Launch your career in Machine Learning by developing in-demand skills and become job-ready in 30 hours or less.
Highlights
Upgrade your career with top notch training
- Enhance Your Skills: Gain invaluable training that prepares you for success.
- Instructor-Led Training: Engage in interactive sessions that include hands-on exercises for practical experience.
- Flexible Online Format: Participate in the course from the comfort of your home or office.
- Accessible Learning Platform: Access course content on any device through our Learning Management System (LMS).
- Flexible Schedule: Enjoy a schedule that accommodates your personal and professional commitments.
- Job Assistance: Benefit from comprehensive support, including resume preparation and mock interviews to help you secure a position in the industry.
Outcomes
By the end of this course, participants will be equipped with:
- Python and Data Tools: Get comfortable with Python programming and able to use tools like Pandas and NumPy to work with data.
- Analyze and Visualize Data: Able to organize, analyze, and create charts with data to understand it better.
- Statistics and Testing: Able to use statistics and test ideas with methods like hypothesis testing and ANOVA to make sense of your data.
- Introduction to Machine Learning: Get an overview of machine learning, including how to use techniques like regression and clustering to solve problems.
- Build and Evaluate Models: Able to create and assess machine learning models, like decision trees and random forests, to make data-based decisions.
About
Python is ideal for machine learning and data analysis. You’ll start with the basics of Python programming and learn to use libraries like Pandas and NumPy for handling and manipulating data. You’ll also explore data visualization with matplotlib and apply statistical methods like hypothesis testing and ANOVA.
You’ll dive into machine learning concepts, including regression and logistic regression, and learn about time series, cluster analysis, and decision trees. These skills will help you build and use models to analyze data and make smart decisions.
Key Learnings
- Learn how to use Python and libraries like Pandas and NumPy to work with data.
- Discover how to create charts and graphs to understand and present data better.
- Understand how to use statistics and tests to make sense of your data.
- Get familiar with machine learning techniques for making predictions and analyzing patterns.
- Learn to use methods like decision trees and random forests for more complex data analysis.
Pre-requisites
- Basic Python Programming Skills: Participants should have a fundamental understanding of Python programming. You can also take <Programming Essentials> using Python before enrolling into this course.
- Understanding of Basic Mathematics and Statistics: A basic understanding of mathematical concepts and Statistics will be helpful.
- Basic Knowledge of Data Visualization (optional):
Familiarity with data visualization concepts and tools can enhance learning, although not strictly required.
Job roles and career paths
This training will equip you for the following job roles and career paths:
- Data Scientist
- Machine Learning Engineer
- Data Analyst
- Business Intelligence Analyst
- Data Engineer
Machine Learning with Python
The demand for machine learning with Python is very high and keeps growing. Many industries are using ML to improve their services and make better decisions based on data. Python is popular for this because it’s easy to use and has powerful libraries that help build ML models. As businesses generate more data and seek to use it smartly, they need experts who can apply ML techniques. This strong demand for ML skills with Python is expected to keep increasing as new technologies and applications continue to emerge.
Topics of Course
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Python—The Programming Language
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Installing Python
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Anaconda
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Spyder
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Jupyter notebook
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IDLE (Integrated Development Environment)
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Implement the Code Using an IDE
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Interact with Python
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Writing Python Code
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Make Calculations
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Import New Libraries and Functions
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Import additional libraries using pip install
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Import msgpack to satisfy basic requirement
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NumPy
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Pandas
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Matplotlib
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Pandas Data Structures
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Introduction
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Creating Your Own Data
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Types of Data
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The dtype Option
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The Series
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The list
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The tupple
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Difference between list & tupple
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The DataFrame
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Making Changes to Series and DataFrames
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Exporting and Importing Data
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CSV
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Excel
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Jason
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Aggregate Functions
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Indexing, Slicing, and Iterating
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Indexing
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Slicing
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Iterating an Array
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Conditions and Boolean Arrays
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Shape Manipulation
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The Index Objects
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Other Functionalities on Indexes
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Reindexing
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Dropping
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Arithmetic and Data Alignment
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Operations between Data Structures
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Flexible Arithmetic Methods
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Operations between DataFrame and Series
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Function Application and Mapping
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Functions by Element
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Functions by Row or Column
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Statistics Functions
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Sorting and Ranking
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“Not a Number” Data
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Assigning a NaN Value
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Working With Missing Data
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Filtering Out NaN Values
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Filling in NaN Occurrences
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Hierarchical Indexing and Leveling
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Reordering and Sorting Levels
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Summary Statistic by Level
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AB Testing
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Data Preparation
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Merging
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Merging Multiple Data Sets
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Concatenating
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Combining
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Pivoting
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Removing
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Data Transformation
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Tidy Data
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Removing Duplicates
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Mapping
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Discretization and Binning
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Detecting and Filtering Outliers
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Permutation
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String Manipulation
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More String Methods
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Built-in Methods for Manipulation of Strings
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Regular Expressions
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Data Aggregation
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Group By
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A Practical Example
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Hierarchical Grouping
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Group Iteration
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Chain of Transformations
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Functions on Groups
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Advanced Data Aggregation
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The matplotlib Library
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Installation
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matplotlib Architecture
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Backend Layer
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Artist Layer
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Scripting Layer (pyplot)
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pyplot
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Line chart
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Scatter plot
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Annotations: Add Text
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Annotations: Properties
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A Simple Interactive Chart
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Set the Properties of the Plot
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Working with Multiple Figures and Axes
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Adding Further Elements to the Chart
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Adding Text
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Adding a Legend
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Legends: Properties
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Saving Your Charts
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Saving the Code
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Saving Your Chart Directly as an Image
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Line Chart
