After completing this course, learners will be able to apply python programming skills and ML alogithms to solve real-world problems and build AI/ML applications.\
Week 1: Machine Learning Basics:
1.1. Artificial Intelligence vs Machine Learning vs Deep Learning
1.2. Types of Machine Learning: Supervised, Unsupervised & Reinforcement Learning
1.3. Supervised Learning & its Types
1.4. Unsupervised Learning & its Types
1.5. Deep Learning – Basics
Week 2: Python Basics for Machine Learning:
2.1. Google Colaboratory for Python – Getting Systems Ready
2.2. Python Basics
2.3. Python Basic Data Types – int, float, string, complex, boolean
2.4. Python Special Data Types – List, Tuple, Set, Dictionary
2.5. Operators in Python
2.6. if else Statement in Python
2.7. Loops in Python – For Loop & While Loop
2.8. Functions in Python
Week 3: Python Libraries Tutorial for Machine Learning:
3.1. Complete Numpy Tutorial for ML
3.2. Complete Pandas Tutorial for ML
3.3. Complete Matplotlib & Seaborn Tutorial for ML
3.4. Complete Sklearn Tutorial for ML
Week 4: Data Collection & Processing:
4.1. Where to collect Data & How to collect Data
4.2. Importing Data through Kaggle API
4.3. Handling Missing Values
4.4. Data Standardization
Week 5: Math Basics for Machine Learning:
5.1. Linear Algebra
5.2. Calculus
5.3. Statistics
5.4. Probability
Week 6: Training the Machine Learning Models:
6.1. What is a Machine Learning Model
6.2. How to select a model for training
6.3. Model Optimization Techniques
6.4. Model Evaluation
Week 7. Classification Models in Machine Learning:
7.1.1. Logistic Regression – Theory & Math
7.1.2. Logistic Regression – Building from Scratch
7.2.1. Support Vector Machines (SVM) – Theory & Math
7.2.2. Support Vector Machines (SVM) – Building from Scratch
7.3.1. Decision Tree Classification – Theory & Math
7.3.2. Decision Tree Classification – Building from Scratch
7.4.1. Random Forest Classification – Theory & Math
7.4.2. Random Forest Classification – Building from Scratch
7.5.1. Naive Bayes – Theory & Math
7.5.2. Naive Bayes – Building from Scratch
7.6.1. K-Nearest Neighbors – Theory & Basics
7.6.2. K-Nearest Neighbors – Building from Scratch
Week 8: Regression Models in Machine Learning:
8.1.1. Linear Regression – Theory & Basics
8.1.2. Linear Regression – Building from Scratch
8.2.1. Lasso Regression – Theory & Basics
8.2.2. Lasso Regression – Building from Scratch
8.3.1. Logistic Regression – Theory & Math
8.3.2. Logistic Regression – Building from Scratch
8.4.1. Support Vector Machine Regression – Theory & Math
8.4.2. Support Vector Machine Regression – Building from Scratch
8.5.1. Decision Tree Regression – Theory & Math
8.5.2. Decision Tree Regression – Building from Scratch
8.6.1. Random Forest Regression – Theory & Math
8.6.2. Random Forest Regression – Building from Scratch
Week 9: Clustering Models in Machine Learning
9.1.1. K-Means Clustering – Theory & math
9.1.2. K-Means Clustering – Building from Scratch
9.2.1. Hierarchical Clustering – Theory & Math
9.2.2. Hierarchical Clustering – Building from Scratch
Week 10: Association Models in Machine Learning:
10.1.1. Apriori – Theory & Basics
10.1.2. Apriori – Building from Scratch
10.2.1. Eclat – Theory & Math
10.2.2. Eclat – Building from Scratch
Week 11: Machine Learning Projects with Python:
Project 1: Face Recognition system
Project 2: SONAR Rock vs Mine Prediction
Project 3: Diabetes Prediction with Python
Project 4: House Price Prediction with Python
Project 5: Fake News Prediction with Python
Project 6: Loan Status Prediction with Python
Week 12: MLOPS Introduction
12.1.1. Anaconda & Streamlit Installation
12.1.2. Deploy ML model using Streamlit
12.1.3. Multiple Disease Prediction System
12.1.4. Public ML Web App on Streamlit Cloud
12.1.5. Public ML Web App on Heroku
12.1.6. Deploy ML model as API
12.1.7. Deploy ML model as Public API using Ngrok
12.1.8. Deploy ML model as Public API using Heroku
For Registration and info:Please feel free to contact at [email protected] or Cell:03225100623
Email : [email protected]
