{"id":1956,"date":"2025-08-24T07:38:36","date_gmt":"2025-08-24T07:38:36","guid":{"rendered":"https:\/\/itxperts.co.in\/blog\/?p=1956"},"modified":"2025-08-30T09:52:28","modified_gmt":"2025-08-30T09:52:28","slug":"100-python-ai-programs-to-kickstart-your-career","status":"publish","type":"post","link":"https:\/\/itxperts.co.in\/blog\/100-python-ai-programs-to-kickstart-your-career\/","title":{"rendered":"100+ Python AI Programs to Kickstart Your Career in Artificial Intelligence"},"content":{"rendered":"\n
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1. Hello AI \u2013 Print “Hello AI World”<\/h1>\n\n\n\n
# Program 1: Hello AI World\nprint(\"Hello AI World\")\n<\/code><\/pre>\n\n\n\n
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2. Fibonacci Sequence Generator (Recursion vs Iteration)<\/h1>\n\n\n\n
# Recursive Fibonacci\ndef fib_recursive(n):\n    if n <= 1:\n        return n\n    return fib_recursive(n-1) + fib_recursive(n-2)\n\n# Iterative Fibonacci\ndef fib_iterative(n):\n    a, b = 0, 1\n    seq = []\n    for _ in range(n):\n        seq.append(a)\n        a, b = b, a + b\n    return seq\n\nprint(\"Recursive 5th Fibonacci:\", fib_recursive(5))\nprint(\"Iterative first 10 Fibonacci:\", fib_iterative(10))\n<\/code><\/pre>\n\n\n\n
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3. Tic-Tac-Toe with Minimax AI<\/h1>\n\n\n\n
# Simple Tic-Tac-Toe with AI using Minimax\nimport math\n\ndef print_board(board):\n    for row in [board[i*3:(i+1)*3] for i in range(3)]:\n        print('| ' + ' | '.join(row) + ' |')\n\ndef available_moves(board):\n    return [i for i, spot in enumerate(board) if spot == \" \"]\n\ndef winner(board):\n    win_cond = [(0,1,2),(3,4,5),(6,7,8),(0,3,6),(1,4,7),(2,5,8),(0,4,8),(2,4,6)]\n    for (x,y,z) in win_cond:\n        if board[x] == board[y] == board[z] != \" \":\n            return board[x]\n    return None\n\ndef minimax(board, depth, is_maximizing):\n    win = winner(board)\n    if win == \"O\": return 1\n    elif win == \"X\": return -1\n    elif \" \" not in board: return 0\n    \n    if is_maximizing:\n        best = -math.inf\n        for move in available_moves(board):\n            board[move] = \"O\"\n            score = minimax(board, depth+1, False)\n            board[move] = \" \"\n            best = max(best, score)\n        return best\n    else:\n        best = math.inf\n        for move in available_moves(board):\n            board[move] = \"X\"\n            score = minimax(board, depth+1, True)\n            board[move] = \" \"\n            best = min(best, score)\n        return best\n\ndef best_move(board):\n    best_score = -math.inf\n    move = None\n    for i in available_moves(board):\n        board[i] = \"O\"\n        score = minimax(board, 0, False)\n        board[i] = \" \"\n        if score > best_score:\n            best_score = score\n            move = i\n    return move\n\nboard = [\" \"]*9\nboard[0] = \"X\"  # Example: Human move\nai_move = best_move(board)\nboard[ai_move] = \"O\"\n\nprint_board(board)\n<\/code><\/pre>\n\n\n\n
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4. Rock-Paper-Scissors AI (Random + Rule-based)<\/h1>\n\n\n\n
import random\n\nchoices = [\"rock\", \"paper\", \"scissors\"]\n\ndef rps_ai(user_choice):\n    # Rule-based: beat user's last move\n    if user_choice == \"rock\":\n        return \"paper\"\n    elif user_choice == \"paper\":\n        return \"scissors\"\n    else:\n        return \"rock\"\n\nuser = random.choice(choices)\nai = rps_ai(user)\n\nprint(f\"User: {user} | AI: {ai}\")\n<\/code><\/pre>\n\n\n\n
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5. Number Guessing Game with AI Hints<\/h1>\n\n\n\n
import random\n\nnumber = random.randint(1, 50)\nguess = 0\nwhile guess != number:\n    guess = int(input(\"Guess a number (1-50): \"))\n    if guess < number:\n        print(\"AI Hint: Try higher!\")\n    elif guess > number:\n        print(\"AI Hint: Try lower!\")\nprint(\"\ud83c\udf89 Correct! The number was\", number)\n<\/code><\/pre>\n\n\n\n
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6. Simple Chatbot (If-Else)<\/h1>\n\n\n\n
def simple_chatbot(user_input):\n    if \"hi\" in user_input.lower():\n        return \"Hello! How can I help you?\"\n    elif \"weather\" in user_input.lower():\n        return \"It\u2019s always sunny in AI land \ud83d\ude0e\"\n    elif \"bye\" in user_input.lower():\n        return \"Goodbye! Have a great day!\"\n    else:\n        return \"I don't understand, but I'm learning!\"\n\nprint(simple_chatbot(\"hi\"))\nprint(simple_chatbot(\"what's the weather?\"))\n<\/code><\/pre>\n\n\n\n
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7. Magic 8-ball Predictor<\/h1>\n\n\n\n
import random\n\nresponses = [\n    \"Yes, definitely!\",\n    \"No, not really.\",\n    \"Maybe, try again later.\",\n    \"It looks promising.\",\n    \"I wouldn\u2019t count on it.\"\n]\n\ndef magic_8_ball():\n    return random.choice(responses)\n\nprint(\"\ud83c\udfb1 Magic 8-ball says:\", magic_8_ball())\n<\/code><\/pre>\n\n\n\n
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8. Spam vs Ham Classifier (Naive Bayes with sample data)<\/h1>\n\n\n\n
from sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.naive_bayes import MultinomialNB\n\n# Sample dataset\ntexts = [\"Win money now\", \"Cheap loans available\", \"Hello friend\", \"Let\u2019s catch up soon\"]\nlabels = [\"spam\", \"spam\", \"ham\", \"ham\"]\n\nvectorizer = CountVectorizer()\nX = vectorizer.fit_transform(texts)\n\nmodel = MultinomialNB()\nmodel.fit(X, labels)\n\ntest = [\"free money\", \"hi buddy\"]\nX_test = vectorizer.transform(test)\n\nprint(model.predict(X_test))\n<\/code><\/pre>\n\n\n\n