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Line Charts with pandas
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Histogram
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Means: The Lure of Averages
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The Average in Python: mean()
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Medians: Caught in the Middle
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The Median in Python: median()
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Statistics à la Mode
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The Mode in Python
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Deviating from the Average
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Measuring Variation
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Back to the Roots: Standard Deviation
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Standard Deviation in Python
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Conditions, Conditions, Conditions …
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Meeting Standards and Standings
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Catching Some Z’s
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Hypotheses, Tests, and Errors
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Hypothesis Tests and Sampling Distributions
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Catching Some Z’s Again
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Z Testing in Python
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t for One
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t Testing in Python
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Working with t-Distributions f
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Visualizing t-Distributions
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Testing a Variance
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Working with Chi-Square Distributions
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Visualizing Chi-Square Distributions
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Hypotheses Built for Two
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Sampling Distributions Revisited
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t for Two
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Like Peas in a Pod: Equal Variances
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t-Testing in Python
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A Matched Set: Hypothesis Testing for Paired Samples
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Paired Sample t-testing in Python
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Testing Two Variances
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Testing More Than Two
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ANOVA in Python
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Another Kind of Hypothesis, Another Kind of Test
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Getting Trendy
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Trend Analysis in Python
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Cracking the Combinations, Two-way ANOVA in Python,
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Two kinds of Variables…at once
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After the Analysis
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Uses and abuses of machine learning
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Machine learning successes
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How machines learn
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Machine learning in practice
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Machine learning with Python
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Understanding regression
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Simple linear regression
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Ordinary least squares estimation
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Multiple Linear Regression
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Regression: What a Line!
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Linear Regression in Python
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Juggling Many Relationships at Once: Multiple Regression
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exploring and preparing the data
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ANOVA: Another Look
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Formulae and Linear Models
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Model Building
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training a model on the data
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evaluating model performance
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improving model performance
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Goodness of Fit with Data—The Perils of Overfitting
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Root-Mean-Square Error
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Model Simplicity and Goodness of Fit
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Assumption checking
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Assumption checking using packages
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Case studies of Linear Regression
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Estimation the quality of wines
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Price prediction of real estate
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Movie popularity prediction
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Retail sales prediction
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Understanding logistic regression
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The logit model
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Generalized Linear Model
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Simple logistic regression
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Multiple logistic regression
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Customer satisfaction analysis with the multiple logistic regression
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Multiple logistic regression with categorical data
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The Dataset and the Data Dictionary
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Data Import in Python
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EDD in Python
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Outlier Treatment in Python
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Missing Value treatment in Python
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Variable transformation and Deletion in Python
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Dummy variable creation in Python
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Automatic dummy variable creation
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Formulae and Logistic Models
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Model Building
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training a model on the data
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evaluating model performance
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improving model performance
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Goodness of Fit with Data—The Perils of Overfitting
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Confusion Matrix
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Creating Confusion Matrix in Python
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Introduction to Time Series Data
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Notation for Time Series Data
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Peculiarities of Time Series Data
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Setting the Frequency
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Treatment of missing values
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White Noise
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Stationarity
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Seasonality
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Correlation Between Past and Present Values
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The Autocorrelation Function (ACF)
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The Partial Autocorrelation Function (PACF)
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Picking the Correct Model
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The Autoregressive (AR) Model
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ARMA
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ARIMA
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Automatic ARIMA
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Unsupervised Learning & Clustering: theory
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K-Means Clustering: Theory
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Example K-Means Clustering in Python
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Visualize K-Means Results in Python
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Model-based Unsupervised Clustering in Python
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How to assess a Clustering Tendency of the dataset
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Selecting the number of clusters for unsupervised Clustering methods (K-Means)
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Assessing the performance of unsupervised learning (clustering) algorithms
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How to compare the performance of different unsupervised clustering algorithms?
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A Simple Tree Model
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Deciding How to Split Trees
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The stopping criteria for controlling tree growth
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Tree Entropy and Information Gain
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Pros and Cons of Decision Trees
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Tree Overfitting
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Pruning Trees
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Decision Trees for Classification
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Conditional Inference Trees
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Conditional Inference Tree Classification
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Building a decision tree in Python
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Model Validation
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Model Improvement
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Model Interpretation
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Ensemble technique
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Random Forest Classification
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Splitting Data into Test and Train Set in Python
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Choose the number of trees
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Model Validation
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Model Improvement
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Model Interpretation
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Accuracy of the model
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Decision Vs Random Forest
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