Python

Level 4

Students can have an insight on how the programming world works and get familiar with the concepts of coding using Python language. With the fundamental knowledge and concept learned in Beginners and Intermediate Level, student can explore to real programming idea on how the application and various system built. For Advanced level, student will unlock the power of Python on creation of user-friendly graphical interfaces in this hands-on course, with real-world IT industry questions. Whether you’re a beginner or an intermediate Python enthusiast, this course is designed to elevate your programming / coding skills to the next level.

Objectives

  • Study different modules for Machine Learning and Artificial Intelligence (AI).
  • Learn to differentiate various framework in Python.
  • Learn to develop basic Machine Learning using trained model and interpret the output accordingly.
  • Gain foundational knowledge of Artificial Intelligence (AI).

Course Info

Platform used:
Python Editor

Lessons:
24 lessons

Duration:
12 months, 24 weeks

Skills:

  • Understand Data Analysis and Machine Learning using various modules
  • Learn how to apply framework into Python’s project
  • Interpret Machine Learning framework and AI project’s output accordingly

Lesson Plan – Expert (Basics of Artificial Intelligence)

*Each Lesson might take 2 classes to complete. Estimated Duration: 36 weeks + Final Project (TBD)

No Lesson Description / Skills
1 Introduction to Data Analysis
  • Overview of Pandas and NumPy for data manipulation.
  • Understand Pandas library in Data Analytics
2 Data Manipulation with Pandas
  • DataFrames, Series, Indexing, Filtering, and Aggregation
3 Basic Statistics with Pandas Library
  • Introduction to statistics
  • Different of mean, standard deviation
  • Understand Aggregate functions with Dataframe
4 Data Handling and Pre processing
  • Data Cleansing techniques
  • Method to handle missing values
5 Data Handling and Pre processing
  • Basic Plotting with Pandas’s Library
  • Understand usage of different type of charts (pie, bar, line, histogram)
6 Visualization with Matplotlib
  • What is Matplotlib?
  • Understand sub-plot in Matplotlib
  • Application of data visualization
7 Advanced Visualization Method
  • Creating advanced plots with Seaborn
  • Visualize different data sets
8 Exploratory Data Analysis (EDA)
  • Data Summary and Pattern Identification
  • Using Visualization to conclude insights from Data
9 Real World Data Analysis
  • Application of Data Analysis Technique
  • Using e-Commerce as sample Data Sets for Analysis and Decision Making
10 Introduction to Machine Learning
  • Understanding supervised and unsupervised learning concepts.
  • Introduction of various machine learning methodologies
11 Regression Models
  • Introduction to regression testing
  • Evaluating linear and logistic regression models
12 Classification Models
  • Understand various Classifications Models
  • Decision trees, k-Nearest Neighbors, and Support vector machines in basic
13 Introduction to Clustering of Data
  • K-Means and Hierarchical Clustering
  • Learn how to interpret the clustered datasets with example
14 Introduction to Neural Networks
  • Introduction of Tensorflow / Keras
  • Build basic concepts of neural networks
15 Application of Neural Networks
  • Hands-on Creation of simple neural network image classification
  • Digit Identifier using Tensorflow
16 Introduction to Artificial Intelligence
  • Overview of AI and understand AI applications in daily life
17 AI with OpenAI API
  • Building a chatbot using OpenAI’s GPT model
  • Understand the concept of API and API key
18 Working on Real Datasets
  • Import datasets from online repositories (Kaggle)
  • Applying Machine Learning methodologies on real world datasets
19-24 Final Project – AI-powered data analysis and Prediction System
  • Students will build a data analysis and prediction system for a specific domain (e.g., stock market prediction, sales forecasting, or customer sentiment analysis).
  • Combine Pandas, Matplotlib, Scikit-Learn, Tensorflow modules

 

Estimated Duration: 36 weeks + 8 weeks for final project

*For final project, students will be provided with the project requirements and objectives to develop a real world application with integration of Machine Learning framework and Artificial Intelligence technology.

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