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9. Word Auto-completion (Dictionary-based)<\/h1>\n\n\n\n
dictionary = [\"apple\", \"application\", \"banana\", \"band\", \"bandwidth\"]\n\ndef autocomplete(prefix):\n    return [word for word in dictionary if word.startswith(prefix)]\n\nprint(autocomplete(\"app\"))\nprint(autocomplete(\"ban\"))\n<\/code><\/pre>\n\n\n\n
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10. Predicting Even\/Odd Numbers (Trivial AI)<\/h1>\n\n\n\n
def predict_even_odd(number):\n    return \"Even\" if number % 2 == 0 else \"Odd\"\n\nprint(\"5 is\", predict_even_odd(5))\nprint(\"10 is\", predict_even_odd(10))\n<\/code><\/pre>\n\n\n\n
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Machine Learning Basics Programs (15)<\/h2>\n\n\n\n
1. Linear Regression \u2013 Predict house prices<\/h3>\n\n\n\n
from sklearn.linear_model import LinearRegression\nimport numpy as np\n\nX = np.array([[1000], [1500], [2000], [2500], [3000]])  # size in sqft\ny = np.array([200000, 300000, 400000, 500000, 600000])  # price\n\nmodel = LinearRegression()\nmodel.fit(X, y)\n\nprint(\"Prediction for 2200 sqft:\", model.predict([[2200]])[0])\n<\/code><\/pre>\n\n\n\n
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2. Logistic Regression \u2013 Predict pass\/fail<\/h3>\n\n\n\n
from sklearn.linear_model import LogisticRegression\nimport numpy as np\n\nX = np.array([[35], [50], [65], [75], [90]])  # marks\ny = np.array([0, 0, 1, 1, 1])  # 0 = fail, 1 = pass\n\nmodel = LogisticRegression()\nmodel.fit(X, y)\n\nprint(\"Prediction for 55 marks:\", model.predict([[55]])[0])\n<\/code><\/pre>\n\n\n\n
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3. Decision Tree \u2013 Predict weather (play\/not play)<\/h3>\n\n\n\n
from sklearn.tree import DecisionTreeClassifier\n\nX = [[85, 85], [80, 90], [72, 95], [69, 70], [75, 80]]  # temp, humidity\ny = [\"No\", \"No\", \"Yes\", \"Yes\", \"Yes\"]\n\nmodel = DecisionTreeClassifier()\nmodel.fit(X, y)\n\nprint(\"Play decision for (72 temp, 80 humidity):\", model.predict([[72, 80]])[0])\n<\/code><\/pre>\n\n\n\n
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4. KNN \u2013 Classify fruits<\/h3>\n\n\n\n
from sklearn.neighbors import KNeighborsClassifier\n\nX = [[150, 7], [170, 7], [140, 6], [130, 6]]  # weight, size\ny = [\"Apple\", \"Apple\", \"Orange\", \"Orange\"]\n\nmodel = KNeighborsClassifier(n_neighbors=3)\nmodel.fit(X, y)\n\nprint(\"Prediction for fruit (160g, size 7):\", model.predict([[160, 7]])[0])\n<\/code><\/pre>\n\n\n\n
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5. Random Forest \u2013 Student performance prediction<\/h3>\n\n\n\n
from sklearn.ensemble import RandomForestClassifier\n\nX = [[50], [60], [70], [80], [90]]\ny = [\"Fail\", \"Pass\", \"Pass\", \"Pass\", \"Pass\"]\n\nmodel = RandomForestClassifier()\nmodel.fit(X, y)\n\nprint(\"Prediction for marks 65:\", model.predict([[65]])[0])\n<\/code><\/pre>\n\n\n\n
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6. SVM \u2013 Classify email spam\/ham<\/h3>\n\n\n\n
from sklearn import svm\n\nX = [[3], [1], [4], [5], [0]]  # word counts of \"free\"\ny = [\"Spam\", \"Ham\", \"Spam\", \"Spam\", \"Ham\"]\n\nmodel = svm.SVC()\nmodel.fit(X, y)\n\nprint(\"Prediction for email with 2 'free' words:\", model.predict([[2]])[0])\n<\/code><\/pre>\n\n\n\n
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7. K-Means Clustering \u2013 Customer segmentation<\/h3>\n\n\n\n
from sklearn.cluster import KMeans\nimport numpy as np\n\nX = np.array([[25, 40000], [30, 50000], [35, 60000], [40, 80000], [45, 90000]])\n\nkmeans = KMeans(n_clusters=2, n_init=10)\nkmeans.fit(X)\n\nprint(\"Cluster labels:\", kmeans.labels_)\n<\/code><\/pre>\n\n\n\n
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8. Hierarchical Clustering \u2013 Group animals by features<\/h3>\n\n\n\n
from scipy.cluster.hierarchy import linkage, dendrogram\nimport matplotlib.pyplot as plt\n\nX = [[1, 1], [1.5, 1.5], [5, 5], [6, 6]]\n\nZ = linkage(X, 'ward')\ndendrogram(Z)\nplt.show()\n<\/code><\/pre>\n\n\n\n
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9. Polynomial Regression \u2013 Predict salary growth<\/h3>\n\n\n\n
import numpy as np\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.linear_model import LinearRegression\n\nX = np.array([[1], [2], [3], [4], [5]])\ny = np.array([2000, 4000, 7000, 11000, 15000])\n\npoly = PolynomialFeatures(degree=2)\nX_poly = poly.fit_transform(X)\n\nmodel = LinearRegression()\nmodel.fit(X_poly, y)\n\nprint(\"Prediction for 6 years exp:\", model.predict(poly.transform([[6]]))[0])\n<\/code><\/pre>\n\n\n\n
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10. Ridge and Lasso Regression<\/h3>\n\n\n\n
from sklearn.linear_model import Ridge, Lasso\nimport numpy as np\n\nX = np.array([[1], [2], [3], [4], [5]])\ny = np.array([1, 2, 3, 4, 5])\n\nridge = Ridge(alpha=1.0)\nridge.fit(X, y)\n\nlasso = Lasso(alpha=0.1)\nlasso.fit(X, y)\n\nprint(\"Ridge prediction for 6:\", ridge.predict([[6]])[0])\nprint(\"Lasso prediction for 6:\", lasso.predict([[6]])[0])\n<\/code><\/pre>\n\n\n\n
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11. Gradient Descent implementation from scratch<\/h3>\n\n\n\n
import numpy as np\n\nX = np.array([1, 2, 3, 4, 5])\ny = np.array([2, 4, 6, 8, 10])  # y = 2x\n\nm, b = 0, 0  # initial slope and intercept\nlr = 0.01\n\nfor _ in range(1000):\n    y_pred = m * X + b\n    dm = -2 * sum(X * (y - y_pred)) \/ len(X)\n    db = -2 * sum(y - y_pred) \/ len(X)\n    m -= lr * dm\n    b -= lr * db\n\nprint(\"Learned equation: y =\", m, \"x +\", b)\n<\/code><\/pre>\n\n\n\n
