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Hand’s On Artificial Intelligence And Machine Learning Programming Training.
- Genuine Placement
- 40k Enrolled
- All levels
- English
Training Overview
| Python Training | Python Programming. |
|---|---|
| DATA | Data Analysis, Data Manipulation, Data Visualisation |
| Supervised Learning I | Decision Tree , Random Forest |
| Supervised Learning II | Nieve Bayes, SVM |
| GUI programming | Application Creation |
| Unsupervised Learning | K Means, Reinforcement Learning. |
Syllabus - In Detail
Course Description:
Today’s highly increasing competitiveness over the industry demands high quality and most consistent products with a competitive price. To address this challenge number of industries considering various new product designs and integrated manufacturing techniques in parallel with the use of automated devices.
Automation takes a step further mechanization that uses a particular machinery mechanism aided human operators for performing a task. Mechanization is the manual operation of a task using powered machinery that depends on human decision making.
One of the remarkable and influential moves for getting the solutions of above mentioned challenge is the industrial automation. Industrial Automation facilitates to increase the product quality, reliability and production rate while reducing production and design cost by adopting new, innovative and integrated technologies and services.
Course Objectives:
- Complete knowledge of AI & ML.
- Able to Program, Testing & Commissioning of Hardware.
- Able to Program, Test & Debug of Software.
- Able to develop algorithm in Deep Learning, Neural Network & Tenser Flow.
- Real-time Projects Execution.
Module 9: Developing Web Maps and Representing information using Plots
Topics:
- Use of Folium Library
- Use of Pandas Library
- Flow chart of Web Map application
- Developing Web Map using Folium and Pandas
- Reading information from Dataset and represent it using Plots
Module 10: Computer vision using OpenCV and Visualisation using Bokeh
Topics:
- Beautiful Soup Library
- Requests Library
- Scrap all hyperlinks from a webpage, using Beautiful Soup & Requests
- Plotting charts using Bokeh
- Plotting scatterplots using Bokeh
- Image Editing using OpenCV
- Face detection using OpenCV
- Motion Detection and Capturing Video
Machine Learning with Python
Module 1: Introduction to Data Science
Topics:
- What is Data Science?
- What does Data Science involve?
- Era of Data Science
- Business Intelligence vs Data Science
- Life cycle of Data Science
- Tools of Data Science
- Introduction to Python
Module 2: Data Extraction, Wrangling, Visualization
Topics:
- Data Analysis Pipeline
- What is Data Extraction
- Types of Data
- Raw and Processed Data
- Data Wrangling
- Exploratory Data Analysis
- Visualization of Data
Module 3: Introduction to Machine Learning with Python
Topics:
- Python Revision (numpy, Pandas, scikit learn, matplotlib)
- What is Machine Learning?
- Machine Learning Use-Cases
- Machine Learning Process Flow
- Machine Learning Categories
- Linear regression
- Gradient descent
The primary and overall objective of this course is to give a hands-on experience of AI ML Programming & Algorithm.
Module I: Introduction to Python
Topics:
- Overview of Python
- The Companies using Python
- Other applications in which Python is used
- Discuss Python Scripts on UNIX/Windows
- Variables
- Operands and Expressions
- Conditional Statements
- Loops
- Command Line Arguments
- Writing to the screen
Module 2: Sequences and File Operations
Topics:
- Python files I/O Functions
- Lists and related operations
- Tuples and related operations
- Strings and related operations
- Sets and related operations
- Dictionaries and related operations
Module 3: Deep Dive – Functions and OOPs
Topics:
- Functions
- Function Parameters
- Global variables
- Variable scope and Returning Values
- Lambda Functions
- Object Oriented Concepts
Module 4: Working with Modules and Handling Exceptions
Topics:
- Standard Libraries
- Modules Used in Python (OS, Sys, Date and Time etc.)
- The Import statement
- Module search path
- Package installation ways
- Errors and Exception Handling
- Handling multiple exceptions
Module 4: Supervised Learning – I
Topics:
- What is Classification and its use cases?
- What is Decision Tree?
- Algorithm for Decision Tree Induction
- Creating a Perfect Decision Tree
- Confusion Matrix
- What is Random Forest?
Module 5: Dimensionality Reduction
Topics:
- Introduction to Dimensionality
- Why Dimensionality Reduction
- PCA
- Factor Analysis
- Scaling dimensional model
- LDA
Module 6: Supervised Learning – II
Topics:
- What is Naïve Bayes?
- How Naïve Bayes works?
- Implementing Naïve Bayes Classifier
- What is Support Vector Machine?
- Illustrate how Support Vector Machine works?
- Hyperparameter Optimization
- Grid Search vs Random Search
- Implementation of Support Vector Machine for Classification
Module 7: Unsupervised Learning
Topics:
- What is Clustering & its Use Cases?
- What is K-means Clustering?
- How K-means algorithm works?
- How to do optimal clustering
- What is C-means Clustering?
- What is Hierarchical Clustering?
- How Hierarchical Clustering works?
Module 5: Introduction to NumPy& Pandas
Topics:
- NumPy – arrays
- Operations on arrays
- Indexing slicing and iterating
- Reading and writing arrays on files
- Pandas – data structures & index operations
- Reading and Writing data from Excel/CSV formats into Pandas
Module 6: Data visualization
Topics:
- matplotlib library
- Grids, axes, plots
- Markers, colours, fonts and styling
- Types of plots – bar graphs, pie charts, histograms
- Contour plots
Module 7: Data Manipulation
Topics:
- Basic Functionalities of a data object
- Merging of Data objects
- Concatenation of data objects
- Types of Joins on data objects
- Exploring a Dataset
- Analyzing a dataset
Module 8: GUI Programming
Topics:
- Ipywidgets package
- Numeric Widgets
- Boolean Widgets
- Selection Widgets
- String Widgets
- Date Picker
- Color Picker
- Container Widgets
- Creating a GUI Application
Self-paced Module: Network Programming and Multithreading
Topics:
- MySQL DB access
- Network programming
- Multithreading Hands-on/Demo
- Database Creation
- CRUD Operations
- Network Creation
- Multithreading
Module 8: Association Rules Mining and Recommendation Systems
Topics:
- What are Association Rules?
- Association Rule Parameters
- Calculating Association Rule Parameters
- Recommendation Engines
- How Recommendation Engines work?
- Collaborative Filtering
- Content Based Filtering
Module 9: Reinforcement Learning
Topics:
- What is Reinforcement Learning
- Why Reinforcement Learning
- Elements of Reinforcement Learning
- Exploration vs Exploitation dilemma
- Epsilon Greedy Algorithm
- Markov Decision Process (MDP)
- Q values and V values
- Q – Learning
- 𝛼 values
Module 10: Time Series Analysis
Topics:
- What is Time Series Analysis?
- Importance of TSA
- Components of TSA
- White Noise
- AR model
- MA model
- ARMA model
- ARIMA model
- Stationarity
- ACF & PACF
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