ML is a subset of AI. It is defined as the collection of using various algorithms to teach computers to find patterns in data to be used for future prediction and forecasting or as a quality check for performance optimization. ML provides computers the ability to learn without being explicitly programmed.

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This Machine Learning course curriculum is an intensive application-oriented, real-world scenario-based program using Python & Machine Learning. This course is a 120 hours program, intensive skill-oriented, practical training program required for building ML-based models. It is designed to give the participant enough exposure to the variety of applications that can be built using techniques covered under this program. This course is designed for professionals from a variety of IT backgrounds. No prior knowledge of statistics or modeling is assumed.


This curriculum of Machine Learning (Basic & Advance) course has been designed for all levels, regardless of your prior knowledge of analytics, statistics, or coding. Familiarity with mathematics is helpful for this course.

Key Learning Outcomes:

When you complete this Machine Learning (Basic& Advance) course, you will be able to accomplish the following:

  • Stay Industry-relevant and grow in your career.
  • Create ML solutions for various business problems.
  • Understand how to apply ML practices in real-world scenarios
  • Define effective objectives for ML projects
  • Work with different types of data.
  • Build and deploy production-grade ML applications.
  • Apply ML methods, techniques, and tools immediately.

Target Audience

This course is ideal for anyone who wishes to learn the details of ML best practices and pursue a career in this growing field of Machine Learning.

Test & Evaluation

  • During the program, the participants will have to take all assignments given to them for better learning.
  • At the end of the program, a final assessment will be conducted


All successful participants will be provided with a certificate of completion.
• Students who do not complete the course / leave it midway will not be awarded any certificate.


120 hours (60 hours ONLINE LIVE sessions + 60 Hours of assignment)

Delivery Mode:

Online Live Instructor led learning.

