đ Course Overview
Machine Learning with Python āĻšāϞ⧠āĻāĻāĻāĻŋ 45 Hours Comprehensive & Project-Based Course, āϝā§āĻāĻžāύ⧠āĻāĻĒāύāĻŋ Python Programming āĻŦā§āϝāĻŦāĻšāĻžāϰ āĻāϰ⧠Machine Learning Models āϤā§āϰāĻŋ, Train, Evaluate āĻāĻŦāĻ Deploy āĻāϰāĻžāϰ Practical Skills āĻ āϰā§āĻāύ āĻāϰāĻŦā§āύāĨ¤
āĻŦāϰā§āϤāĻŽāĻžāύ⧠Machine Learning (ML) āĻšāϞ⧠Artificial Intelligence-āĻāϰ āĻ āύā§āϝāϤāĻŽ āĻā§āϰā§āϤā§āĻŦāĻĒā§āϰā§āĻŖ āĻļāĻžāĻāĻž, āϝāĻž Data Analysis, Prediction, Recommendation Systems, Fraud Detection, Healthcare, Finance āĻāĻŦāĻ Business Intelligence-āĻ āĻŦā§āϝāĻžāĻĒāĻāĻāĻžāĻŦā§ āĻŦā§āϝāĻŦāĻšā§āϤ āĻšāĻā§āĻā§āĨ¤ āĻāĻ āĻā§āϰā§āϏ⧠āĻāĻĒāύāĻŋ Python, NumPy, Pandas, Matplotlib, Scikit-learn āĻāĻŦāĻ āĻ āύā§āϝāĻžāύā§āϝ āĻāύāĻĒā§āϰāĻŋāϝāĻŧ ML Libraries āĻŦā§āϝāĻŦāĻšāĻžāϰ āĻāϰ⧠Real-world Projects-āĻāϰ āĻŽāĻžāϧā§āϝāĻŽā§ Machine Learning āĻļāĻŋāĻāĻŦā§āύāĨ¤
āĻā§āϰā§āϏāĻāĻŋ āĻāĻŽāύāĻāĻžāĻŦā§ āĻĄāĻŋāĻāĻžāĻāύ āĻāϰāĻž āĻšāϝāĻŧā§āĻā§ āϝāĻžāϤ⧠āĻāĻĒāύāĻŋ Theory-āĻāϰ āĻĒāĻžāĻļāĻžāĻĒāĻžāĻļāĻŋ Hands-on Practice-āĻāϰ āĻŽāĻžāϧā§āϝāĻŽā§ Industry-ready Skills āĻ āϰā§āĻāύ āĻāϰāϤ⧠āĻĒāĻžāϰā§āύāĨ¤
Basic Python Programming Knowledge Required.
đ Course Information
- Course Name: Machine Learning with Python
- Duration: 45 Hours
- Level: Beginner to Intermediate
- Language: Bangla + English
- Class Type: Online / Offline
- Prerequisite: Basic Python Programming & Basic Mathematics
- Hands-on Practice: Yes
- Real-world Projects: Yes
- Certificate: Yes
đ¯ What You’ll Learn
āĻāĻ āĻā§āϰā§āϏ⧠āĻāĻĒāύāĻŋ āĻļāĻŋāĻāĻŦā§āύâ
â Python for Machine Learning
â Data Collection & Data Preprocessing
â NumPy & Pandas
â Data Visualization with Matplotlib
â Introduction to Machine Learning
â Supervised Learning
â Unsupervised Learning
â Regression Algorithms
â Classification Algorithms
â Clustering Techniques
â Model Training & Evaluation
â Feature Engineering
â Hyperparameter Tuning
â Scikit-learn Library
â Real-world Machine Learning Projects
â Model Deployment Basics
đ Course Modules
Module 1 â Python for Data Science
- Python Refresher
- NumPy Fundamentals
- Pandas for Data Analysis
- Data Cleaning
- Data Manipulation
Module 2 â Data Visualization
- Matplotlib
- Data Visualization Techniques
- Exploratory Data Analysis (EDA)
- Understanding Patterns in Data
Module 3 â Introduction to Machine Learning
- What is Machine Learning?
- Types of Machine Learning
- ML Workflow
- Preparing Datasets
Module 4 â Supervised Learning
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- K-Nearest Neighbors (KNN)
- Support Vector Machine (SVM)
Module 5 â Unsupervised Learning
- K-Means Clustering
- Hierarchical Clustering
- Dimensionality Reduction
- Principal Component Analysis (PCA)
Module 6 â Model Evaluation
- Train/Test Split
- Cross Validation
- Accuracy, Precision & Recall
- Confusion Matrix
- Model Optimization
Module 7 â Feature Engineering & Deployment
- Feature Selection
- Feature Scaling
- Hyperparameter Tuning
- Saving Models
- Model Deployment Basics
Module 8 â Real-world Projects
- House Price Prediction
- Customer Churn Prediction
- Sales Forecasting
- Spam Email Detection
- Student Performance Prediction
- Final Machine Learning Project
đ¨âđģ Who Should Join?
āĻāĻ āĻā§āϰā§āϏāĻāĻŋ āĻŦāĻŋāĻļā§āώāĻāĻžāĻŦā§ āĻāĻĒāϝā§āĻā§â
- Students
- Python Developers
- Data Analysts
- Aspiring Data Scientists
- AI & ML Enthusiasts
- Software Engineers
- Researchers
- IT Professionals
- Freelancers
- Anyone interested in Machine Learning
đ After Completing This Course
āĻāĻ āĻā§āϰā§āϏ āĻļā§āώ āĻāϰāĻžāϰ āĻĒāϰ āĻāĻĒāύāĻŋ āĻĒāĻžāϰāĻŦā§āύâ
- Python āĻŦā§āϝāĻŦāĻšāĻžāϰ āĻāϰ⧠Machine Learning Models āϤā§āϰāĻŋ āĻāϰāϤā§āĨ¤
- Real-world Dataset Analyze āĻ Preprocess āĻāϰāϤā§āĨ¤
- Regression, Classification āĻāĻŦāĻ Clustering Algorithms āĻŦā§āϝāĻŦāĻšāĻžāϰ āĻāϰāϤā§āĨ¤
- Data Visualization āĻ Feature Engineering āĻāϰāϤā§āĨ¤
- Machine Learning Models Evaluate āĻ Optimize āĻāϰāϤā§āĨ¤
- Scikit-learn āĻŦā§āϝāĻŦāĻšāĻžāϰ āĻāϰ⧠End-to-End ML Projects āϤā§āϰāĻŋ āĻāϰāϤā§āĨ¤
- Basic Model Deployment āϏāĻŽā§āĻĒāϰā§āĻā§ āϧāĻžāϰāĻŖāĻž āĻ āϰā§āĻāύ āĻāϰāϤā§āĨ¤
- Data Science āĻ AI Career-āĻāϰ āĻāύā§āϝ Strong Foundation āϤā§āϰāĻŋ āĻāϰāϤā§āĨ¤
đŧ Practical Projects
- House Price Prediction Model
- Sales Forecasting System
- Customer Churn Prediction
- Spam Email Classifier
- Student Result Prediction
- Customer Segmentation
- Recommendation System (Basic)
- Final End-to-End Machine Learning Project