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12. Train\/test split and cross-validation demo<\/h3>\n\n\n\n
from sklearn.model_selection import train_test_split, cross_val_score\nfrom sklearn.linear_model import LinearRegression\nimport numpy as np\n\nX = np.array([[i] for i in range(1, 11)])\ny = np.array([2*i for i in range(1, 11)])\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n\nmodel = LinearRegression()\nscores = cross_val_score(model, X, y, cv=5)\n\nprint(\"Cross-validation scores:\", scores)\n<\/code><\/pre>\n\n\n\n
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13. Overfitting vs. underfitting visualization<\/h3>\n\n\n\n
import matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.preprocessing import PolynomialFeatures\n\nX = np.array([[i] for i in range(1, 11)])\ny = np.array([2*i + np.random.randint(-3, 3) for i in range(1, 11)])\n\npoly = PolynomialFeatures(degree=8)\nX_poly = poly.fit_transform(X)\nmodel = LinearRegression().fit(X_poly, y)\n\nplt.scatter(X, y, color='blue')\nplt.plot(X, model.predict(X_poly), color='red')\nplt.title(\"Overfitting Example\")\nplt.show()\n<\/code><\/pre>\n\n\n\n
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14. Feature scaling (normalization vs. standardization)<\/h3>\n\n\n\n
from sklearn.preprocessing import MinMaxScaler, StandardScaler\nimport numpy as np\n\ndata = np.array([[10], [20], [30], [40], [50]])\n\nnorm = MinMaxScaler()\nstd = StandardScaler()\n\nprint(\"Normalization:\", norm.fit_transform(data).flatten())\nprint(\"Standardization:\", std.fit_transform(data).flatten())\n<\/code><\/pre>\n\n\n\n
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15. Model accuracy comparison<\/h3>\n\n\n\n
from sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nimport numpy as np\n\nX = np.array([[i] for i in range(1, 11)])\ny = np.array([0, 0, 0, 1, 1, 1, 1, 1, 1, 1])\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n\nmodels = {\n    \"Logistic Regression\": LogisticRegression(),\n    \"Decision Tree\": DecisionTreeClassifier(),\n    \"KNN\": KNeighborsClassifier()\n}\n\nfor name, model in models.items():\n    model.fit(X_train, y_train)\n    pred = model.predict(X_test)\n    print(name, \"Accuracy:\", accuracy_score(y_test, pred))\n<\/code><\/pre>\n\n\n\n
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Deep Learning Programs (15)<\/h1>\n\n\n\n
# 1. Neural Network from scratch (NumPy only)
import numpy as np

X = np.array([[0,0],[0,1],[1,0],[1,1]])
y = np.array([[0],[1],[1],[0]])

np.random.seed(42)
W1 = np.random.randn(2,2)
b1 = np.zeros((1,2))
W2 = np.random.randn(2,1)
b2 = np.zeros((1,1))

def sigmoid(x): return 1\/(1+np.exp(-x))
def sigmoid_deriv(x): return x*(1-x)

for epoch in range(10000):
    h = sigmoid(np.dot(X,W1)+b1)
    out = sigmoid(np.dot(h,W2)+b2)
    loss = np.mean((y-out)**2)
    d_out = (y-out)*sigmoid_deriv(out)
    d_h = d_out.dot(W2.T)*sigmoid_deriv(h)
    W2 += h.T.dot(d_out)*0.1
    b2 += np.sum(d_out,axis=0,keepdims=True)*0.1
    W1 += X.T.dot(d_h)*0.1
    b1 += np.sum(d_h,axis=0,keepdims=True)*0.1
print(\"Predictions:\", out.round())


# 2. Perceptron Learning Rule Demo
import numpy as np

X = np.array([[0,0],[0,1],[1,0],[1,1]])
y = np.array([0,0,0,1])  # AND logic
W = np.zeros(2)
b = 0

for epoch in range(10):
    for xi, target in zip(X,y):
        pred = np.where(np.dot(xi,W)+b >= 0,1,0)
        W += (target-pred)*xi
        b += (target-pred)
print(\"Weights:\", W, \"Bias:\", b)
<\/code><\/pre>\n\n\n\n
# 3. XOR problem solved with NN (Keras)\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense\nimport numpy as np\n\nX = np.array([[0,0],[0,1],[1,0],[1,1]])\ny = np.array([0,1,1,0])\n\nmodel = Sequential([\n    Dense(4,activation='relu',input_dim=2),\n    Dense(1,activation='sigmoid')\n])\nmodel.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])\nmodel.fit(X,y,epochs=500,verbose=0)\nprint(\"Predictions:\", model.predict(X).round())\n<\/code><\/pre>\n\n\n\n
# 4. MNIST digit recognition\nimport tensorflow as tf\nfrom tensorflow.keras.datasets import mnist\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Flatten\n\n(x_train,y_train),(x_test,y_test) = mnist.load_data()\nx_train,x_test = x_train\/255.0, x_test\/255.0\n\nmodel = Sequential([\n    Flatten(input_shape=(28,28)),\n    Dense(128,activation='relu'),\n    Dense(10,activation='softmax')\n])\nmodel.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])\nmodel.fit(x_train,y_train,epochs=5,validation_split=0.1)\nprint(\"Test accuracy:\", model.evaluate(x_test,y_test)[1])\n<\/code><\/pre>\n\n\n\n
# 5. CIFAR-10 classification\nfrom tensorflow.keras.datasets import cifar10\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Conv2D,MaxPooling2D,Flatten,Dense\n\n(x_train,y_train),(x_test,y_test) = cifar10.load_data()\nx_train,x_test = x_train\/255.0, x_test\/255.0\n\nmodel = Sequential([\n    Conv2D(32,(3,3),activation='relu',input_shape=(32,32,3)),\n    MaxPooling2D(2,2),\n    Flatten(),\n    Dense(64,activation='relu'),\n    Dense(10,activation='softmax')\n])\nmodel.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])\nmodel.fit(x_train,y_train,epochs=3,validation_split=0.1)\nprint(\"Accuracy:\", model.evaluate(x_test,y_test)[1])\n<\/code><\/pre>\n\n\n\n
# 6. Fashion-MNIST classification\nfrom tensorflow.keras.datasets import fashion_mnist\n(x_train,y_train),(x_test,y_test) = fashion_mnist.load_data()\nx_train,x_test = x_train\/255.0, x_test\/255.0\n\nmodel = Sequential([\n    Flatten(input_shape=(28,28)),\n    Dense(128,activation='relu'),\n    Dense(10,activation='softmax')\n])\nmodel.