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Machine Learning


Machine Learning Training in Pune

About the Course

Ethans training institute, Pune introduce you world class Machine Learning training in Pune (Pimple saudagar, Baner and Kharadi area). Our Machine Learning Training includes Python Programming, Machine Learning with Python.

  • Python & Machine Learning Training at Ethans Tech is providing by Jatin, Abhinandan, Nikhil, Kalpesh, Parth, Vikram and Ayush Sir at three different locations in Pune, i.e. Pimple Saudagar, Baner and Kharadi. All  trainers are working professional having average 10+ years of exp in IT. They have an average 4.5/5 star feedback by the students.  
  • Our Python and ML program consist, Python Foundation, DB Interface, Regular Ex, API Development, Webscrapping, Machine Learning Algos in details. For Details Syllabus visit our Syllabus tab.
  • This program expected to take 16-18 weekends with total 30 classes, each class is having three hours training. It can take lesser time if the number of hours per day is increased.
  • Duration: Duration of the program is 100 hours, consist weekdays and weekends classes.
  • Pre-requisites for Machine Learning Training: No prerequisites required to learn ML (This course is very useful for students).
  • Lab:  55 hours’ lab sessions + 6 mini projects are included in the program
  • Objective of this program is to provide 100% practical, interview and certification oriented training. It is expected that students will easily crack Python amd ML interview and have advance knowledge of ML algos.
  •  Lab: Execises on Python, Multiple Algorithims in ML

After the Machine Learning classes:

  • Students is confident in Python and ML, able to do projects indivisually.
  • Students will have good understanding on Python and Machine Learning, help them to crack interviews
  • Deep Learning Concepts are clear and ready towork indivisually

Who gets this machine learning training in pune?

  • Robotics Engineer
  • Data Scientist
  • Business Analysts
  • Hadoop Developers
  • Python for Data Science
  • College Graduates



Machine Learning classes in Pune

Software to be used in Class:

  1. Anaconda Distribution (Python 3.X) :
  2. Pycharm Community Edition : 
  3. Create Kaggle Account :   
  4. Create GIT Account and Install Bash Utility:  

Python Prerequisites need to be completed before the Data Science / Machine Learning Class

Introduction to Anaconda Distribution

  • What is Anaconda Distribution?
  • How it is different from Python Distribution?
  • How to install Anaconda?
  • conda repository
  • Anaconda Navigator
  • pip and conda to get new package
  • pip and conda commands
  • set Virtual
  • Integrating Anaconda with Pycharm

Using Git and GitHub

  • Setting up Your GitHub Account
  • Configuring Your First Git Repository
  • Making Your First Git Commit
  • Pushing Your First Commit to GitHub
  • Git and GitHub Workflow Step-by-Step

Introduction to SQL and DataBases

  • SQL/RDBMS database management
  • SQL Queries
  • CRUD Operations

Introduction to numpy and statistical Analysis 

  • What is numpy?
  • numpy performance test
  • Introduction to numpy arrays and Matrices
  • Indexing and Selection 
  • Introduction to numpy function
  • Numpy Operations Array with Array, Array with Scalars, Universal Array Functions
  • Dealing with Flat files using numpy
  • Mathematical functions
  • Statisticals function

Introduction to Pandas and Data Analysis 

  • Integrating Anaconda with Pycharm 
  • What is Pandas 
  • Creating Series 
  • Creating Data Frames
  • Grouping, Sorting 
  • Group by Operations 
  • Merging, Joining and Concatenating DataFrame
  • Pandas Operations 
  • Data Input and Output from a variety of data formats like csv, excel, db, json and html
  • Missing Data (Imputation)
  • Data analysis with data set 
  • Practical use cases using data analysis

Statistics and Probability 

  • Type of Dataset: Numerical,Categorical and Ordinal 
  • Mean, Median and Mode 
  • Variance and Standard Deviation 
  • Probability Density Function(PDF) and Probability Mass Function (PMF) 
  • Percentiles and Moments 
  • Covariance and Correlation 
  • Conditional Probability
  • Bayes’ Theorem Module

Data Visualization using Matplotlib

  • Plotting for exploratory data analysis (EDA)
  • Line Graph on time Series
  • Pie Chart, Bar and Horizontal Bar Graph
  • Introduction to IRIS dataset 
  • 2D scatter plot 
  • Pair plots 
  • Histogram and Introduction to PDF(Probability Density Function) 
  • CDF(Cumulative Distribution Function) 
  • Mean, Variance and Standard Deviation 
  • Median
  • Percentiles and Quantiles 
  • Box-plot with Whiskers 
  • Summarizing Plots, Univariate, Bivariate and Multivariate analysis
  • Multivariate Probability Density, Contour Plo

