Python for AI Roadmap

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Python for AI Roadmap by Mind Map: Python for AI Roadmap

1. Hi, I'm Mahesh, your instructor for this course. I am passionate about Python, AI, and helping you navigate the exciting world of machine learning!

2. Playlist

2.1. Week 1: Python Basics and Setup

2.1.1. Introduction to Python for AI

2.1.2. Evolution of programming Language

2.1.3. Installation of python and setting up environment development.

2.1.4. Syntax, variables and Introduction to Datatypes.

2.1.5. Conditional statements and loops (if-else, for, while).

2.1.6. Functions, lambda functions, and modules.

2.2. Week 2: Data Structures and File Handling

2.2.1. File handling: Reading, writing, and working with CSV/JSON files.

2.2.2. Lists, tuples, dictionaries, and sets.

2.2.3. List comprehensions and dictionary comprehensions.

2.2.4. Exception handling and debugging.

2.3. Week 3: Python for Data Manipulation

2.3.1. Grouping, merging, and aggregating datasets.

2.3.2. Introduction to Pandas: DataFrames, Series, and indexing.

2.3.3. NumPy: Arrays, slicing, and broadcasting.

2.3.4. Data cleaning and preprocessing techniques.

2.4. Week 4: Data Visualization and Exploratory Data Analysis (EDA)

2.4.1. Matplotlib: Line plots, bar charts, and histograms.

2.4.2. Seaborn: Heatmaps, scatter plots, and pair plots.

2.4.3. Creating interactive visualizations using Plotly.

2.4.4. Conducting EDA to identify patterns and trends.

2.5. Week 5: Python for Machine Learning Basics

2.5.1. Introduction to Machine Learning

2.5.2. Types of Machine Learning

2.5.3. Regression and Classification Algorithms

2.5.4. EDA project with Real time use case

2.5.5. One End to End Machine learning project

3. Reading List

3.1. "Python Crash Course" by Eric Matthes.

3.2. "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron.

3.3. Python for Data Analysis" by Wes McKinney.

3.4. Official Python, NumPy, Pandas, and scikit-learn documentation.

4. Software Requirements

4.1. Python (3.8 or later).

4.2. Editor: Vs code , Google colab, or Jupyter notebook.

4.3. Required Libraries: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, flask , Streamlit.