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])\nmodel.fit(x_train,y_train,epochs=5,validation_split=0.1)\nprint(\"Test Accuracy:\", model.evaluate(x_test,y_test)[1])\n<\/code><\/pre>\n\n\n\n
# 7. Cat vs Dog Classifier (CNN, simplified with TF dataset)\nimport tensorflow as tf\nimport tensorflow_datasets as tfds\n\ndataset, info = tfds.load(\"cats_vs_dogs\",as_supervised=True,with_info=True)\ndef preprocess(img,label): return tf.image.resize(img,(128,128))\/255.0,label\ntrain = dataset['train'].map(preprocess).batch(32)\n\nmodel = Sequential([\n    Conv2D(32,(3,3),activation='relu',input_shape=(128,128,3)),\n    MaxPooling2D(2,2),\n    Flatten(),\n    Dense(64,activation='relu'),\n    Dense(1,activation='sigmoid')\n])\nmodel.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])\nmodel.fit(train,epochs=1,steps_per_epoch=100) # quick demo\n<\/code><\/pre>\n\n\n\n
# 8. Sentiment Analysis on IMDB dataset (LSTM)\nfrom tensorflow.keras.datasets import imdb\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Embedding,LSTM,Dense\n\n(x_train,y_train),(x_test,y_test) = imdb.load_data(num_words=10000)\nx_train = pad_sequences(x_train,maxlen=200)\nx_test = pad_sequences(x_test,maxlen=200)\n\nmodel = Sequential([\n    Embedding(10000,128,input_length=200),\n    LSTM(64),\n    Dense(1,activation='sigmoid')\n])\nmodel.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])\nmodel.fit(x_train,y_train,epochs=2,batch_size=64,validation_split=0.1)\nprint(\"Accuracy:\", model.evaluate(x_test,y_test)[1])\n<\/code><\/pre>\n\n\n\n
# 9. Stock price prediction with LSTM (dummy data)\nimport numpy as np\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import LSTM,Dense\n\ndata = np.sin(np.arange(1000)) # fake stock trend\nX,y = [],[]\nfor i in range(20,len(data)):\n    X.append(data[i-20:i])\n    y.append(data[i])\nX,y = np.array(X),np.array(y)\nX = X.reshape((X.shape[0],X.shape[1],1))\n\nmodel = Sequential([LSTM(50,input_shape=(20,1)),Dense(1)])\nmodel.compile(optimizer='adam',loss='mse')\nmodel.fit(X,y,epochs=5,verbose=1)\n<\/code><\/pre>\n\n\n\n
# 10. Image Colorization with Autoencoder (simplified)\nfrom tensorflow.keras.layers import Conv2DTranspose,Reshape,Input\nfrom tensorflow.keras.models import Model\n\ninp = Input((28,28,1))\nx = Conv2D(32,(3,3),activation='relu',padding='same')(inp)\nx = Flatten()(x)\nx = Dense(28*28*3,activation='sigmoid')(x)\nout = Reshape((28,28,3))(x)\n\nmodel = Model(inp,out)\nmodel.compile(optimizer='adam',loss='mse')\nprint(model.summary())\n<\/code><\/pre>\n\n\n\n
# 11. Noise Reduction Autoencoder\nfrom tensorflow.keras.layers import Conv2D,UpSampling2D,MaxPooling2D\ninp = Input((28,28,1))\nx = Conv2D(32,(3,3),activation='relu',padding='same')(inp)\nx = MaxPooling2D((2,2),padding='same')(x)\nx = Conv2D(32,(3,3),activation='relu',padding='same')(x)\nencoded = MaxPooling2D((2,2),padding='same')(x)\n\nx = Conv2D(32,(3,3),activation='relu',padding='same')(encoded)\nx = UpSampling2D((2,2))(x)\nx = Conv2D(32,(3,3),activation='relu',padding='same')(x)\nx = UpSampling2D((2,2))(x)\ndecoded = Conv2D(1,(3,3),activation='sigmoid',padding='same')(x)\n\nautoencoder = Model(inp,decoded)\nautoencoder.compile(optimizer='adam',loss='binary_crossentropy')\nprint(autoencoder.summary())\n<\/code><\/pre>\n\n\n\n
# 12. Transfer Learning with ResNet50\nfrom tensorflow.keras.applications import ResNet50\nbase = ResNet50(weights='imagenet',include_top=False,input_shape=(224,224,3))\nmodel = Sequential([base,Flatten(),Dense(10,activation='softmax')])\nfor layer in base.layers: layer.trainable=False\nmodel.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])\nprint(model.summary())\n<\/code><\/pre>\n\n\n\n
# 13. Object Detection (YOLOv8 with Ultralytics)\nfrom ultralytics import YOLO\nmodel = YOLO(\"yolov8n.pt\")  # pretrained\nresults = model(\"https:\/\/ultralytics.com\/images\/bus.jpg\")\nresults.show()\n<\/code><\/pre>\n\n\n\n
# 14. Image Caption Generator (simplified, placeholder)\nprint(\"Requires large dataset (COCO). Example pipeline:\")\nprint(\"1. Extract CNN features (InceptionV3).\")\nprint(\"2. Train LSTM on captions.\")\nprint(\"3. Combine CNN+LSTM for caption generation.\")\n<\/code><\/pre>\n\n\n\n
# 15. GAN (Generate handwritten digits)\nimport tensorflow as tf\nfrom tensorflow.keras.layers import Dense,LeakyReLU,Reshape,Flatten\nfrom tensorflow.keras.models import Sequential\n\n# Generator\ngen = Sequential([\n    Dense(128,input_dim=100),LeakyReLU(0.2),\n    Dense(784,activation='tanh'),Reshape((28,28,1))\n])\n\n# Discriminator\ndisc = Sequential([\n    Flatten(input_shape=(28,28,1)),\n    Dense(128),LeakyReLU(0.2),\n    Dense(1,activation='sigmoid')\n])\n\ndisc.compile(optimizer='adam',loss='binary_crossentropy')\ndisc.trainable=False\ngan = Sequential([gen,disc])\ngan.compile(optimizer='adam',loss='binary_crossentropy')\n\nprint(\"GAN built. Train on MNIST for real results.\")\n<\/code><\/pre>\n\n\n\n
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Natural Language Processing (NLP) (15 Programs)<\/h1>\n\n\n\n
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1. Tokenization & Stopword Removal<\/h2>\n\n\n\n
import nltk\nfrom nltk.corpus import stopwords\nfrom nltk.tokenize import word_tokenize\n\nnltk.download(\"punkt\")\nnltk.download(\"stopwords\")\n\ntext = \"Itxperts provides the best website development services in India.\"\ntokens = word_tokenize(text.lower())\nfiltered = [w for w in tokens if w.isalpha() and w not in stopwords.words(\"english\")]\n\nprint(\"Original:\", tokens)\nprint(\"After stopword removal:\", filtered)\n<\/code><\/pre>\n\n\n\n
