PRO
Machine Learning Essentials with Python
Machine Learning Essentials with Python
Learn to solve real-world data problems with this machine learning course in Python. Gain practical skills in regression, classification, clustering, feature engineering and model evaluation to build and fine-tune ML models.
Become a skilled professional
Learn from the best
Taught by top faculty & industry experts
Learn by doing
Apply skills with guided projects and interactive coding exercises
Mock Interview
Guided Projects
Coding Exercises
Learn with AI
Get AI-powered coding hints & instant doubt resolution
24x7 Doubt resolution
Coding Hints
Course outline
Industry focussed curriculum designed by experts
Introduction to Machine Learning
2 items
0.46 hr
- Introduction to Machine Learning - Part 1
- Introduction to Machine Learning - Part 2
Supervised Learning - Linear Regression
13 items
3.04 hr
- Supervised Machine Learning - Introduction
- Linear Regression and its Pearson’s Coefficient
- Linear Regression Mathematically and Coefficient of Determinant
- Model Analysis and Squared Errors
- Summary and Lab Exercise of Linear Regression
- Descriptive Analysis on the Dataset
- Analyse the Distribution - Dependent Column
- Missing Values Imputation
- Bivariate Analysis
- Building Model Using All Information
- Exploratory Data Analysis (EDA)
- Fluke Correlation
Supervised Learning - Logistic Regression
3 items
1.30 hr
- Classification Algorithm - Logistic Regression
- Logistic Regression Model and Sigmoid Function
- Logistic Regression, Confusion Matrix, Precision and Recall: Hands on
Introduction to Ensemble Techniques
11 items
2.26 hr
- Decision Trees - Introduction
- Decision Trees - Hands on Exercise
- Ensemble Methods
- Bagging
- Bagging - Hands on Exercise
- Boosting
- Types of Boosting
- Adaboosting - Hands on Exercise
- Gradient Boosting - Hands on Exercise
- Random Forest
- Random Forest - Hands on Exercise
Introduction to Unsupervised Learning
4 items
0.5 hr
- Unsupervised Learning - Clustering
- Clustering - Types and Distance
- Clustering - Distance Calculations
- K-means Clustering
Principal Component Analysis
2 items
0.27 hr
- Principal Component Analysis
- PCA for Dimensionality Reduction
Feature Engineering and Cross Validation
4 items
0.39 hr
- Cross Validation Concept and Procedure
- Implementing K Fold Cross Validation
- Some Salient Features of K-fold
- Bootstrap Sampling Concept and Hands-on
Model Performance Measures - Model and Hyperparameter Tuning
4 items
0.40 hr
- Model Tuning and Performance
- Hyperparameters and Tuning
- GridSearch
- RandomizedSearch CV
Guided Projects
Solve real-world projects with a step-by-step guide, starter code templates, and access to model solutions to boost your skills and build a standout resume.
- GUIDED PROJECT 1
- Loan Approval Prediction System
- Build a Logistic Regression-based classifier to predict loan approval using applicant and loan-specific features. This interpretable model aids lending institutions in making smarter, faster, and unbiased decisions.
Course Instructors
Prof. Mukesh Rao
Senior Faculty, Academics, Great Learning
Prof. Mukesh Rao
Senior Faculty, Academics, Great Learning
Key highlights of our pro courses
Self-paced learning for your convenience
Comprehensive short courses with industry-relevant curriculum
Learn foundation to intermediate level skills
Hands-on guided projects and interactive coding exercises
Get recognised with a certificate of completion
AI support for interactive learning & mock interviews
Earn a certificate and verify your skills
Stand out to recruiters
Share on professional channels
Globally recognised
Land your dream job
Application-driven learning to
Become a Skilled Professional with Pro Courses
Gain work-ready skills with guided projects, top faculty and AI tools, all at an affordable price.
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