Machine Learning Training with R & Python
Course Objective
This Level focuses on building all the important and popular Machine Learning Models and algorithms that provide you the basis of working as a professional Data Scientist. You will learn and work hands-on to create your models based on different advanced techniques as well as learn how to evaluate and enhance the model’s efficiencies and accuracy.
Suitable For
All those working/aspiring to work as Data Scientists.Since this is an advance level, it is required that the participants have strong Foundation in Statistics as well as proficiency in big data analytics and Visualization using R or Python along with required business experience to be able to apply the new skills.
Ambeone’s Certification Training in Machine learning with R & Python for Data Science, Artificial Intelligence makes you an expert Data Scientist.
In this advance Data Science Module, we cover training in Machine Learning algorithms with R and Python based on strong foundation of Statistical concepts for model interpretation and evaluation.
We also introduce practical applications of Machine Learning in various business domains.
Predictive Analytics is the core concept underlying most Machine Learning applications and is at the cutting edge of decision making in many leading businesses and governments around the world.Different Machine Learning models based on the Predictive analytics are covered in this module.
We cover the most important and popular Machine Learning Models and Algorithms used in Business and Research environment for prediction, forecasting, segmentation and recommendations in functions related to Marketing, HR, Finance, Supply Chain, Operations etc across Industries.
Example Regression models are being used to predict consumer demand and Classification models are being used to predict if a loan-applicant should be granted a loan or not. A/B testing is being employed to determine which color in a marketing add generates more revenue and recommender systems are being used by E-commerce stores to determine which items a first-time visitor maybe interested in.
We also cover important forecasting models in machine learning for Time Series like ETS and ARIMA with coverage of statistical concepts behind it.
Text and Sentiment analysis along with web scraping techniques – all fast growing Machine Learning applications are also covered in this module.
Topics covered in Training Module for Machine Learning with R & Python
- Introduction to Machine Learning
- Linear Regression
- Logistic Regression
- Classification
- Decision Tree
- Random Forest
- KNN Clustering
- Recommender Systems
- Basic Time Series Forecasting
- ETS and ARIMA for TimeSeries
- Text and Sentiment Analysis
- Social Media ,Twitter Analysis
- Brief introduction to Neural Networks & Deep Learning
Once you have mastered topics covered in our Machine Learning Training Module, You can start working as a full fledged Data Scientist and apply your skills in any department in any industry. May it be in HR for a Logistics Company, Sales of a Retail Giant or even Engineering Analysis in a Utility Company. Machine Learning is going to be everywhere very soon!
Our carefully planned and recommended learning structure for Data Science Series
| Level | Course Name | Know More |
|---|---|---|
| Level I | Fundamental of Data Analytics & Interpretation ,Simple Measures of Data | Click here |
| Level II | Business Analytics with KPI Measurement using Statistics | Click here |
| Level III | Statistics for Data Analytics & Data Science | Click here |
| Level IV | Big Data Analytics & Visualization with R | Click here |
| Level V | Advanced Data Mining & Manipulation with Python | Click here |
| Level VI | Predictive Modeling & Evaluation with Machine Learning | Click here |
| Level VII | Advance Machine Learning and Artificial Intelligence with R & Python | Click here |
| Level VIII | Applied Analytics-Using Data Science & Machine Learning in Business Analytics | Click here |
| Level IX | Neural Network & Unsupervised Learning for Advanced AI | Click here |
Course Duration
24 Hours of Instructor Led Sessions
+
30 Hours of Assignments & Capstone Project
Course Format
Four Hours Sessions each Weekend for 6 Weeks for Public batches
or
Five Days Intensive Bootcamps/Workshop for International/ Corporate batches
Course Pre-requisite
- Participants in this course must have completed all our earlier levels in Data Science Series course covering Statistics and Big Data Analytics and Visualization using R or Python or both as well as Predictive Modeling using regression.
- They must demonstrate good understanding of the following topics by passing an examination prior to course registration.
- Fundamentals of Statistics. This course will be heavily using Statistical concepts to build AI models and it is expected participants have a robust understanding of Statistics.
- Data Exploration & Statistical Modeling. This course build on our previous course of Big Data Analytics. It is expected that participants of this course have a good understanding of how to conduct Data Exploration & set-up Statistical Models.
- We offer Discount Packages for the prerequisite introductory courses needed to successfully complete this course. Contact our team to know more.
Big Data Analytics is an evolving science with new and unique applications cropping up every day. Check out our carefully planned and recommended sequence of training courses that will ensure you gain all the necessary skills needed in a systematic and meaningful way to succeed as a Data Scientist!
Machine Learning Training Course Schedule
| Course | Course Format | Start Date | Duration | Register |
Course Details for Training in Machine Learning with R & Python
- Duration of the course is 24 hours of Instructor Led Class Room code-along Training with 30 hours of self/group study with assignments Hours.
- Students have to submit at least two Capstone project with their Machine Learning Alogrithm using R or Python programming and Advanced Analytics.
- This course is offered as ,
- As part of our Six Month Associate in Big Data Analytics.
- We currently offer this course Dubai, Abu Dhabi, Delhi, Mumbai, Bangalore and Nigeria.
- Checkout our course schedule more information.
- Participants must bring their own laptop. Preferably with more than 8GB ram.
- Our Machine Learning Training Module will be mainly taught using R & Python as the programming language.
- All softwares and Databases used for the course are open-source and participants will be taught how to install and set-up the environment.
- All training topics covered in the course will be taught using relevant industry specific case studies and examples.
- The course is very hands-on/practical and is not a lecture or seminar. Participants will be expected to complete exercises and case studies on their own with necessary support and guidance from the instructor.
Steps to become a Big Data Scientist
Knowledge of Statistics Techniques.
- Aggregate functions like Mean, Medium & Mode
- Probability Theory
- Normal & Gaussian Distribution
- Confidence Intervals
- Hypothesis Testing
- Intro to Linear Regression
Data Interpretation, Business Intelligence & Business Insights
- Foundations of Statistical Modelling & Statistical Inference
- Sampling Strategies & Experimental Designs
- Understanding Sources of Data
- Generating Key Performance Indicators (KPI’s)
- Implementing Business Intelligence Solutions
- Interpreting Data Trends & Generating Business Insights
- Making Business Decisions based on Data Insights
Big Data Analytics with R & Python
- Analyze large and complex datasets with ease.
- Clean untidy datasets and merge datasets.
- Advanced data exploration and data mining.
- Advanced data visualizations and graphs.
- Machine learning with R & Python.
Machine Learning & Predictive Modeling
- Linear & Logistic Regression
- Classification – Decision Tree & Random Forest
- K-Means Cluster Analysis
- Bayesian ML : A/B Testing
- Support Vector Machines
- Recommender Systems
- Principal Component Analysis
- Intro to Natural Language Processing & Deep Learning
- Recommender Systems
Artificial Intelligence & Deep Learning
- Neural Networks
- Perceptron & Activation Functions
- Cost Functions & Gradient Descent Back propagation
- TensorFlows & Theano Implementation
- Convolution Neural Networks
- Recurrent Neural Networks
- Artificial Intelligence: Reinforcement Learning
- Vibrational Autoencoders
- Generative Adversarial Networks (GANS)