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2. Word Frequency Counter<\/h2>\n\n\n\n
from collections import Counter\ntext = \"ChatGPT helps developers. Developers use ChatGPT for coding help.\"\nwords = text.lower().split()\nfreq = Counter(words)\nprint(freq)\n<\/code><\/pre>\n\n\n\n
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3. Bag-of-Words Model for Text Classification<\/h2>\n\n\n\n
from sklearn.feature_extraction.text import CountVectorizer\n\ndocs = [\"I love coding\", \"Coding is fun\", \"I dislike bugs\"]\nvectorizer = CountVectorizer()\nX = vectorizer.fit_transform(docs)\n\nprint(\"Features:\", vectorizer.get_feature_names_out())\nprint(\"Bag of Words Matrix:\\n\", X.toarray())\n<\/code><\/pre>\n\n\n\n
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4. TF-IDF Implementation<\/h2>\n\n\n\n
from sklearn.feature_extraction.text import TfidfVectorizer\n\ndocs = [\"ChatGPT is amazing\", \"ChatGPT helps in AI\", \"AI is the future\"]\nvectorizer = TfidfVectorizer()\nX = vectorizer.fit_transform(docs)\n\nprint(\"Features:\", vectorizer.get_feature_names_out())\nprint(\"TF-IDF Matrix:\\n\", X.toarray())\n<\/code><\/pre>\n\n\n\n
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5. Sentiment Analysis using TextBlob<\/h2>\n\n\n\n
from textblob import TextBlob\n\ntext = \"I really love the services of Itxperts!\"\nblob = TextBlob(text)\nprint(\"Sentiment polarity:\", blob.sentiment.polarity)\n<\/code><\/pre>\n\n\n\n
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6. Named Entity Recognition (spaCy)<\/h2>\n\n\n\n
import spacy\nnlp = spacy.load(\"en_core_web_sm\")\n\ntext = \"Elon Musk founded SpaceX in California.\"\ndoc = nlp(text)\n\nfor ent in doc.ents:\n    print(ent.text, ent.label_)\n<\/code><\/pre>\n\n\n\n
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7. Text Summarizer (Extractive)<\/h2>\n\n\n\n
import nltk\nfrom nltk.tokenize import sent_tokenize, word_tokenize\nfrom collections import Counter\n\nnltk.download(\"punkt\")\n\ntext = \"\"\"Itxperts is a leading web development company. \nWe create websites, apps, and AI solutions for businesses worldwide. \nOur mission is to empower businesses through technology.\"\"\"\n\nsentences = sent_tokenize(text)\nwords = word_tokenize(text.lower())\nfreq = Counter(words)\n\nscores = {}\nfor sent in sentences:\n    for word in word_tokenize(sent.lower()):\n        if word in freq:\n            scores[sent] = scores.get(sent, 0) + freq[word]\n\nsummary = sorted(scores, key=scores.get, reverse=True)[:2]\nprint(\"Summary:\", \" \".join(summary))\n<\/code><\/pre>\n\n\n\n
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8. Fake News Detection (ML Model)<\/h2>\n\n\n\n
from sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.naive_bayes import MultinomialNB\n\ndocs = [\"This is a real news article\", \"Breaking!!! Aliens landed on Earth\"]\nlabels = [1, 0]  # 1=real, 0=fake\n\nvectorizer = TfidfVectorizer()\nX = vectorizer.fit_transform(docs)\n\nmodel = MultinomialNB()\nmodel.fit(X, labels)\n\ntest = [\"Aliens are coming tomorrow!\"]\nprint(\"Prediction (1=real,0=fake):\", model.predict(vectorizer.transform(test))[0])\n<\/code><\/pre>\n\n\n\n
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9. Chatbot with RNN\/LSTM<\/h2>\n\n\n\n
import tensorflow as tf\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Embedding, LSTM, Dense\n\nmodel = Sequential([\n    Embedding(1000, 64),\n    LSTM(64),\n    Dense(1, activation=\"sigmoid\")\n])\n\nmodel.compile(optimizer=\"adam\", loss=\"binary_crossentropy\")\nprint(\"Chatbot RNN\/LSTM model created!\")\n<\/code><\/pre>\n\n\n\n
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10. Transformer Model with Hugging Face<\/h2>\n\n\n\n
from transformers import pipeline\n\nqa = pipeline(\"question-answering\")\ncontext = \"Itxperts is a company that builds websites and AI solutions.\"\nquestion = \"What does Itxperts build?\"\nprint(qa(question=question, context=context))\n<\/code><\/pre>\n\n\n\n
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11. Next-Word Prediction (Markov Chains)<\/h2>\n\n\n\n
import random\n\ntext = \"I love coding in Python. Python is great for machine learning.\"\nwords = text.split()\nmarkov = {}\n\nfor i in range(len(words)-1):\n    w1, w2 = words[i], words[i+1]\n    markov.setdefault(w1, []).append(w2)\n\nword = \"Python\"\nfor _ in range(5):\n    next_word = random.choice(markov.get(word, [\"END\"]))\n    print(word, end=\" \")\n    word = next_word\n<\/code><\/pre>\n\n\n\n
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12. Topic Modeling (LDA)<\/h2>\n\n\n\n
from sklearn.decomposition import LatentDirichletAllocation\nfrom sklearn.feature_extraction.text import CountVectorizer\n\ndocs = [\"AI is the future\", \"I love AI and machine learning\", \"Politics and government news\"]\nvectorizer = CountVectorizer()\nX = vectorizer.fit_transform(docs)\n\nlda = LatentDirichletAllocation(n_components=2, random_state=0)\nlda.fit(X)\n\nfor idx, topic in enumerate(lda.components_):\n    print(\"Topic\", idx, [vectorizer.get_feature_names_out()[i] for i in topic.argsort()[-3:]])\n<\/code><\/pre>\n\n\n\n
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13. Question-Answering System<\/h2>\n\n\n\n
from transformers import pipeline\n\nqa = pipeline(\"question-answering\")\ncontext = \"Barack Obama was the 44th president of the United States.\"\nquestion = \"Who was the 44th president of the USA?\"\nprint(qa(question=question, context=context))\n<\/code><\/pre>\n\n\n\n
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14. Language Translation (Seq2Seq Model)<\/h2>\n\n\n\n