  • Introduction of Machine Learning
  • Evolution of Machine Learning
  • Application of Machine Learning
  • Introduction
  • AI & ML for Products or Services
  • How Google Uses Artificial Intelligence and Data Science
  • How LinkedIn Uses Artificial Intelligence and Data Science
  • How Amazon Uses Artificial Intelligence and Data Science
  • Netflix: Using Artificial Intelligence and Data Science to Drive Engagement
  • Media and Entertainment Industry
  • Education Industry
  • Healthcare Industry
  • Government
  • Difference between Traditional Programming and ML Programming
  • Requirements for Machine Learning Practical Implementation
  • Required software and tools for Machine Learning implementations
  • Setup Anaconda
  • Installation of PyCharm or Spyder or Jupyter
  • Configure PyCharm/Spyder/Jupyter with Anaconda
  • Introduction
  • Variables
  • Data Types with Python
  • Assisted Practice: Data Types in Python
  • Keywords and Identifiers
  • Expressions
  • Basic Operators
  • Operators in Python
  • Functions
  • Search for a Specific Element from a Sorted List
  • String Operations
  • String Operations in Python
  • Tuples
  • Tuples in Python
  • Lists
  • Lists in Python
  • Sets
  • Sets in Python
  • Dictionaries
  • Dictionary in Python
  • Dictionary and its Operations
  • Conditions and Branching
  • While Loop
  • For Loop
  • Break and Continue Statements
  • Learning Objectives
  • File Handling
  • File Opening and Closing
  • Reading and Writing Files
  • Directories in File Handling
  • Assisted Practice: File Handling
  • Modules and Packages
  • Assisted Practice: Package Handling
  • Learning objectives
  • NumPy
  • Create and Print Numpy Arrays
  • Operations
  • Executing Basic Operations in Numpy Array
  • Performing Operations Using Numpy Array
  • Demonstrate the Use of Copy and Use
  • Manipulate the Shape of an Array
  • Learning Objectives
  • Introduction to Pandas
  • Data Structures
  • Create Pandas Series
  • Data Frame
  • Create Pandas Data Frames
  • Missing Values
  • Handle Missing Values
  • Various Data Operations
  • Data Operations in Pandas Data Frame
  • Learning Objectives
  • Data Visualization
  • Considerations of Data Visualization
  • Factors of Data Visualization
  • Python Libraries
  • Create Your First Plot Using Matplotlib
  • Line Properties
  • Multiple Plots and Subplots
  • Create a Plot with Annotation
  • Create Multiple Subplots Using plt.subplots
  • Types of plots
  • Create a Stacked Histogram
  • Create a Scatter Plot of Pretest scores and Posttest Scores
  • Create a Pie Chart
  • Create a Bar Chart
  • Create Box Plots
  • Analyzing Variables Individually
  • Key Takeaways
  • Types of Machine Learning
  • Labeled Data and Unlabeled Data
  • Steps of Machine Learning
  • Concept of Collecting the historic training Data for ML
  • Concept of Preprocess data for Machine Learning
  • Concept of Train the ML model
  • Concept of Test the ML Algorithm
  • Concept of using the ML Algorithm
  • Introduction
  • Types of Data collection- Offline Data and Online Data
  • Practical implementations of Reading the offline dataset using Numpy
  • Practical implementations of Reading the online dataset using Numpy
  • Practical implementations of Reading the offline dataset using Pandas
  • Practical implementations of Reading the online iris dataset using Pandas
  • Introduction
  • Types of Machine Learning
  • Labeled Data and Unlabeled Data
  • Concept of Supervised Machine Learning
  • Concept of Unsupervised Machine Learning
  • Regression and Classification
  • Linear Regression and Logistic Regression
  • Introduction
  • Concept of Univariate plots
  • Univariate Histogram Plots.
  • Univariate Density Plots.
  • Univariate Box and Whisker Plots.
  • Concept of Multivariate plots
  • Correlation Matrix Plot
  • Scatter Matrix Plot
  • Introduction
  • Need for Data Pre-processing
  • Data Transforms Steps
  • Types of Data Transformation Methods
  • Rescale Data
  • Standardize Data
  • Normalize Data
  • Binarize Data
  • Introduction
  • Implementation Foundation of Supervised Machine Learning Algorithms
  • Regression and Classification
  • Linear Regression and Logistic Regression
  • Practical implementations of Supervised ML Algorithms- Linear Regression
  • Practical implementations of Supervised ML Algorithms- Logistic Regression
  • Concept of Sigmoid Function
  • k-NN Algorithm
  • Naive Bayes Classifiers
  • Decision trees and etc.
  • Concept of Support vector machines
  • Introduction to concepts of forecasting
  • Introduction
  • Evaluate Machine Learning Algorithms
  • Split into Train and Test Sets
  • K-fold Cross-Validation
  • Leave One Out Cross-Validation
  • Repeated Random Test-Train Splits
  • What Techniques to Use When
  • Introduction
  • Algorithm Evaluation Metrics
  • Logistic Regression Algorithm Performance Evaluation Metrics
  • Classification Accuracy (Default).
  • Logarithmic Loss.
  • Area Under ROC Curve (AUC).
  • Confusion Matrix.
  • Classification Report.
  • Linear Regression Algorithm Performance Evaluation Metrics
  • Mean Absolute Error.
  • Mean Squared Error.
  • R2 Error
  • Introduction
  • Concepts and Steps of Unsupervised Machine Learning Algorithm
  • Concept of Clustering,
  • Practical implementations of Machine Learning Unsupervised Algorithms
  • K-Means Clustering.
  • Concept of Algorithm Spot-Checking
  • Algorithms Overview
  • Linear Machine Learning Algorithms Spot-check
  • Nonlinear Machine Learning Algorithms Spot-check
  • Introduction
  • Finalize Your Model with pickle
  • Finalize Your Model with Joblib
  • Project Descriptions
  • Datasets
  • Coding & Implementations
  • Performance Evaluation

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