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Course Overview:

The course is a stepping stone to initiate your journey to become a Data Scientist/Machine Learning Expert. The course starts with a quick introduction to Data Science, ML, AI and Big Data. Initially it provides a high level overview about the Data Science Lifecycle then it starts to delve deeper into hands-on aspects of the DataScience Process. The course provides a pragmatic introduction to various steps involved in DataScience Process including Data ingestion, munging, exploratory data analysis, modeling, optimization etc. The intention is to enable participants how to solve problems, define strategy and uncover hidden needs. 


Intended Audience:

This program is designed for those who aspire for Data/ML/AI roles:

  • Software Engineers
  • Data Scientists
  • Machine Learning Engineers
  • Data Integration Engineers
  • Data Architects


Pre-requisites:

Participants should preferably have some hands-on experience in programming language like Python


Course Outcomes:

After this course, you will be able to:

  • Describe the role of Machine Learning and Data Science 
  • Apprehend role of Scalable Data Science
  • Use cases of Machine Learning
  • Understand various key tools for performing Data Science
  • Gain pragmatic understanding of Data Science Process
  • Understand types of Statistics
  • Identify visualization approaches for exploring data sets
  • Understand Supervised and Unsupervised Machine Learning
  • Which Algorithm to choose when
  • Solve Classification and Regression problems
  • Work with Decision tree, Random Forest


Course Duration:

This course will be delivered in 2 day

Course Outline




  • Significance of Data
  • What is Machine Learning (ML)?
  • Practical Use Cases
  • Concepts and Terms
  • Tools / Platforms for ML
  • Machine Learning End to End Pipeline
  • Data Science Process
  • Roles and Responsibilities of Data Engineer and Data Scientist
  • Installing Anaconda
  • Setting up Jupyter Notebook
  • Experiencing Notebooks
  • Hands-on Exercise(s)
  • Types of Statistics
  • Population and Sample
  • Measures of Central Tendency
  • Measures of Dispersion
  • Percentiles & Quartiles
  • Box plots and outlier detection
  • Hypothesis Testing
  • Z Test
  • Hands-on Exercise(s)
  • Working with NumPy Array
  • NumPy Arrays Compared to Python Lists
  • Manipulating Arrays
  • Hands-on Exercise(s)
  • Acquisition Approaches, Pros & Cons
  • Working with Beautiful Soup
  • Acquiring data using Twitter Streaming APIs
  • Connecting to External data sources
  • Hands-on Exercise(s)
  • Why is Storytelling important?
  • How to share stories?
  • Key types of plots
  • Demo: Exploratory Analysis using MatPlot Lob
  • Hands-on Exercises
  • Introduction to Seaborn
  • Demo: Working with Seaborn
  • Hands-on Exercise(s)
  • Key Tools for Data Manipulation
  • Basic types - Series and DataFrames
  • Working with a Series
  • Dataframe Operations
  • Creating a DataFrame from various sources
  • Data Manipulation using Pandas
  • Joining datasets
  • Hands-on Exercise(s)
  • How to make data useful for Machine Learning?
  • Exploratory Data Analysis
  • Data Cleaning techniques
    • Add default values
    • Remove incomplete rows
    • Deal with error-prone columns
    • Dealing with missing data
  • Data Preparation for ML
    • Normalize data types
    • Feature Scaling
    • Feature Standardization
    • Label Encoding
    • One-Hot Encoding
  • Hands-on Exercise(s)
  • What is Feature Engineering?
  • Why Feature Engineering?
  • How to apply Feature Engineering?
  • Discussions on various scenarios
  • Hands-on Exercise(s)
  • Types of Machine Learning
  • Key Algorithms in Machine Learning
  • Concepts and Terms
  • Why Scikit Learn?
  • Gradient Descent
  • Loss function
  • Bias vs Variance Tradeoff
  • Model Interpretability
  • Hands-on Exercise(s)
  • Accuracy
  • Evaluation Metric for imbalanced datasets
    • Precision
    • Recall
  • Confusion Matrix
  • Regression Metrics
  • Strategies for Splitting Data
  • Key Classification Algorithms
  • Conditional Probability
  • Proof of Bayes Theorem
  • Naïve Bayes Classifier
  • Linear and Logistic Regressions
  • Decision Trees and Random Forest
  • Hyper Parameter Tuning
  • Hands-on Exercise(s)
  • Key types of Unsupervised ML
  • Performing Clustering of data
  • Principal Component Analysis
  • Hands-on Exercise(s)
  • Machine Learning for Software Engineers - Level 2
    • How to improve Accuracy of Model
    • Practical Bias Variance Tradeoff Examples
    • Advanced Regression Techniques
    • Ensemble Models
    • More on Model Tuning
    • Cross Validation
    • Model Deployment



On Successful Completion of this Course,
You will receive below Certificate

Big Data Crash Course Certificate

Frequently Asked Questions


When will this class happen next?

In case, you are not available on the specified date then you can fill the training interest form and we will get back to you with next schedule

 


What are the system requirements for taking this course?

Your system must fulfill the following requirements:

  • 64-bit Operating System
  • Chrome/firefox/Internet explorer should be installed in your system
  • 8GB RAM
  • Internet speed of 10 MB/sec or more