Machine Learning with Python
( 24 Modules )

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

Module #2
Setting up Python for Machine Learning
Installing Python, essential libraries, and IDEs for Machine Learning

Module #3
Python Basics for Machine Learning
Review of Python fundamentals necessary for Machine Learning

Module #4
Data Preprocessing
Handling missing values, data normalization, feature scaling, and data transformation

Module #5
Visualizing Data
Introduction to data visualization using Matplotlib and Seaborn

Module #6
Introduction to scikit-learn
Overview of scikit-learn library and its features

Module #7
Supervised Learning - Linear Regression
Theory and implementation of Linear Regression using scikit-learn

Module #8
Supervised Learning - Logistic Regression
Theory and implementation of Logistic Regression using scikit-learn

Module #9
Supervised Learning - Decision Trees
Theory and implementation of Decision Trees using scikit-learn

Module #10
Supervised Learning - Random Forests
Theory and implementation of Random Forests using scikit-learn

Module #11
Unsupervised Learning - K-Means Clustering
Theory and implementation of K-Means Clustering using scikit-learn

Module #12
Unsupervised Learning - Hierarchical Clustering
Theory and implementation of Hierarchical Clustering using scikit-learn

Module #13
Unsupervised Learning - Principal Component Analysis (PCA)
Theory and implementation of PCA using scikit-learn

Module #14
Model Evaluation and Selection
Metrics for evaluating Machine Learning models and techniques for model selection

Module #15
Hyperparameter Tuning
Techniques for hyperparameter tuning using GridSearchCV and RandomizedSearchCV

Module #16
Introduction to Deep Learning
Overview of Deep Learning, neural networks, and Keras library

Module #17
Deep Learning - Convolutional Neural Networks (CNNs)
Theory and implementation of CNNs using Keras

Module #18
Deep Learning - Recurrent Neural Networks (RNNs)
Theory and implementation of RNNs using Keras

Module #19
Natural Language Processing (NLP)
Introduction to NLP, text preprocessing, and text classification using scikit-learn and NLTK

Module #20
Machine Learning with Big Data
Overview of big data, distributed computing, and Machine Learning using Spark MLlib

Module #21
Model Deployment
Techniques for deploying Machine Learning models using Flask and TensorFlow Serving

Module #22
Machine Learning Best Practices
Best practices for Machine Learning, including data cleaning, feature engineering, and model validation

Module #23
Machine Learning Project
Guided project to implement a Machine Learning model using Python

Module #24
Course Wrap-Up & Conclusion
Planning next steps in Machine Learning with Python career

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Machine Learning with Python ( 24 Modules )

Machine Learning with Python
( 24 Modules )