- Home
- Artificial Intelligence, Machine Learning and Data Science
- Internet of Things with Python
- Advanced Python Scripting
- Linux Kernel Internals and Advanced Programming
- Embedded Linux System Development
- Linux Device Drivers and Kernel Programming
- Unix/Linux Shell Scripting with sed and awk
- Python - Django Web Framework
- Advanced Python for Network Engineers
- About
- Contact Us
- Blog
Artificial INTELLIGENCE, Machine Learning and Data Science Workshop with Python
Course duration: 5 full days
Course Outline:
Module 1: Python Basics
- Python Introduction
- Using the Interpreter
- Python Scripting
- Working with Variables in Python
- Numeric Operations in Python
- Python Compound Statements
- Python String Types
- Python's Tuples
- Python's Lists
- Python Dictionaries
- Creating Python Functions
- Classes and Objects
- Modules and Packages
Module 2: Data Science, Machine Learning & Deep Learning
- Introduction to Artificial Intelligence and Machine Learning
- Applications of Machine Learning
- Machine learning examples
- Setting up Anaconda & Python Notebooks.
- Working on notebooks for Data Science
- Intro to Data Science
- Working with arrays & matrix (using NumPy)
-
Working with Series & DataFrames
- Accessing/Importing and Exporting Data
- Data Manipulation - Cleaning-Munging
-
Data Visualization
- Univariate Analysis (Distribution of data & Graphical Analysis)
- Bivariate Analysis( Cross Tabs, Distributions & Relationships, Graphical Analysis}
-
Creating Graphs:
- Bar plot
- Pie plot
- Count plot
- Line chart
- Histogram
- Boxplot
- Scatter
- Density
- Violine Plot
- Swarmplot
- Distplot
- Pair plot
- Heatmap
-
Statistics Overview
- Concepts of linear algebra
- Euclidean and Non-Euclidean geometry
- Introduction to Calculus
- Probability and statistics
- Distributions, CDF, PDF
- Mean, Median, Mode
- Standard Deviation, quartiles, percentiles
- Variable Relationships & Estimation
- Hypothesis Testing
- Predictive Models
- Machine Learning
-
Supervised Learning
- Linear Regression, Multiple Regression, Polynomial Regression
- Logistic Regression
- Decision Trees
- Ensemble Learning - Bagging, Boosting, Ada Boost, XGBoost
- Random Forest
- Naive Bayes
-
Kernel Learning
- Support Vector Machines
- Principal Component Analysis
- Ridge Regression
- Spectral Clustering
- Time Series Forecasting - ARIMA
-
Unsupervised Learning: Segmentation
- What is the segmentation & the Role of ML in Segmentation?
- Clustering algorithms
- Concept of Distance and related math background
- K-Means Clustering
- Hierarchical Clustering
-
Dealing with Real-World Data
- Experimental Design
- Model Evaluation, Improvements & Performance Metrics
- Data Split Practices
- Cross-Validation
- K-Fold Validation
- Confusion Matrix
- ROC Curves
- Mean Absolute/Square Errors & R-Square
- Ensemble Learning & Model Stacking
-
Natural Language Processing
- What is NLP & How to solve NLP problems?
- NLP Feature Engineering & Modelling
- How to process any raw data file.
- Build models for solving practical real-world problems.
-
Deep Learning - Artificial Neural Networks (ANN)
- The motivation for Neural Networks and Its Applications
- Perceptron and Single Layer Neural Network, and Hand Calculations
- Learning In a Multi-Layered Neural Net: BackPropagation and Conjugant Gradient Techniques
- Introducing & Using Tensorflow
- Neural Networks for Regression
- Neural Networks for Classification
- Interpretation of Outputs and Fine-tune the models with hyper-parameters
-
Validating ANN models
-
End to End ML Implementation and Use Case specific discussions.