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Pragmatic Machine Learning

  /    /  Pragmatic Machine Learning

Machine Learning for Software Engineers

Categories:
Machine Learning
Reviews:

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.

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
Understanding the Big Picture
  • 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
Environment for Experiments
  • Installing Anaconda
  • Setting up Jupyter Notebook
  • Experiencing Notebooks
  • Introduction to Google Colab
  • Hands-on Exercise(s)
Understand Basic Statistics
  • 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)
Lego of Machine Learning: NumPy
  • Working with NumPy Array
  • NumPy Arrays Compared to Python Lists
  • Manipulating Arrays
  • Hands-on Exercise(s)
Storytelling with Data
  • 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)
Working with Pandas
  • 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)
Data Preparation
  • 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)
Feature Engineering
  • What is Feature Engineering?
  • Why Feature Engineering?
  • How to apply Feature Engineering?
  • Discussions on various scenarios
  • Hands-on Exercise(s)
Machine Learning Concepts
  • 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)
Machine Learning Concepts
  • 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)
Model Evaluation Metric
  • Accuracy
  • Evaluation Metric for imbalanced datasets
    • Precision
    • Recall
  • Confusion Matrix
  • Regression Metrics
Supervised Machine Learning
  • Strategies for Splitting Data 
  • Key Classification Algorithms
  • Conditional Probability 
  • Proof of Bayes Theorem
  • Naïve Bayes Classifier
  • Linear and Logistic Regressions
  • Decision Trees
  • Hyper Parameter Tuning
  • Hands-on Exercise(s)
Un-Supervised Machine Learning
  • Key types of Unsupervised ML
  • Performing Clustering of data
  • Principal Component Analysis
  • Hands-on Exercise(s)

Participants should preferably have some hands-on experience in programming language. Knowledge of Python would be a plus.

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
  • Solve Classification and Regression problems

Course Information

Duration

3 Days

Mode of Delivery

Instructor led/Virtual

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