Machine Learning Internship – 27th October Batch
This Internship training leverages Machine Learning and Python with Numpy, Panda, and more to work on real industry challenges.
This internship is focused on efficiency: never spend time on confusing, out of date, incomplete ways of learning. Get handson training on Machine Learning. This comprehensive and projectbased internship will introduce you to all of the modern skills of a Data Scientist and along the way, you will build many realworld projects to add to your portfolio and solve some real industry challenges. This training would cover the following topics:
 Statistics
 Python Programming
 Introduction to Machine Learning
 Numpy Library
 Pandas Library
 Matplotlib Library
 Sklearn Library
 Linear Regression
 Logistic Regression
 Decision Tree and Random Forest
 Ensemble Techniques
 Naves Bayes and Support vector machine
 Unsupervised Learning Algorithms
 Dimensionality reduction Algorithms
 Introduction to Deep learning
Who this internship is for:
 Anyone with zero experience (or beginner/junior) who wants to learn Machine Learning, Data Science, Python and has a passion for statistics and Mathematics.
 You’re looking for one single opportunity to get hands ON Machine learning training to catch up to speed with the modern techniques of the industry.
Internship Features
 Lectures 29
 Quizzes 19
 Duration 18 Hours
 Skill level Beginner
 Language English
 Students 2003
 Assessments Yes

Training Week1
 ML Live Introductory Session Details
 ML 1) Introduction to Statistics
 ML Quiz 1: Introduction to Statistics
 ML 2) Summary Statistics
 ML Quiz 2: Summary Statistics
 ML 3) Probability
 ML Quiz 3: Probability
 ML 4) Permutations & Combinations
 ML Quiz 4: Permutations & Combinations
 ML 5) Discrete Probability Distributions
 ML 6) Continuous Probability Distributions
 ML 7) Inferential Statistics
 ML Quiz 7: Inferential Statistics
 ML 8) Basics of Python Programming
 ML Quiz 8: Basic of Python Programming
 ML 9) Advanced Python Programming
 ML Quiz 9: Advanced Python Programming
 ML 10) Python Libraries: Numpy
 ML Quiz 10: Python Libraries: Numpy

Training Week2
 ML 11) Python Libraries: Pandas
 ML Quiz 11: Python Libraries: Pandas
 ML 12) Python Libraries: Matplotlib
 ML Quiz 12: Python Libraries: Matplotlib
 ML 13) Introduction to Machine Learning
 ML Quiz 13: Introduction to Machine Learning
 ML 14) Python Libraries: Sklearn
 ML Quiz 14: Python Libraries: Sklearn
 ML 15) Linear Regression
 ML Quiz 15: Linear Regression
 ML 16) Logistic Regression
 ML Quiz 16: Logistic Regression

Training Week3
 ML 17) Decision tree and Random forest
 ML Quiz 17: Decision tree and Random forest
 ML 18) Ensemble techniques
 ML Quiz 18: Ensemble techniques
 ML 19) Navise Bayes and SVM
 ML Quiz 19: Naive Bayes and SVM
 ML 20) Unsupervised learning
 ML Quiz 20: Unsupervised learning
 ML 21) Key ML Algorithms – KNN
 ML Quiz 21: Key ML Algorithms – KNN
 ML 22) Neural Network and Deep Learning

Internship Week1
Your Internship Project is divided in ten steps. Understand the problem well and then work your way through the steps.

Internship Week2
Follow the process of Project Submission to ensure ontime and correct submission