Python for Data Science & Machine Learning

Python for Data Science & Machine learning in Manhattan New York – Tutor, Coach, Consultant or Freelancers

Price range $19-29 per hour Email: nyc@qcfinance.in

Three methods of course delivery used: 1) doubt based 2) real project based 3) book based (or combo).
Course is based on three books, which are given below. These are the most famous books and have several exercises and discussions available online.

Reference text books recommended to learn for the course:

  1. Learning python the hard way, pdf
  2. Python for Data Analysis, pdf
  3. Python Crash Course: A Hands-on, Project-based Introduction to Programming, pdf

Python data science Tutor in New York / Mentor $25/hr (Downtown Manhattan)

Do you need a Programming Tutor that gets you things automated?

Teaching method tailored to meet specific student needs/types

Introduction to Python Anaconda Jupyter
Data processing using Panda
Web scraping using beautiful soup
Regression and Monte Carlo intro
Data visualization using matplot
Introduction to SparkR
Learner project based on his work also investment and trading

Skills that you will GAIN while working on the course are:

  • Python Programming Language
  • Statistical Hypothesis Testing
  • IPython
  • Hypothesis-testing
  • NetworkX
  • Matplotlib
  • Numpy
  • Pandas
  • Scipy
  • Python Lambdas
  • Python Regular Expressions

Python Basics

An introduction to the basic concepts of Python. Learn how to use Python both interactively and through a script. Create your first variables and acquaint yourself with Python’s basic data types.

Learn to store, access and manipulate data in lists: the first step towards efficiently working with huge amounts of data.

Functions and Packages

To leverage the code that brilliant Python developers have written, you’ll learn about using functions, methods and packages. This will help you to reduce the amount of code you need to solve challenging problems!

 NumPy

NumPy is a Python package to efficiently do data science. Learn to work with the NumPy array, a faster and more powerful alternative to the list, and take your first steps in data exploration.

Course Syllabus

Section 1: Python Basics

Take your first steps in the world of Python. Discover the different data types and create your first variable.

Section 2: Python Lists

Get the know the first way to store many different data points under a single name. Create, subset and manipulate Lists in all sorts of ways.

 Section 3: Functions and Packages & Control flow and Pandas

Learn how to get the most out of other people’s efforts by importing Python packages and calling functions.

Write conditional constructs to tweak the execution of your scripts and get to know the Pandas DataFrame: the key data structure for Data Science in Python.

Section 4: Numpy and Matplotlib

Write superfast code with Numerical Python, a package to efficiently store and do calculations with huge amounts of data.

Create different types of visualizations depending on the message you want to convey. Learn how to build complex and customized plots based on real data.

Python data science Tutor $25/hr

Teaching method tailored to meet specific student needs/types

Introduction to Python Anaconda Jupyter
Data processing using Panda
Web scraping using beautiful soup
Regression and Monte Carlo intro
Data visualization using matplot
Introduction to SparkR
Learner project based on your work (marketing,investment and trading)

Collection of powerful, open-source, tools needed to analyze data and to conduct data science. Specifically, you’ll learn how to use:

  • python
  • jupyter anaconda notebooks
  • pandas
  • numpy
  • matplotlib
  • git
  • and many other tools.

We’ll cover the machine learning and data mining techniques real employers are looking for, including:

  • Regression analysis
  • K-Means Clustering
  • Principal Component Analysis
  • Train/Test and cross validation
  • Bayesian Methods
  • Decision Trees and Random Forests
  • Multivariate Regression
  • Multi-Level Models
  • Support Vector Machines
  • Reinforcement Learning
  • Collaborative Filtering
  • K-Nearest Neighbor
  • Bias/Variance Tradeoff
  • Ensemble Learning
  • Term Frequency / Inverse Document Frequency
  • Experimental Design and A/B Tests

 

Statistics and Probability Refresher, and Python 

  • Bayes’ Theorem
  • Predictive Models
  • Linear Regression
  • Polynomial Regression
  • Multivariate Regression, and Predicting Car Prices
  • Multi-Level Models
  •  Machine Learning with Python
  • Supervised vs. Unsupervised Learning, and Train/Test
  • Using Train/Test to Prevent Overfitting a Polynomial Regression
  • Bayesian Methods: Concepts
  • Implementing a Spam Classifier with Naive Bayes
  • K-Means Clustering
  • Clustering people based on income and age
  • Measuring Entropy
  • Install GraphViz
  • Decision Trees: Concepts
  • Decision Trees: Predicting Hiring Decisions
  • Ensemble Learning
  • Support Vector Machines (SVM) Overview
  • Using SVM to cluster people using scikit-learn
  •  User-Based Collaborative Filtering
  • Item-Based Collaborative Filtering
  • Finding Movie Similarities
  • Improving the Results of Movie Similarities
  • Making Movie Recommendations to People
  • Improve the recommender’s results
  •  More Data Mining and Machine Learning Techniques
  • K-Nearest-Neighbors: Concepts
  • Using KNN to predict a rating for a movie
  • Dimensionality Reduction; Principal Component Analysis
  • PCA Example with the Iris data set
  • Data Warehousing Overview: ETL and ELT
  • Reinforcement Learning
  •  Dealing with Real-World Data
  • Bias/Variance Tradeoff
  • K-Fold Cross-Validation to avoid overfitting
  • Data Cleaning and Normalization
  • Cleaning web log data
  • Normalizing numerical data
  • Detecting outliers
  • –Apache Spark: Machine Learning on Big Data
  • Installing Spark – Part
  • Spark Introduction
  • Spark and the Resilient Distributed Dataset (RDD)
  • Introducing MLLib
  • Decision Trees in Spark
  • K-Means Clustering in Spark
  • TF / IDF
  • Searching Wikipedia with Spark
  • Using the Spark 2.0 DataFrame API for MLLib
  • Experimental Design
  • A/B Testing Concepts
  • T-Tests and P-Values
  • Hands-on With T-Tests
  • Determining How Long to Run an Experiment
  • A/B Test Gotchas

