Practical Machine Learning with Python

This program is designed to give a deep dive into machine learning applications for the application developers. Program covers the basics of data science to cloud deployment of developed models with application integration. You’ll learn about data processing, developing model, deploying the model and integration with real systems.

As part of this program you will learn basic and advanced python programming for data science. Project based training will give you the perception of real world problems in consumer and industrial segments.

Training Details

Location

Timing

COST

Action

Practical Machine Learning with Python

4th Floor, Plot no 26, Gafoor Nagar, Madhapur, Beside Hotel Westin, Hyderabad, Telangana – 500081

Weekend, Sat & Sun (within 3 Months)

Rs. 30000

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View Curriculum

Data Science

Module 1 - Defining Data Science
  • What is data science?
  • There are many paths to data science
  • Any advice for a new data scientist?
  • What is the cloud?
  • "Data Science: The Sexiest Job in the 21st Century"
Module 2 - What do data science people do?
  • A day in the life of a data science person
  • R versus Python?
  • Data science tools and technology
  • "Regression"
Module 3 - Data Science in Business
  • How should companies get started in data science?
  • "The Final Deliverable"
Module 4 - Use Cases for Data Science
  • Applications for data science
  • "The Report Structure"
Module 5 -Data Science People
  • Things data science people say
  • "What Makes Someone a Data Scientist?"

Python for Data Science

Module 1 - Python Basics
  • Your first program
  • Types
  • Expressions and Variables
  • String Operations
Module 2 - Python Data Structures
  • Lists and Tuples
  • Sets
  • Dictionaries
Module 3 - Python Programming Fundamentals
  • Conditions and Branching
  • Loops
  • Functions
  • Objects and Classes
Module 4 - Working with Data in Python
  • Reading files with open
  • Writing files with open
  • Loading data with Pandas
  • Working with and Saving data with Pandas
Module 5 - Introducing Jupyter Notebooks
  • What are Jupyter notebooks?
  • Getting started with Jupyter
  • Data and Notebooks in Jupyter
  • Sharing your Jupyter Notebooks and data
  • Apache Spark in Jupyter Notebooks
  • LAB: Getting Started with Jupyter Notebooks

Data Science Math Skills

Module 1 - Introduction to Descriptive Statistics
  • Central Tendency
  • Variability
  • Standard Normal Distribution
  • Sampling Distribution
Module 2 - Introduction to Inferential Statistics
  • Estimation
  • Hypothesis Testing
  • t-test
  • Chi-square test
  • Anova
  • Correlation
  • Regression
Module 3 - From Concepts to Data Analysis
  • Conditional Probability
  • Probability Distribution
  • Bayes Theorem

Data Analysis with Python

Module 1 - Importing Datasets
  • Learning Objectives
  • Understanding the Domain
  • Understanding the Dataset
  • Python package for data science
  • Importing and Exporting Data in Python
  • Basic Insights from Datasets
Module 2 - Cleaning the Data
  • Identify and Handle Missing Values
  • Data Formatting
  • Data NormalizationSets
  • Binning
  • Indicator variables
Module 3 - Summarizing the Data Frame
  • Descriptive Statistics
  • Basic of Grouping
  • ANOVA
  • Correlation
  • More on Correlation
Module 4 - Model Development
  • Simple and Multiple Linear Regression
  • Model Evaluation Using Visualization
  • Polynomial Regression and Pipelines
  • R-squared and MSE for In-Sample Evaluation
  • Prediction and Decision Making
Module 5 - Model Evaluation
  • Model Evaluation
  • Over-fitting, Under-fitting and Model Selection
  • Ridge Regression
  • Grid Search
  • Model Refinement

