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Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience (historical data) without being explicitly programmed. Machine learning focuses on the development of computer programs and statistical models that can access data, find patterns, and use it for predictions.

In simple terms, Understanding the patterns in the data and formulating the common patterns in the form of a re-usable equation, which can be later used for prediction, to build pro-active business strategies.

What does this Course Cover?

  • Python Programming and Data manipulations
  • Data Cleaning and Preparation
  • Basic Statistics
  • Basic Machine Learning Algorithms
  • Advanced Machine Learning Algorithms
  • Model building, validating and finetuning
  • Feature engineering techniques
  • Tips and Tricks in ML projects

What Will You Learn in the Course?

  • How to apply machine learning to real-life business problems
  • How to build ML models and draw useful inferences from them
  • How to write Python programs and how to handle data in Python
  • How to perform fundamental descriptive analysis to gain insights from the data
  • How to explore, validate, and clean data in a systematic way
  • How to find associations between variables
  • How to predict a dependent variable using several independent factors
  • How to validate and finetune machine learning models

Key Features of this Course

  • 100% hands-on sessions
  • Learning through problem-solving and case studies
  • Continuous assessments and Feedback
  • Access to recorded videos
  • Taught by industry professionals with many years of experience in the field
  • It contains numerous examples right from the first lecture until the end.
  • Every concept is explained using a business scenario or case study.
  • It offers the right mix of theory and hands-on labs.
  • The course simplifies complex statistical concepts.
  • The course offers Python code and a sufficient number of data sets.
  • The course is self-sufficient. You do not need any other resource or reference to grasp the concepts.

Target Audience for this Course

  • Everybody who wants to get started with machine learning & deep learning
  • Reporting analysts who aim to become data scientists.
  • Predictive modeling profile candidates who want to learn ML and DL
  • Data visualization experts
  • Any Data science aspirants
  • Graduates and undergraduate students
  • Computer Science Engineering students

Pre-Requisites

  • This program is the first course in machine learning. There is no strict pre-requisite
  • Anyone with a primary degree can get started with this course
  • Basic mathematical skills are sufficient
  • Statistical Knowledge is NOT a prerequisite. It will be taught in class.
  • Cutting-edge programming knowledge is NOT a pre-requisite. Coding will be taught in the class.

Duration:

  • 3 Live Sessions Classes every week for 3 months
  • Each session is 4 hours

Lab Setup:

  • Windows 10 with 8 GB RAM.
  • Proxy-free internet, Admin rights to execute scripts
  • Firefox and Chrome browsers
  • Access to Google colab notebooks – https://colab.research.google.com/

 

How do we measure the impact of the training?

  • Post-training assessment for all the participants.
  • Project assignments after each module
  • Multiple quizzes in each module.

Course Delivery Plan

  • The training has four major phases in each concept discussion:
  • Theory, Demo, in-class exercise, and project assignment.
  • The algorithm theory will be explained first. The instructor will take a dataset and demonstrate the concept on a dataset. In the third phase, participants will try the code on a new dataset. In the fourth phase, a real-time dataset will be considered and floated as a project.

Course Curriculum

Module 1: Python Programming For Machine Learning and Basic Statistics
MLPy Live Module 1: Python Programming for Machine Learning 7 days
<ul> <li>Introduction to Machine Learning and Data Science</li> <li>Python Programming Basics</li> <li>Data Handling in Python</li> <li>Basic Statistics</li> </ul> <ul> <li>Mean, Median, Variance</li> <li>Percentiles</li> </ul> <ul> <li>Data Exploration, Validation and Cleaning</li> </ul>
Module 2: Supervised Machine Learning
MLPy Live Module 2 : Supervised Machine Learning 2 weeks
<ul> <li>Regression Analysis and Simple Regression</li> <li>Multiple Regression and Multi-collinearity</li> <li>Logistic Regression Model Building</li> <li>Decision Trees</li> <li>Information gain</li> <li> Over-fitting</li> <li>Pruning</li> <li>Model Validation and Selection<li> <li>Bias Variance Trade-off</li> <li>Feature Engineering</li> </ul>
Module 3: Unsupervised Machine Learning, Ensemble Learninga and Boosting
MLPy Live Module 3 : Unsupervised Learning, Ensemble Learning and Boosting 2 weeks
  • Unsupervised learning
    • Cluster Analysis
  • Ensemble Learning
    • Bagging
  • Random Forests
    • Hyperparameters in Random Forests
  • Gradient Boosting
    • Hyperparameters in Gradient Boosting
Module 4 : Artificial Neural Networks and Deep Learning
MLPy Live Module 4 : Artificial Neural Networks and Deep Learning 7 days
  • Artificial Neural networks
  • Concept of the decision boundary
  • The non-linear decision boundary
  • Gradient Descent and Backpropagation Algorithm
  • Deep Learning Introduction
    • CNN and RNN
Module 5: Natural Language Processing
MLPy Live Module 5 : Natural Language Processing 7 days
  • Text mining Basics
  • Preparing text for Analysis
  • Naive Bayes Model and Sentiment Analysis
  • Word2Vec Algorithm
Module 6: Final Project and Assesment
MLPy Live Module 6 : Final Project and Assessment 2 weeks
  • Additional Projects
  • Assessments
  • Documentation of the projects
  • Profile building
  • 15,000
  • 1 weekSTARTS IN
  • 3 months
  • 30 SEATS
  • Course Badge
12 STUDENTS ENROLLED

Statinfer

Statinfer derived from Statistical inference is a company that focuses on the data science training and R&D.We offer training on Machine Learning, Deep Learning and Artificial Intelligence using tools like R, Python and TensorFlow

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