Introduction to Machine Learning
( 25 Modules )

Module #1
Introduction to Machine Learning
Overview of Machine Learning, its importance, and applications

Module #2
Types of Machine Learning
Supervised, Unsupervised, and Reinforcement Learning explained

Module #3
Machine Learning Workflow
Understanding the ML workflow from problem definition to deployment

Module #4
Python Basics for Machine Learning
Introduction to Python and its essential libraries for ML

Module #5
Data Preprocessing
Handling missing values, data normalization, and feature scaling

Module #6
Data Visualization
Visualizing data using Matplotlib, Seaborn, and Plotly

Module #7
Supervised Learning - Introduction
Understanding Supervised Learning and its applications

Module #8
Linear Regression
Simple and Multiple Linear Regression explained

Module #9
Logistic Regression
Binary Classification using Logistic Regression

Module #10
Decision Trees
Introduction to Decision Trees and their applications

Module #11
Random Forests
Ensemble Learning with Random Forests

Module #12
Unsupervised Learning - Introduction
Understanding Unsupervised Learning and its applications

Module #13
K-Means Clustering
K-Means Clustering algorithm explained

Module #14
Hierarchical Clustering
Hierarchical Clustering algorithm explained

Module #15
Principal Component Analysis (PCA)
Dimensionality Reduction using PCA

Module #16
Support Vector Machines (SVMs)
SVMs for Classification and Regression

Module #17
Model Evaluation Metrics
Understanding accuracy, precision, recall, F1-score, and more

Module #18
Model Selection and Hyperparameter Tuning
Selecting the best model and tuning hyperparameters

Module #19
Neural Networks - Introduction
Introduction to Artificial Neural Networks

Module #20
Deep Learning with Keras
Building Deep Learning models using Keras

Module #21
Natural Language Processing (NLP) with Python
Introduction to NLP using NLTK and spaCy

Module #22
Specialized Machine Learning Topics
Introduction to recommender systems, time series forecasting, and more

Module #23
Machine Learning Best Practices
Best practices for ML model development and deployment

Module #24
Real-World Applications of Machine Learning
Case studies and applications of ML in various industries

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Introduction to Machine Learning career

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Introduction to Machine Learning ( 25 Modules )

Introduction to Machine Learning
( 25 Modules )