Data Visualization with Python

Module 1 - Introduction to Visualization Tools
  • Introduction to Data Visualization
  • Introduction to Matplotlib
  • Basic Plotting with Matplotlib
  • Dataset on Immigration to Canada
  • Line Plots
Module 2 - Basic Visualization Tools
  • Area Plots
  • Histograms
  • Bar Charts
Module 3 - Specialized Visualization Tools
  • Pie Charts
  • Box Plots
  • Scatter Plots
  • Bubble Plots
Module 4 - Advanced Visualization Tools
  • Waffle Charts
  • Word Clouds
  • Seaborn and Regression Plots
Module 5 - Creating Maps and Visualizing Geospatial Data
  • Introduction to Folium
  • Maps with Markers
  • Choropleth Maps

Machine Learning with Python

Module 1 - Supervised vs Unsupervised Learning
  • Machine Learning vs Statistical Modelling
  • Supervised vs Unsupervised Learning
  • Supervised Learning Classification
  • Unsupervised Learning
Module 2 - Supervised Learning I
  • Regression Algorithms
  • Model Evaluation
  • Model Evaluation: Overfitting & Underfitting
  • Understanding Different Evaluation Models
Module 3 - Supervised Learning II
  • K-Nearest Neighbors
  • Support Vector Machine
  • Navie Bayes
  • Decision Trees
  • Random Forests
  • Reliability of Random Forests
  • Advantages & Disadvantages of Decision Trees
  • Model Evaluation
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 - Association Rule Learning
  • Apriori
  • Eclat
Module 6 - Dimensionality Reduction & Collaborative Filtering
  • Dimensionality Reduction: Feature Extraction & Selection
  • Collaborative Filtering & Its Challenges
Module 7- Natural Language Processing
  • Introduction to Natural Language Processing
  • NLP Tasks and Text Similarity
  • Syntax and Parsing
  • Language Modeling and Word Sense Disambiguation
  • Part of Speech Tagging and Information Extraction
  • Text Summarization
  • Collocations and Information Retrieval
  • Sentiment Analysis and Semantics
  • Discourse, Machine Translation, and Generation
  • Hands-on projects with Natural Language Toolkit

Machine Learning – IBM Data Science Experience

Module 1 – Introduction to Data Platform Overview
  • Setting up IBM data platform
  • Getting started with DSX
  • Getting started with Data Catalog
  • Getting Started with IBM Data Refinery
Module 2 – Organize resources in a project
  • Set up a project
  • Watson Data Platform projects
  • Project Collaborators
  • Add associated services
Module 3 – Prepare data
  • Add data to a project
  • Refine data
  • Ingest streaming data
Module 4 – Working with Jupyter Notebooks
  • Create notebooks
  • Code and run notebooks
  • Share and publish notebooks
Module 5 – Machine Learning Flows
  • Creating Machine Learning flows with IBM SPSS
  • Creating Machine Learning flows with Spark MLlib
Module 6 – Watson Machine Learning
  • Setting up your machine learning environment
  • Building models
  • Deploying machine learning
Module 7 – Visualizations
  • PixieDust
  • Brunel visualization
  • SPSS model visualization
Module 8 – Predictive Analytics Algorithms
  • Classification and regression
  • Clustering
  • Forecasting
  • Survival analysis
Key Features:
  • Learn python from basic to advanced level
  • Project-based learning with real-application development
  • Practical model development & deployment with cloud-based machine learning services (IBM Watson Machine Learning)
  • Covers use cases from consumer & industrial sectors
  • More than 2-Projects on each topic
  • Complete hands-on based training
  • Best training for transforming your career
  • Deep dive into concepts & algorithms
  • Get Internship Certification
  • Affordable cost
  • Accessible Location

Program Duration: 
3 Months (Sat & Sun)

Program Cost: 
Rs. 30,000/-

Location: 
4th Floor, Plot no 26, Gafoor Nagar, Madhapur, Beside Hotel Westin, Hyderabad, Telangana – 500081

Contact Person:
Srikanth Dakoju – 9490386807
Sneha Kandacharam – 9618000216

Email:
Machine Learning <mltraining@thesmartbridge.com>