AI training institute ahmedabad

Python with AI and ML Course Syllabus

Python Fundamentals for Data Science
  • NumPy: Numerical computing, array manipulation.
  • Pandas: Data manipulation and analysis (DataFrames).
  • Matplotlib & Seaborn: Data visualization.
  • Scikit-learn: Machine learning algorithms.
Foundations of AI and ML
  • Definitions, history, types of ML (supervised, unsupervised, reinforcement learning), applications.
  • Mathematics for ML
  • Linear algebra (vectors, matrices)
  • Probability and statistics (distributions, hypothesis testing)
  • Calculus (derivatives, optimization basics).
Data Handling and Preprocessing
  • Data Acquisition: Loading data from various sources (CSV, databases).
  • Data Cleaning: Handling missing values, outliers, inconsistencies.
  • Feature Engineering: Creating new features, transforming existing ones.
  • Exploratory Data Analysis (EDA): Summarizing and visualizing data to understand patterns.
Machine Learning Algorithms
  • Supervised Learning
  • Regression: Linear Regression, Polynomial Regression, Ridge, Lasso.
  • Classification: Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machines (SVM)
  • Decision Trees, Naive Bayes, Ensemble Methods (Random Forest, Gradient Boosting).
  • Unsupervised Learning
  • Clustering: K-Means, Hierarchical Clustering.
  • Dimensionality Reduction: Principal Component Analysis (PCA).
Deep Learning (Introduction)
  • Neural Networks: Perceptrons, activation functions, backpropagation.
  • Deep Learning Frameworks: Introduction to TensorFlow or Keras.
  • Types of Neural Networks: Convolutional Neural Networks (CNNs) for image data
  • Recurrent Neural Networks (RNNs) for sequential data.
Natural language processing (NLP)
  • Text preprocessing: Tokenization, stemming, and lemmatization.
  • Text representation: Word embeddings like Word2Vec and GloVe.
  • Practical applications: Building sentiment analysis and text classification models.
Model Evaluation and Deployment
  • Model Evaluation Metrics: Accuracy, precision, recall, F1-score
  • ROC-AUC (for classification); R-squared, Mean Squared Error (MSE) (for regression).
  • Cross-Validation & Hyperparameter Tuning: Optimizing model performance.
  • Model Deployment Concepts: Introduction to deploying ML models.
  • Recurrent Neural Networks (RNNs) for sequential data.
Projects and case studies
  • Hands-on projects to apply skills to real-world problems.
  • Example projects: Building a housing price prediction model.
  • Creating a sentiment analyzer for customer reviews.
  • Developing an image classification model for medical diagnoses.
  • Constructing a fraud detection system.

Duration : 5 Months

Eligibility : Knowledge of Core Python

Available Batches : Regular Batch -> Monday-Saturday
WeekEnd Batch -> Saturday and Sunday
Fast-Track Batch ->4 to 5 hrs daily
Present & Future Scope
  • Got placements in Mid and Large size co.s
  • Advantage of Open source - more acceptance
  • Mobile is future Technology - Helpful to build career
  • For long term it is good technology
  • Go for other technologies also for taking added advantage
  • Android applications are more developed comparing to iPhone
  • We are famous as Android training institute in ahmedabad.
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