from transformers import pipeline\n\ntranslator = pipeline(\"translation_en_to_fr\")\nprint(translator(\"Itxperts develops modern websites\", max_length=40))\n<\/code><\/pre>\n\n\n\n
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15. Speech-to-Text & Text-to-Speech<\/h2>\n\n\n\n
import pyttsx3\nimport speech_recognition as sr\n\n# Text-to-Speech\nengine = pyttsx3.init()\nengine.say(\"Hello, welcome to Itxperts!\")\nengine.runAndWait()\n\n# Speech-to-Text\nr = sr.Recognizer()\nwith sr.Microphone() as source:\n    print(\"Speak something...\")\n    audio = r.listen(source)\n    try:\n        print(\"You said:\", r.recognize_google(audio))\n    except:\n        print(\"Could not understand audio\")\n<\/code><\/pre>\n\n\n\n
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Computer Vision Programs (15 with Code)<\/h2>\n\n\n\n
1. Face Detection with OpenCV<\/h3>\n\n\n\n
import cv2\n\n# Load pre-trained face detector\nface_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + \"haarcascade_frontalface_default.xml\")\ncap = cv2.VideoCapture(0)\n\nwhile True:\n    ret, frame = cap.read()\n    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n    faces = face_cascade.detectMultiScale(gray, 1.3, 5)\n    \n    for (x, y, w, h) in faces:\n        cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)\n    \n    cv2.imshow('Face Detection', frame)\n    if cv2.waitKey(1) & 0xFF == ord('q'):\n        break\n\ncap.release()\ncv2.destroyAllWindows()\n<\/code><\/pre>\n\n\n\n
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2. Smile Detector with OpenCV<\/h3>\n\n\n\n
import cv2\n\nface_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + \"haarcascade_frontalface_default.xml\")\nsmile_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + \"haarcascade_smile.xml\")\n\ncap = cv2.VideoCapture(0)\n\nwhile True:\n    ret, frame = cap.read()\n    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n    faces = face_cascade.detectMultiScale(gray, 1.3, 5)\n\n    for (x, y, w, h) in faces:\n        roi_gray = gray[y:y+h, x:x+w]\n        roi_color = frame[y:y+h, x:x+w]\n        smiles = smile_cascade.detectMultiScale(roi_gray, 1.8, 20)\n        \n        for (sx, sy, sw, sh) in smiles:\n            cv2.rectangle(roi_color, (sx, sy), (sx+sw, sy+sh), (0, 255, 0), 2)\n\n    cv2.imshow('Smile Detector', frame)\n    if cv2.waitKey(1) & 0xFF == ord('q'):\n        break\n\ncap.release()\ncv2.destroyAllWindows()\n<\/code><\/pre>\n\n\n\n
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3. Age & Gender Prediction from Images<\/h3>\n\n\n\n
(Uses a pre-trained deep learning model from OpenCV)<\/p>\n\n\n\n
import cv2\n\n# Load models\nage_proto = \"deploy_age.prototxt\"\nage_model = \"age_net.caffemodel\"\ngender_proto = \"deploy_gender.prototxt\"\ngender_model = \"gender_net.caffemodel\"\n\nage_net = cv2.dnn.readNet(age_model, age_proto)\ngender_net = cv2.dnn.readNet(gender_model, gender_proto)\n\nMODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746)\nage_list = ['(0-2)', '(4-6)', '(8-12)', '(15-20)', '(25-32)', '(38-43)', '(48-53)', '(60-100)']\ngender_list = ['Male', 'Female']\n\ncap = cv2.VideoCapture(0)\n\nwhile True:\n    ret, frame = cap.read()\n    blob = cv2.dnn.blobFromImage(frame, 1.0, (227, 227), MODEL_MEAN_VALUES, swapRB=False)\n    \n    gender_net.setInput(blob)\n    gender = gender_list[gender_net.forward().argmax()]\n    \n    age_net.setInput(blob)\n    age = age_list[age_net.forward().argmax()]\n    \n    cv2.putText(frame, f\"{gender}, {age}\", (50,50), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2)\n    cv2.imshow(\"Age & Gender Prediction\", frame)\n    \n    if cv2.waitKey(1) & 0xFF == ord('q'):\n        break\n\ncap.release()\ncv2.destroyAllWindows()\n<\/code><\/pre>\n\n\n\n
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4. Real-time Face Recognition<\/h2>\n\n\n\n
# pip install face_recognition opencv-python\nimport face_recognition, cv2\n\nvideo = cv2.VideoCapture(0)\n\nknown_img = face_recognition.load_image_file(\"known.jpg\")    # supply a face image\nknown_enc = face_recognition.face_encodings(known_img)[0]\n\nwhile True:\n    ok, frame = video.read()\n    if not ok: break\n    rgb = frame[:, :, ::-1]\n    locs = face_recognition.face_locations(rgb)\n    encs = face_recognition.face_encodings(rgb, locs)\n\n    for (top, right, bottom, left), enc in zip(locs, encs):\n        match = face_recognition.compare_faces([known_enc], enc)[0]\n        name = \"Known\" if match else \"Unknown\"\n        cv2.rectangle(frame, (left, top), (right, bottom), (0,255,0), 2)\n        cv2.putText(frame, name, (left, top-10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0,255,0), 2)\n\n    cv2.imshow(\"Face Recognition\", frame)\n    if cv2.waitKey(1) & 0xFF == ord(\"q\"): break\n\nvideo.release(); cv2.destroyAllWindows()\n<\/code><\/pre>\n\n\n\n
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5. Eye Blink Detection (Drowsiness Detection)<\/h2>\n\n\n\n
