Best Python Training in Mohali

Build a strong foundation in Python programming.

• Python Basics
  • Introduction to Python, Installation & IDEs
  • Variables, Data Types, Operators
  • Input/Output
  • Basic control structures: if-else, loops (for, while)

• Data Structures
  • Lists, Tuples, Dictionaries, Sets
  • List comprehension
  • String manipulation
  • Functions, Lambda expressions
• OOP in Python
  • Classes, Objects
  • Inheritance, Encapsulation, Polymorphism
  • Dunder (Magic) methods
  • Exception handling
• Python Libraries Overview
  • Working with files (txt, CSV)
  • Overview of libraries (NumPy, pandas, matplotlib)
  • Introduction to Python testing (unittest, pytest)

Learn essential data manipulation and visualization skills.

• NumPy
  • Introduction to NumPy
  • Arrays, Array operations
  • Mathematical functions and array manipulations
• pandas
  • Introduction to pandas
  • DataFrames, Series
  • Data Wrangling: Filtering, Sorting, Aggregations
• Data Visualization
  • Introduction to Matplotlib and Seaborn
  • Plotting basics (line plots, bar plots, histograms, etc.)
  • Customizing plots
• Exploratory Data Analysis (EDA)
  • Descriptive statistics (mean, median, mode, variance, etc.)
  • Correlation, Covariance
  • Data Cleaning: Handling missing data, outliers
  • pandas-profiling, creating EDA reports

Understand the fundamentals of Machine Learning.

• Introduction to Machine Learning
  • What is ML? Overview and types of ML (Supervised, Unsupervised, Reinforcement)
  • Key concepts: Features, Labels, Training, Testing
  • Steps of ML pipeline
• Supervised Learning – Regression
  • Linear Regression
  • Polynomial Regression
  • Evaluation metrics (MAE, MSE, RMSE)
  • Implementing regression using scikit-learn
• Supervised Learning – Classification
  • Logistic Regression
  • Decision Trees
  • k-Nearest Neighbors (k-NN)
  • Evaluation metrics (Accuracy, Precision, Recall, F1 Score)
• Model Validation
  • Train-Test Split
  • Cross-validation techniques
  • Bias-Variance trade-off

Learn the basics of Deep Learning.

• Ensemble Learning
  • Random Forests
  • Bagging, Boosting (AdaBoost, Gradient Boosting)
  • XGBoost
• Support Vector Machines (SVM)
  • SVM for classification
  • Kernels and the kernel trick
• Unsupervised Learning – Clustering
  • K-Means clustering
  • Hierarchical clustering
  • DBSCAN
  • Evaluating clustering performance
• Dimensionality Reduction
  • Principal Component Analysis (PCA)
  • t-SNE
  • Feature Engineering and Feature Scaling

Learn the basics of Deep Learning.

• Introduction to Neural Networks
  • What is Deep Learning? Differences between ML and DL
  • Introduction to Artificial Neural Networks (ANN)
  • Forward and Backward Propagation
• Training Neural Networks
  • Activation functions (ReLU, Sigmoid, Tanh)
  • Cost function and optimization (Gradient Descent, Adam)
  • Overfitting, Regularization (Dropout, L2)
• Deep Learning with TensorFlow/Keras
  • Introduction to TensorFlow and Keras
  • Building a simple ANN
  • Model evaluation and tuning (Learning Rate, Batch Size)
• Convolutional Neural Networks (CNN)
  • Introduction to CNNs for image data
  • Convolution and Pooling layers
  • Building CNNs for Image Classification

Learn cutting-edge AI techniques.

• Recurrent Neural Networks (RNN)
  • Introduction to RNNs for sequential data
  • Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)
  • Time series forecasting using RNNs
• Natural Language Processing (NLP)
  • Text preprocessing (Tokenization, Lemmatization)
  • Word Embeddings (Word2Vec, GloVe)
  • Text classification using RNN/CNN
• Reinforcement Learning (RL)
  • What is Reinforcement Learning? Key concepts
  • Q-Learning, Deep Q-Networks (DQN)
  • Applications of RL (e.g., gaming, robotics)
• Model Deployment and AI Ethics
  • Model deployment using Flask or FastAPI
  • Introduction to MLOps and CI/CD for ML
  • Ethical issues in AI: Bias, fairness, transparency

Throughout the 6 months, you’ll become familiar with:

• Python Libraries: NumPy, pandas, matplotlib, seaborn, scikit-learn
• ML Libraries: TensorFlow, Keras, PyTorch (optional)
• Deployment: Flask/FastAPI
• Other Tools: Git, Docker (for model deployment), Jupyter Notebooks