Acquire high-value and in-demand skills through a combination of reading and hands-on practice, dedicating just a few minutes each day.
In this course :-
Basic To Advance
Get your hands dirty with projects
Practice questions after each tutorial
Each and every topic covered
Complete Course Plan :-
This is complete roadmap to learn machine learning from beginner to advance.
Module 1: Python Basics for Machine Learning:
1.1. Grasping Python Fundamentals
1.2. Exploring Python Data Types: int, float, string, complex, boolean
1.3. Special Data Types in Python: List, Tuple, Set, Dictionary
1.4. Operators and Control Statements in Python
1.5. Conditional Statements: if-else in Python
1.6. Iterative Statements: For Loop & While Loop
1.7. Functions in Python
Module 2: Python Libraries Tutorial for Machine Learning:
2.1. Numpy Essentials for ML
2.2. Pandas for Data Manipulation in ML
2.3. Matplotlib & Seaborn for Data Visualization
2.4. Sklearn for Machine Learning in Python
Module 3: Data Collection & Processing:
3.1. Sourcing and Collecting Data
3.2. Importing Data Using Kaggle API
3.3. Handling Missing Values
3.4. Data Standardization Techniques
Module 4: Math Basics for Machine Learning:
4.1. Essential Linear Algebra
4.2. Basic Calculus for ML
4.3. Statistics in Machine Learning
4.4. Probability Concepts
Module 5: Training the Machine Learning Models:
5.1. Understanding Machine Learning Models
5.2. Model Selection and Training
5.3. Techniques for Model Optimization
5.4. Evaluating Model Performance
Module 6: Classification Models in Machine Learning: 6.1-6.6. Understanding and Building from Scratch: Logistic Regression, SVM, Decision Trees, Random Forest, Naive Bayes, K-Nearest Neighbors
Module 7: Regression Models in Machine Learning: 7.1-7.6. Theory and Building from Scratch: Linear Regression, Lasso Regression, Logistic Regression, SVM Regression, Decision Tree Regression, Random Forest Regression
Module 8: Clustering Models in Machine Learning: 8.1-8.2. Exploring and Building from Scratch: K-Means Clustering, Hierarchical Clustering
Module 9: Association Models in Machine Learning: 9.1-9.2. Understanding and Building from Scratch: Apriori, Eclat
Module 10: Machine Learning Projects with Python: Projects covering Face Recognition, SONAR Rock vs Mine Prediction, Diabetes Prediction, House Price Prediction, Fake News Prediction, and Loan Status Prediction.