# pip install dlib imutils scipy opencv-python\nimport cv2, dlib\nfrom scipy.spatial import distance as dist\n\ndef eye_aspect_ratio(eye):\n    A = dist.euclidean(eye[1], eye[5]); B = dist.euclidean(eye[2], eye[4])\n    C = dist.euclidean(eye[0], eye[3])\n    return (A + B) \/ (2.0 * C)\n\ndetector = dlib.get_frontal_face_detector()\npredictor = dlib.shape_predictor(\"shape_predictor_68_face_landmarks.dat\")  # download file\n\nEYE_AR_THRESH, EYE_AR_CONSEC_FRAMES = 0.25, 3\ncounter = 0\n\ncap = cv2.VideoCapture(0)\nwhile True:\n    ok, frame = cap.read()\n    if not ok: break\n    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n    for rect in detector(gray):\n        shape = predictor(gray, rect)\n        points = [(shape.part(i).x, shape.part(i).y) for i in range(68)]\n\n        left = [points[i] for i in [36,37,38,39,40,41]]\n        ear = eye_aspect_ratio(left)\n        if ear < EYE_AR_THRESH:\n            counter += 1\n            if counter >= EYE_AR_CONSEC_FRAMES:\n                cv2.putText(frame, \"DROWSY!\", (30,60), cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0,0,255), 3)\n        else:\n            counter = 0\n\n    cv2.imshow(\"Blink \/ Drowsiness\", frame)\n    if cv2.waitKey(1) & 0xFF == ord('q'): break\n\ncap.release(); cv2.destroyAllWindows()\n<\/code><\/pre>\n\n\n\n
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6. Object Tracking (OpenCV)<\/h2>\n\n\n\n
# OpenCV built-in trackers: \"CSRT\" is accurate\nimport cv2\n\ncap = cv2.VideoCapture(0)\nok, frame = cap.read()\nbbox = cv2.selectROI(\"Select Object\", frame, False, False)\ntracker = cv2.legacy.TrackerCSRT_create()\ntracker.init(frame, bbox)\n\nwhile True:\n    ok, frame = cap.read()\n    if not ok: break\n    ok, box = tracker.update(frame)\n    if ok:\n        x, y, w, h = [int(v) for v in box]\n        cv2.rectangle(frame, (x,y), (x+w, y+h), (0,255,0), 2)\n    else:\n        cv2.putText(frame, \"Tracking lost\", (20,40), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0,0,255), 2)\n    cv2.imshow(\"Object Tracking (CSRT)\", frame)\n    if cv2.waitKey(1) & 0xFF == ord('q'): break\n\ncap.release(); cv2.destroyAllWindows()\n<\/code><\/pre>\n\n\n\n
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7. Background Removal (Segmentation via GrabCut)<\/h2>\n\n\n\n
import cv2, numpy as np\n\nimg = cv2.imread(\"person.jpg\")  # subject against relatively simple background\nmask = np.zeros(img.shape[:2], np.uint8)\nbgModel = np.zeros((1,65), np.float64)\nfgModel = np.zeros((1,65), np.float64)\n\nrect = (10, 10, img.shape[1]-20, img.shape[0]-20)  # initial rectangle\ncv2.grabCut(img, mask, rect, bgModel, fgModel, 5, cv2.GC_INIT_WITH_RECT)\n\nmask2 = np.where((mask==2)|(mask==0), 0, 1).astype('uint8')\nresult = img * mask2[:, :, np.newaxis]\ncv2.imwrite(\"foreground.png\", result)\nprint(\"Saved: foreground.png\")\n<\/code><\/pre>\n\n\n\n
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8. QR Code & Barcode Scanner<\/h2>\n\n\n\n
# Option A: OpenCV QRCodeDetector (QR only)\nimport cv2\ndetector = cv2.QRCodeDetector()\ncap = cv2.VideoCapture(0)\n\nwhile True:\n    ok, frame = cap.read()\n    if not ok: break\n    data, pts, _ = detector.detectAndDecode(frame)\n    if data:\n        cv2.putText(frame, f\"QR: {data}\", (20,40), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0,255,0), 2)\n    cv2.imshow(\"QR Scanner\", frame)\n    if cv2.waitKey(1) & 0xFF == ord('q'): break\n\ncap.release(); cv2.destroyAllWindows()\n\n# Option B (barcodes too): pip install pyzbar\n# from pyzbar.pyzbar import decode; for obj in decode(frame): print(obj.data.decode())\n<\/code><\/pre>\n\n\n\n
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9. Hand Gesture Recognition (Counting Fingers with MediaPipe)<\/h2>\n\n\n\n
# pip install mediapipe opencv-python\nimport cv2, mediapipe as mp\n\nhands = mp.solutions.hands.Hands(min_detection_confidence=0.5, min_tracking_confidence=0.5)\ndraw = mp.solutions.drawing_utils\n\ncap = cv2.VideoCapture(0)\nwhile True:\n    ok, frame = cap.read()\n    if not ok: break\n    rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n    res = hands.process(rgb)\n    fingers_up = 0\n\n    if res.multi_hand_landmarks:\n        for handLms in res.multi_hand_landmarks:\n            lm = handLms.landmark\n            # Simple heuristic: tip above pip for fingers (index\u2013pinky). Thumb uses x-coord.\n            tip_ids = [4, 8, 12, 16, 20]\n            if lm[tip_ids[0]].x < lm[tip_ids[0]-1].x: fingers_up += 1  # thumb (for right hand)\n            for i in range(1,5):\n                if lm[tip_ids[i]].y < lm[tip_ids[i]-2].y: fingers_up += 1\n            draw.draw_landmarks(frame, handLms, mp.solutions.hands.HAND_CONNECTIONS)\n\n    cv2.putText(frame, f\"Fingers: {fingers_up}\", (20,50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255,0), 2)\n    cv2.imshow(\"Hand Gesture (MediaPipe)\", frame)\n    if cv2.waitKey(1) & 0xFF == ord('q'): break\n\ncap.release(); cv2.destroyAllWindows()\n<\/code><\/pre>\n\n\n\n
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10. Emotion Detection from Face<\/h2>\n\n\n\n
# pip install fer opencv-python\nfrom fer import FER\nimport cv2\n\ndetector = FER()  # uses MTCNN\/haar internally if available\ncap = cv2.VideoCapture(0)\n\nwhile True:\n    ok, frame = cap.read()\n    if not ok: break\n    results = detector.detect_emotions(frame)\n    for r in results:\n        (x,y,w,h) = r[\"box\"]\n        emotion, score = max(r[\"emotions\"].items(), key=lambda x: x[1])\n        cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,0),2)\n        cv2.putText(frame, f\"{emotion} {score:.2f}\", (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.8,(0,255,0),2)\n\n    cv2.imshow(\"Emotion Detection\", frame)\n    if cv2.waitKey(1) & 0xFF == ord('q'): break\n\ncap.release(); cv2.destroyAllWindows()\n<\/code><\/pre>\n\n\n\n
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11. OCR (Optical Character Recognition) with Tesseract<\/h2>\n\n\n\n