Linear Algebra and Calculus 

  • Introduction to Vectors(2-D, 3-D, n-D) , Row Vector and Column Vector 
  • Dot Product and Angle between 2 Vectors 
  • Projection and Unit Vector 
  • Equation of a line (2-D), Plane(3-D) and Hyperplane (n-D), Plane Passing through origin, Normal to a Plane 
  • Distance of a point from a Plane/Hyperplane, Half-Spaces 
  • Equation of a Circle (2-D), Sphere (3-D) and Hypersphere (n-D) 
  • Equation of an Ellipse (2-D), Ellipsoid (3-D) and Hyperellipsoid (n-D) 
  • Square ,Rectangle 

Machine Learning Introduction 

  • What is Machine Learning?
  • Machine Learning Process 
  • Different Categories of Machine Learning: Supervised,Unsupervised and Reinforcement 
  • Scikit-Learn Overview 
  • Scikit-Learn cheat-shee

Classification – k-Nearest Neighbor(KNN)

  • Classification and Regression
  • Application, Advantages and Disadvantages
  • Distance Metric – Euclidean, Manhattan, Chebyshev, Minkowski
  • Measuring accuracy using Cross-Validation, Stratified k-fold, Confusion Matrix, Precision, Recall, F1-score.

Projects on KNN:

  1. Breast Cancer Wisconsin (Diagnostic) Project using KNN :
  2. Iris Species:

Classification – Naive Bayes 

  • Conditional probability
  • Independent vs Mutually exclusive events
  • Bayes Theorem with examples
  • Exercise problems on Bayes Theorem
  • Naive Bayes algorithm
  • Toy example: Train and test stages
  • Naive Bayes on Text data
  • Laplace/Additive Smoothing
  • Log-probabilities for numerical stability
  • Bias and Variance tradeoff
  • Feature importance and interpretability
  • Code example

 Logistic Regression

  • Extensions to Logistic Regression: Generalized linear models(GLM)
  • Code sample: Logistic regression, GridSearchCV, RandomSearchCV
  • Non-linearly separable data & feature engineering
  • Train & Run time space & time complexity
  • Collinearity of features
  • Feature importance and Model interpretability
  • Column Standardization
  • hyperparameter Search: Grid search and random search
  • Loss minimization interpretation
  • Probabilistic Interpretation: Gaussian Naive Bayes
  • L1 regularization and sparsity
  • L2 Regularization: Overfitting and Underfitting
  • Weight vector
  • Mathematical formulation of Objective function
  • Sigmoid function: Squashing
  • Geometric intuition of Logistic Regression

Projects on Regression:

  1. Digit Recognizer:
  2. Titanic: Machine Learning from Disaster :

Linear Regression 

  • What is Linear Regression
  • Geometric intuition of Linear Regression
  • Mathematical formulation
  • Real world Cases
  • Code sample for Linear Regression
  1. Predicting Boston House Prices :
  2. Insurance forecast :

Classification - SVM (Support Vector Machine)

  • Classification and Regression
  • Separating line, Margin and Support Vectors
  • Linear SVC Classification
  • Polynomial Kernel – Kernel Trick
  • Gaussian Radial Basis Function (rbf)
  • Grid Search to tune hyper-parameters.
  • Support Vector Regression
  1. Breast Cancer Wisconsin (Diagnostic) Project using KNN -
  2. Iris Species:

Classification - Decision Tree 

  • CART (Classification and Regression Tree)
  • Advantages and Disadvantages and its applications
  • Decision Tree Learning algorithms – ID3, C4.5, C5.0 and CART
  • Gini Impurity, Entropy and Information Gain
  • Decision Tree Regression
  • Visualizing a Decision Tree using graphviz module.
  • Regularization using tuning hyper-parameters using GridSearch CV.


  1. IBM HR Analytics Employee Attrition and Performance -
  2. Zomato Restaurants Data -

Unsupervised Learning

  • Metrics for Clustering
  • K-Means: Geometric intuition, Centroids
  • K-Means: Mathematical formulation: Objective function
  • K-Means Algorithm.
  • How to initialize: K-Means++
  • Failure cases/Limitations
  • K-Medoids
  • Determining the right K


  1.  Analyze Lending Club's issued loans -
  2.  Credit Card Dataset for Clustering

Density based clustering

  • DBSCAN (Density based clustering) Technique
  • Density based clustering
  • Eps: Density
  • Core, Border and Noise points
  • Density edge and Density connected points.
  • DBSCAN Algorithm
  • Hyper Parameters: MinPts and EpsA
  • Advantages and Limitations of DBSCAN


  1. Compares socio-economic info with suicide rates by year and country:

 Testimonial for Machine Learning:

  • Name: Rishabh Gupta

    Review: I did Machine Learning training from Ethan's Tech which was taken by Abinandan sir. Now, I have confidence of diving in Machine Learning world and not scared of mathsand Stats things that come along with it. I would definitely recommend for Machine Learning classes.


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