Options

Classroom Training

  • Customized curriculum as per industry needs.
  • Training by real-time professionals.
  • The best classroom and Lab infrastructure.
  • Student friendly staff and management.
  • 365 Days placement support with partner clients.

Online Training

  • Live demonstration of of features and practicals.
  • Get LMS access of each online training session that you attend through GotoMeeting.
  • Gain guidance on certification.
  • Attend a Free Demo before signing up.

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

Upcoming Batch Schedules

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