 

Explore many algorithms and models:

Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.
Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests.
Get ready to do more learning than your machine!

COURSE SYLLABUS GUIDELINES
Module 1 – Machine Learning vs Statistical Modeling & Supervised vs Unsupervised Learning
Machine Learning Languages, Types, and Examples
Machine Learning vs Statistical Modelling
Supervised vs Unsupervised Learning
Supervised Learning Classification
Unsupervised Learning
Module 2 – Supervised Learning I

K-Nearest Neighbors
Decision Trees
Random Forests
Reliability of Random Forests
Advantages & Disadvantages of Decision Trees
Module 3 – Supervised Learning II
Regression Algorithms
Model Evaluation
Model Evaluation: Overfitting & Underfitting
Understanding Different Evaluation Models
Module 4 – Unsupervised Learning
K-Means Clustering plus Advantages & Disadvantages
Hierarchical Clustering plus Advantages & Disadvantages
Measuring the Distances Between Clusters – Single Linkage Clustering
Measuring the Distances Between Clusters – Algorithms for Hierarchy Clustering
Density-Based Clustering
Module 5 – Dimensionality Reduction & Collaborative Filtering

Dimensionality Reduction: Feature Extraction & Selection
Collaborative Filtering & Its Challenges

Advanced Data Science with Python: Machine Learning

 Prerequisites

Knowledge of Python programming and basic features of Python

Able to munge, analyze, and visualize data in Python with Pandas and charting

Syllabus

Unit 1: Introduction and Regression

How to dive into Machine Learning

Simple Linear Regression and Multiple Linear Regression

Forward and Backward Selection

Numpy/Scikit-Learn Lab

Class 2:

Part Classification I

Logistic Regression – Application in Default and other variables

Discriminant Analysis

Naive Bayes

Supervised Learning Lab

Resampling and Model Selection

Cross-Validation

Bootstrap – Breaking it down into simple

Feature Selection

Model Selection and Regularization lab

Class 3:

Classification II

Support Vector Machines SVM

Decision Trees – and Branch Analysis

Bagging and Random Forests

Decision Tree in Python and SVM Lab

Class 4:

Unsupervised Learning – Breaking it down

Principal Component Analysis

Kmeans and Hierarchical Clustering

PCA and Clustering Lab

 

 

Another variation of this course could be built on “automate the boring stuff with python” where the learner would learn with his data and work projects on how to automate office stuff.

We are a group of python automation consultant in Manhattan New York working remotely from India and onsite.

Although this course is based is more focused on Machine Learning.

We provide official automation services on Excel VBA Python SQL R in New York / Indore India

Automation is the next biggest revolution, legacy methods if not replaced by automation will reduce the productivity of the firm which might even lead to extinction.

Our can reduce a lot of manual work and use lot of Excel Analytics features, our clients have reduced work by upto 50-70% which helped me focus on their product and other value addition to their core business.

Get more hours from your employees and more robust analytical framework!

Please contact me for more details about various processes that we can automate.

Please email info@qcfinance.in to know more information.

Keywords: Private Home Tuition

Course is developed and taken by Shivgan Joshi:

satyadhar

Other Supporting Instructors and Course Developers for New York Clientele:

  1. Dr. Ankit Vora ankit@Qcfinance.in – New York
  2. Shubhangi Kulkarni shubh@qcfinance.in Online Tutor for New York
  3. Dr. Parag Parandkar parag@qcfinance.in – Online dedicated for New York Learner
  4. Piyush Ludh piyush@qcfinance.in – Business developer for New York Learners

About Shubhangi -

From Hyderabad, India and is having six years of experience in the IT industry in the domain of BFSI(US Home loan mortgages, credit cards,investment banking).Having completed an executive program in applied Finance from a premier institute in India called IIM Calcutta,I am currently working as a freelancer in the field of quant finance in the sense that I take online classes on VBA, R, on various topics in quantitative finance like derivatives pricing, time series, bayesian modeling, econometric modeling, credit risk using logistic regression,ordinal and multivariate regression, technical analysis of stocks,accounting which includes business valuation,M &A, project finance,interest rate derivatives,trading strategies,financial time series,data visualisation etc, I have a bachelors in engineering(Industrial Engineering and Management) as the base degree.

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