Course Content
Day-2: How to use VScode (an IDE) for Python?
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Day-3: Basics of Python Programming
This section will train you for Python programming language
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Day-4: Data Visualization and Jupyter Notebooks
You will learn basics of Data Visualization and jupyter notebooks in this section.
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Day-5: MarkDown language
You will learn whole MarkDown Language in this section.
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Day-10: Data Wrangling and Data Visualization
Data Wrangling and Visualization is an important part of Exploratory Data Analysis, and we are going to learn this.
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Day-11: Data Visualization in Python
We will learn about Data Visualization in Python in details.
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Day-12,13: Exploratory Data Analysis (EDA)
EDA stands for Exploratory Data Analysis. It refers to the initial investigation and analysis of data to understand the key properties and patterns within the dataset.
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Day-15: Data Wrangling Techniques (Beginner to Pro)
Data Wrangling in python
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Day-26: How to use Conda Environments?
We are going to learn conda environments and their use in this section
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Day-37: Time Series Analysis
In this Section we will learn doing Time Series Analysis in Python.
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Day-38: NLP (Natural Language Processing)
In this section we learn basics of NLP
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Day-39: git and github
We will learn about git and github
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Day-40: Prompt Engineering (ChatGPT for Social Media Handling)
Social media per activae rehna hi sab kuch hy, is main ap ko wohi training milay ge.
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Python ka Chilla for Data Science (40 Days of Python for Data Science)
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    Muhammad Tufail 4 months ago
    we are importing from sklearn.neural_network import MLPClassifier # # Create a multi-layer perceptron classifier model = MLPClassifier() # # Train the model model.fit(X_train, y_train) y_pred =model.predict(X_test) cm = confusion_matrix(y_test,y_pred) cm print("confusion_matrix:",confusion_matrix(y_test,y_pred)) print("Precision_score:",precision_score(y_test,y_pred)) print("Recall_score:",recall_score(y_test,y_pred)) print("F1 score:",f1_score(y_test,y_pred))
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    Muhammad Adil Naeem 4 months ago
    from sklearn.model_selection import cross_val_score from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score from sklearn.linear_model import LogisticRegression from sklearn.neighbors import KNeighborsClassifier from sklearn.svm import SVC from sklearn.naive_bayes import GaussianNB from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor# Initialize models models = { "Logistic Regression": LogisticRegression(), "K Nearest Neighbors": KNeighborsClassifier(), "Support Vector Machines": SVC(), "Kernel SVM": SVC(kernel='rbf'), "Naive Bayes": GaussianNB(), "Decision Tree Classification": DecisionTreeClassifier(), "Decision Tree Regression": DecisionTreeRegressor() }# Load your data and split into features X and target variable y# Define evaluation metrics scoring = { 'accuracy': 'accuracy', 'precision': 'precision_macro', 'recall': 'recall_macro', 'f1': 'f1_macro' }# Perform model selection results = {} for name, model in models.items(): scores = cross_val_score(model, X, y, cv=5, scoring=scoring) results[name] = { 'Accuracy': scores.mean(), 'Precision': scores.mean(), 'Recall': scores.mean(), 'F1': scores.mean() }# Print results for name, scores in results.items(): print(f"Model: {name}") print(f"Accuracy: {scores['Accuracy']:.4f}") print(f"Precision: {scores['Precision']:.4f}") print(f"Recall: {scores['Recall']:.4f}") print(f"F1: {scores['F1']:.4f}") print()
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    shafiq ahmed 7 months ago
    from sklearn.ensemble import GradientBoostingClassifier# Create a gradient boosting classifier model = GradientBoostingClassifier()# # Train the model model.fit(X_train, y_train) y_pred =model.predict(X_test) cm = confusion_matrix(y_test,y_pred) cmprint("confusion_matrix:",confusion_matrix(y_test,y_pred)) print("Precision_score:",precision_score(y_test,y_pred)) print("Recall_score:",recall_score(y_test,y_pred)) print("F1 score:",f1_score(y_test,y_pred))
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    shafiq ahmed 7 months ago
    from sklearn.neural_network import MLPClassifier# # Create a multi-layer perceptron classifier model = MLPClassifier()# # Train the model model.fit(X_train, y_train) y_pred =model.predict(X_test) cm = confusion_matrix(y_test,y_pred) cmprint("confusion_matrix:",confusion_matrix(y_test,y_pred)) print("Precision_score:",precision_score(y_test,y_pred)) print("Recall_score:",recall_score(y_test,y_pred)) print("F1 score:",f1_score(y_test,y_pred))
    shafiq ahmed 7 months ago
    from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score# Assuming you have a dataset with features X and corresponding labels y # Split the dataset into training and testing sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)# Create a Random Forest classifier object rf = RandomForestClassifier(n_estimators=100, random_state=42)# Train the classifier on the training data rf.fit(X_train, y_train)# Make predictions on the test data y_pred = rf.predict(X_test)# Evaluate the accuracy of the classifier accuracy = accuracy_score(y_test, y_pred) print("Accuracy:", accuracy)
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    Ameer Hamza 7 months ago
    Everything is clear. Alhamdulillah
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    Liaqat Ali 9 months ago
    Sir,to day lecturer us difficult.
    Reply
    Babar 9 months ago
    currently looking difficult, hope so inshaALLAH after 1st oct it will be easy to understand when attempt it 2nd time.
    Reply