AITutorials

  🟢 Level 1: Beginner (The Foundation) Goal: Understand how TensorFlow handles data and build your first "Hello World" models. Introduction to Tensors: What are Tensors? (Scalars, Vectors, Matrices). Tensor types and shapes ( tf.constant , tf.Variable ). Basic operations: Addition, matrix multiplication, and reshaping. The Keras Sequential API: Building simple Dense (Linear) layers. Compiling models: Selection of Optimizers (Adam, SGD) and Loss Functions (MSE, Binary Cross-Entropy). Data Preprocessing Basics: Using tf.data.Dataset for efficient data pipelines. Normalizing and scaling numerical data. Hands-on Project: Predicting house prices using a Simple Linear Regression model. 🟡 Level 2: Intermediate (Computer Vision & NLP) Goal: Build models that can see and read. Computer Vision with CNNs: Convolutional layers, Pooling, and Dropout. Image Augmentation: Flipping, rotating, and zooming to prevent overfitting. Transfer Learning: Using pre-trained models like Mob...

📊 Complete Matplotlib Master Tutorial (Beginner → Advanced) 🧠 1️⃣ What is Matplotlib? Matplotlib is the core Python visualization library used for: Data analysis Machine learning evaluation Research plots Business dashboards Financial charts Main module: import matplotlib.pyplot as plt 🟢 2️⃣ Basic Plot (Foundation) import matplotlib.pyplot as plt x = [1,2,3,4,5] y = [10,20,25,30,40] plt.plot(x, y) plt.title("Basic Line Plot") plt.xlabel("X Axis") plt.ylabel("Y Axis") plt.show() When to use? Trends Time series ML loss curves 🔵 3️⃣ Line Customization (Professional Style) plt.plot(x, y, color="blue", linestyle="--", marker="o", linewidth=2) plt.grid(True) plt.show() Important parameters: color linestyle marker linewidth alpha (transparency) 🟡 4️⃣ Bar Chart (Category Comparison) names = ["Hari", "Rahul", "Amit"] salary = [50000, 60000, 55000] plt.bar(names...

📌 1️⃣ What is Pandas? Pandas is a Python library used for: Data Analysis Data Cleaning Data Manipulation Handling CSV, Excel, SQL data It works mainly with: Series → 1D data DataFrame → 2D tabular data (like Excel table) 📌 2️⃣ Installation pip install pandas import pandas as pd 📌 3️⃣ Pandas Core Data Structures 🔹 3.1 Series (1D) import pandas as pd data = [10, 20, 30, 40] s = pd.Series(data) print(s) With custom index: s = pd.Series(data, index=["a", "b", "c", "d"]) 🔹 3.2 DataFrame (2D Table) data = { "Name": ["Hari", "Rahul", "Amit"], "Age": [25, 30, 35], "Salary": [50000, 60000, 70000] } df = pd.DataFrame(data) print(df) 📌 4️⃣ Reading Data 🔹 CSV df = pd.read_csv("data.csv") 🔹 Excel df = pd.read_excel("data.xlsx") 🔹 JSON df = pd.read_json("data.json") 📌 5️⃣ Basic Data Inspection df.head() # First 5 rows df.tail() # Last...

🐍 Python Syllabus (Beginner → Advanced) 1. Beginner Level Introduction What is Python, installation, IDEs Running Python scripts Basics Syntax, keywords, indentation Variables & data types Input/Output Operators (arithmetic, relational, logical) Control Flow Conditional statements ( if , elif , else ) Loops ( for , while ) Loop control ( break , continue , pass ) Collections Lists, tuples, sets, dictionaries Indexing, slicing, methods 2. Intermediate Level Functions Defining functions, arguments, return values Default & keyword arguments Lambda functions Modules & Packages Importing modules Standard libraries overview Error Handling Exceptions ( try , except , finally ) Custom exceptions File Handling Reading/writing files Working with CSV, JSON Object-Oriented Programming Classes & objects Constructors, methods Inheritance, polymorphism, encapsulation 3. Advanced Level Iterators & Generators iter() , n...