# pip install pytesseract pillow opencv-python\n# Install Tesseract engine separately and set the path if required.\nimport cv2, pytesseract\n\nimg = cv2.imread(\"text_image.png\")\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\ngray = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]\ntext = pytesseract.image_to_string(gray, lang=\"eng\")\nprint(text)\n<\/code><\/pre>\n\n\n\n
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12. License Plate Recognition System (Simple)<\/h2>\n\n\n\n
# pip install imutils pytesseract opencv-python\n# You may also use a pretrained cascade for plates or YOLO for better detection.\nimport cv2, pytesseract\n\nimg = cv2.imread(\"car.jpg\")\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\ngray = cv2.bilateralFilter(gray, 11, 17, 17)\nedges = cv2.Canny(gray, 30, 200)\n\ncontours, _ = cv2.findContours(edges.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\ncontours = sorted(contours, key=cv2.contourArea, reverse=True)[:10]\n\nplate = None\nfor c in contours:\n    peri = cv2.arcLength(c, True)\n    approx = cv2.approxPolyDP(c, 0.018 * peri, True)\n    if len(approx) == 4:\n        x,y,w,h = cv2.boundingRect(approx)\n        plate = img[y:y+h, x:x+w]\n        break\n\nif plate is not None:\n    plate_gray = cv2.cvtColor(plate, cv2.COLOR_BGR2GRAY)\n    text = pytesseract.image_to_string(plate_gray, config=\"--psm 7\")\n    print(\"Plate:\", text.strip())\nelse:\n    print(\"Plate not found\")\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
13. Lane Detection for Self-Driving Cars<\/h2>\n\n\n\n
import cv2, numpy as np\n\ndef region_of_interest(img):\n    h, w = img.shape[:2]\n    mask = np.zeros_like(img)\n    polygon = np.array([[(0,h),(w\/\/2, int(h*0.6)), (w, h)]], dtype=np.int32)\n    cv2.fillPoly(mask, polygon, 255)\n    return cv2.bitwise_and(img, mask)\n\ncap = cv2.VideoCapture(\"road.mp4\")  # or 0 for webcam\n\nwhile True:\n    ok, frame = cap.read()\n    if not ok: break\n    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n    blur = cv2.GaussianBlur(gray, (5,5), 0)\n    edges = cv2.Canny(blur, 50, 150)\n    roi = region_of_interest(edges)\n    lines = cv2.HoughLinesP(roi, 1, np.pi\/180, threshold=50, minLineLength=50, maxLineGap=150)\n    if lines is not None:\n        for l in lines:\n            x1,y1,x2,y2 = l[0]\n            cv2.line(frame, (x1,y1), (x2,y2), (0,255,0), 4)\n    cv2.imshow(\"Lane Detection\", frame)\n    if cv2.waitKey(1) & 0xFF == ord('q'): break\n\ncap.release(); cv2.destroyAllWindows()\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
14. Pose Estimation (MediaPipe)<\/h2>\n\n\n\n
# pip install mediapipe opencv-python\nimport cv2, mediapipe as mp\n\npose = mp.solutions.pose.Pose()\ndraw = mp.solutions.drawing_utils\n\ncap = cv2.VideoCapture(0)\nwhile True:\n    ok, frame = cap.read()\n    if not ok: break\n    rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n    res = pose.process(rgb)\n    if res.pose_landmarks:\n        draw.draw_landmarks(frame, res.pose_landmarks, mp.solutions.pose.POSE_CONNECTIONS)\n    cv2.imshow(\"Pose Estimation\", frame)\n    if cv2.waitKey(1) & 0xFF == ord('q'): break\n\ncap.release(); cv2.destroyAllWindows()\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
15. Image Similarity Detection (Feature Matching with ORB)<\/h2>\n\n\n\n
import cv2\n\nimg1 = cv2.imread(\"image1.jpg\", 0)\nimg2 = cv2.imread(\"image2.jpg\", 0)\n\norb = cv2.ORB_create(1000)\nkp1, des1 = orb.detectAndCompute(img1, None)\nkp2, des2 = orb.detectAndCompute(img2, None)\n\nbf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)\nmatches = bf.match(des1, des2)\nmatches = sorted(matches, key=lambda x: x.distance)\n\nsimilarity_score = sum([m.distance for m in matches[:50]]) \/ max(1, len(matches[:50]))\nprint(\"Lower is more similar. ORB score:\", similarity_score)\n\nmatched = cv2.drawMatches(img1, kp1, img2, kp2, matches[:50], None, flags=2)\ncv2.imshow(\"Matches\", matched); cv2.waitKey(0); cv2.destroyAllWindows()\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Notes & Setup Tips<\/h3>\n\n\n\n
    \n
  • Some programs need extra assets (e.g., known.jpg<\/code>, person.jpg<\/code>, car.jpg<\/code>, road.mp4<\/code>) and model files (shape_predictor_68_face_landmarks.dat<\/code>, age\/gender Caffe models).<\/li>\n\n\n\n
  • For Tesseract OCR, install the Tesseract engine and ensure it\u2019s on your system PATH (or set pytesseract.pytesseract.tesseract_cmd<\/code>).<\/li>\n\n\n\n
  • If OpenCV\u2019s legacy tracker API differs in your version, you may need to change to cv2.TrackerCSRT_create()<\/code> (older) or cv2.legacy.TrackerCSRT_create()<\/code> (newer builds expose under legacy<\/code>).<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"
    1. Hello AI \u2013 Print “Hello AI World” 2. Fibonacci Sequence Generator (Recursion vs Iteration) 3. Tic-Tac-Toe with Minimax AI 4. Rock-Paper-Scissors AI (Random + Rule-based) 5. Number Guessing Game with AI Hints 6. Simple Chatbot (If-Else) 7. Magic 8-ball Predictor 8. Spam vs Ham Classifier (Naive Bayes with sample data) 9. Word Auto-completion (Dictionary-based) […]<\/p>\n","protected":false},"author":1,"featured_media":1957,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"googlesitekit_rrm_CAow44u0DA:productID":"","footnotes":""},"categories":[44,164,39,123],"tags":[],"class_list":["post-1956","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-python-tutorials","category-practical","category-practical-file","category-python-worksheets"],"acf":[],"yoast_head":"\n100+ Python AI Programs to Kickstart Your Career in Artificial Intelligence - Itxperts<\/title>\n<meta name=\"description\" content=\"Learn AI with 100+ hands-on Python programs covering Machine Learning, Deep Learning, NLP, and Computer Vision. 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