Machine Learning with Python Certified Course

 Course Description:

This Machine Learning with Python course dives into the basics of machine learning using Python, an approachable and well-known programming language. You’ll learn about supervised vs. unsupervised learning, look into how statistical modelling relates to machine learning, and do a comparison of each.

Key Features of Course Divine:

• Collaboration with E‑Cell IIT Tirupati
• 1:1 Online Mentorship Platform
• Credit-Based Certification
• Live Classes Led by Industry Experts
• Live, Real-World Projects
• 100% Placement Support
• Potential Interview Training
• Resume-Building Activities

Career Opportunities After Machine Learning with Python:

• Machine Learning Engineer
• Data Scientist
• Data Analyst
• AI/Deep Learning Engineer
• Business Intelligence Developer
• NLP Engineer
• Computer Vision Engineer
• Robotics Engineer

Essential Skills you will Develop Machine Learning with Python:

• Python Programming for Data Science
• Supervised & Unsupervised Learning
• Model Evaluation & Optimization
• Deep Learning Basics
• Natural Language Processing (NLP) 

Tools Covered:

• Python Programming
• Core language for implementing ML algorithms
• Libraries and syntax for data manipulation
• NumPy
• Numerical computations
• Handling arrays and matrices
• Pandas
• Data manipulation and analysis
• DataFrames for structured data

Syllabus:

Module 1: Introduction to Machine Learning What is Machine Learning? Types of Machine Learning: Supervised, Unsupervised, Reinforcement Real-world Applications Overview of ML workflow.

Module 2: Python for Machine Learning Python basics recap Libraries: Number, Pandas, Seaborn Data handling and visualization.

Module 3: Data Preprocessing Data Cleaning & Transformation Handling missing values & outliers Feature scaling: Normalization & Standardization Feature engineering & encoding categorical variables.

Module 4: Supervised Learning Algorithms Linear Regression
Logistic Regression K-Nearest Neighbors (KNN) Support Vector Machines (SVM)
Decision Trees & Random Forest.

Module 5: Unsupervised Learning Algorithms Clustering: K-Means, Hierarchical
Dimensionality Reduction: PCA, t-SNE Anomaly Detection.

Module 6: Model Evaluation and Tuning Train-Test Split, Cross-Validation Metrics: Accuracy, Precision, Recall, F1 Score, AUC-ROC.

Module 7: Ensemble Learning Bagging & Boosting Ada Boost, Gradient Boosting, XG Boost Stacking models.

Module 8: Deep Learning Basics with Tens or Flow & Neural Network fundamentals
Introduction to Tens or Flow &  Building basic ANN models.

Module 9: Real-Time Projects Predictive analytics (e.g., housing prices, stock trends) Classification project (e.g., email spam detection) Clustering project (e.g., customer segmentation).

Module 10: Deployment and Final Assessment Model deployment using Introduction to ML Ops Final certification project and assessment.

Industry Projects:

• Customer Churn Prediction
• Sales Forecasting
• Credit Card Fraud Detection
• Movie Recommendation System
• Disease Prediction

Who is this program for?

• Students and Graduates
• Working Professionals
• Software Developers
• Researchers and Academicians

How To Apply:

Mobile: 9100348679

Email: coursedivine@gmail.com