5 Days Machine Learning Specialization

Day 1 Topic 1 - Python Fundamental

Topic 1.1 Get Started on Python

• Overview of Python
• Set Python
• Code Your First Python Script

Topic 1.2: Data Types

• Number
• String
• List
• Tuple
• Dictionary
• Set

Topic 1.3 Operators

• Arithmetic Operators
• Compound Operators
• Comparison Operators
• Membership Operators
• Logical Operators

Topic 1.4 Control Structure, Loop and Comprehension

• Conditional
• Loop
• Iterating Over Multiple Sequences
• Comprehension

Topic 1.5 Function

• Function Syntax
• Return Values
• Default Arguments
• Variable Arguments
• Lambda, Map, Filter

Topic 1.6 Modules & Packages

• Import Modules and Packages
• Python Standard Packages
• Third Party Packages

Day 2 Topic 2 - Data Analytics and Visualization with Python

Topic 2.1 Data Preparation

• Data Analytics with Pandas
• Pandas DataFrame and Series
• Import and Export Data
• Filter and Slice Data
• Clean Data

Topic 2.2 Data Transformation

• Join Data
• Transform Data
• Aggregate Data

Topic 2.3 Data Visualization

• Data Visualization with Matplotlib and Seaborn
• Visualize Statistical Relationships with Scatter Plot
• Visualize Categorical Data with Bar Plot
• Visualize Correlation with Pair Plot and Heatmap
• Visualize Linear Relationships with Regression

Topic 2.4 Data Analysis

• Statistical Data Analysis
• Time Series Analysis

Topic 2.5 Advanced Data Analytics

• Data Piping
• Groupby and Apply Custom Functions
• Linear Regression

Day 3 Topic 3 Machine Learning with Scikit Learn

Topic 3.1 Overview of Machine Learning and Scikit Learn

• Introduction to Machine Learning
• Supervised vs Unsupervised Learnings
• Machine Learning Applications and Case Studies
• What is Scikit Learn
• Installing Scikit-Learn

Topic 3.2 Classification

• What is Classification
• Classification Algorithms
• Classification Workflow
• Confusion Matrix
• Binary Classification Metrics
• ROC and AUC

Topic 3.3 Regression

• What is Regression?
• Regression Algorithms
• Regression Workflow
• Regression Metrics
• Overfitting and Regularizations

Topic 3.4 Clustering

• What is Clustering
• K-Means Clustering
• Silhouette Analysis
• Dendrogram and Hierarchical Clustering

Topic 3.5 Principal Component Analysis

• Curse of Dimensionality Issue
• What is Principal Component Analysis (PCA)
• Feature Reduction with PCA

Day 4 Topic 4 Basic Neural Network with Tensorflow

Topic 4.1 Introduction to Deep Learning

• Machine Learning vs Deep Learning
• Deep Learning Methodology
• Overview of Tensorflow Keras
• Install and Run Tensorflow Keras
• Basic Tensorflow Keras Operations

Topic 4.2 Neural Network for Regression

• What is Neural Network (NN)?
• Loss Function and Optimizer
• Build a Neural Network Model for Regression

Topic 4.3 Neural Network for Classification

• One Hot Encoding and SoftMax
• Cross Entropy Loss Function
• Build a Neural Network Model for Classification

Day 5 Topic 5 Advanced Neural Networks with Tensorflow

Topic 5.1 Convolutional Neural Network (CNN)

• Introduction to Convolutional Neural Network?
• ImageDataGenerator
• Image Classification Model with CNN
• Data Augmentation and Dropout

Topic 5.2 Transfer Learning

• Introduction to Transfer Learning
• Applications of Pre-Trained Models
• Fine Tuning Pre-Trained Models

Topic 5.3 Recurrent Neural Network (RNN)

• Introduction to Recurrent Neural Network (RNN)
• LSTM and GRU
• Build a RNN Model for Time Series Forecasting
• Build a RNN Model for Sentiment Analysis