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 1618 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.
 Prerequisites 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
Syllabus
Machine Learning classes in Pune
Software to be used in Class:
 Anaconda Distribution (Python 3.X) : https://www.anaconda.com/distribution/
 Pycharm Community Edition : https://www.jetbrains.com/pycharm/download/
 Create Kaggle Account : https://www.kaggle.com/
 Create GIT Account and Install Bash Utility: https://github.com
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 StepbyStep
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
 Boxplot with Whiskers
 Summarizing Plots, Univariate, Bivariate and Multivariate analysis
 Multivariate Probability Density, Contour Plot
Linear Algebra and Calculus
 Introduction to Vectors(2D, 3D, nD) , Row Vector and Column Vector
 Dot Product and Angle between 2 Vectors
 Projection and Unit Vector
 Equation of a line (2D), Plane(3D) and Hyperplane (nD), Plane Passing through origin, Normal to a Plane
 Distance of a point from a Plane/Hyperplane, HalfSpaces
 Equation of a Circle (2D), Sphere (3D) and Hypersphere (nD)
 Equation of an Ellipse (2D), Ellipsoid (3D) and Hyperellipsoid (nD)
 Square ,Rectangle
Machine Learning Introduction
 What is Machine Learning?
 Machine Learning Process
 Different Categories of Machine Learning: Supervised,Unsupervised and Reinforcement
 ScikitLearn Overview
 ScikitLearn cheatsheet
Classification – kNearest Neighbor(KNN)
 Classification and Regression
 Application, Advantages and Disadvantages
 Distance Metric – Euclidean, Manhattan, Chebyshev, Minkowski
 Measuring accuracy using CrossValidation, Stratified kfold, Confusion Matrix, Precision, Recall, F1score.
Projects on KNN:
 Breast Cancer Wisconsin (Diagnostic) Project using KNN : https://www.kaggle.com/uciml/breastcancerwisconsindata
 Iris Species: https://www.kaggle.com/uciml/iris
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
 Logprobabilities 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
 Nonlinearly 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:
 Digit Recognizer: https://www.kaggle.com/c/digitrecognizer
 Titanic: Machine Learning from Disaster : https://www.kaggle.com/c/titanic
Linear Regression
 What is Linear Regression
 Geometric intuition of Linear Regression
 Mathematical formulation
 Real world Cases
 Code sample for Linear Regression
 Predicting Boston House Prices : https://www.kaggle.com/schirmerchad/bostonhoustingmlnd
 Insurance forecast : https://www.kaggle.com/mirichoi0218/insurance
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 hyperparameters.
 Support Vector Regression
 Breast Cancer Wisconsin (Diagnostic) Project using KNN  https://www.kaggle.com/uciml/breastcancerwisconsindata
 Iris Species: https://www.kaggle.com/uciml/iris
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 hyperparameters using GridSearch CV.
Projects
 IBM HR Analytics Employee Attrition and Performance  https://www.kaggle.com/pavansubhasht/ibmhranalyticsattritiondataset
 Zomato Restaurants Data  https://www.kaggle.com/shrutimehta/zomatorestaurantsdata
Unsupervised Learning
 Metrics for Clustering
 KMeans: Geometric intuition, Centroids
 KMeans: Mathematical formulation: Objective function
 KMeans Algorithm.
 How to initialize: KMeans++
 Failure cases/Limitations
 KMedoids
 Determining the right K
Projects
 Analyze Lending Club's issued loans  https://www.kaggle.com/wendykan/lendingclubloandata
 Credit Card Dataset for Clustering  https://www.kaggle.com/arjunbhasin2013/ccdata
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
Project:
 Compares socioeconomic info with suicide rates by year and country: https://www.kaggle.com/russellyates88/suicideratesoverview1985to2016
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.
Rating:
Inquire Now For Machine Learning Classes:
 Pimple Saudagar: +91 8698585003
 Baner: +91 7620182669
 Kharadi: +91 8551013133
 Noida: +91 8080934159