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Welcome!
This AI using Python syllabus provides a comprehensive roadmap, offering hands-on experience with key libraries like Pandas, NumPy, Scikit-learn, TensorFlow, Keras, NLTK, spaCy, OpenCV, and Gym. It prepares students to build AI-driven solutions in real-world scenarios, covering machine learning, deep learning, natural language processing, computer vision, and reinforcement learning, enabling them to tackle diverse AI challenges effectively.
Prerequisites
Prerequisites for learning AI using Python include basic programming skills, preferably in Python, knowledge of linear algebra, probability, and statistics, as well as a fundamental understanding of machine learning concepts. Familiarity with data structures and algorithms will be beneficial.
Learning Objectives
Learning objectives for AI using Python include mastering AI techniques like machine learning, deep learning, natural language processing, and computer vision. Students will gain hands-on experience with essential libraries and learn to develop AI-driven solutions for real-world applications across various domains.
Course Overview
Module 1: Introduction to Artificial Intelligence and Python
- AI vs. Machine Learning vs. Deep Learning
- AI in Industry (Healthcare, Finance, Automotive, etc.)
- AI Development Lifecycle
Overview, PVM, Installation, IDEs, First Program, I/O, Tokens, Variables
Data Types, Type Casting, Operators, Expressions
Conditional (if, else, elif), Loops (for, while)
Strings, Indexing, Slicing, Methods, Regular Expressions
Lists, Indexing, Slicing, Methods, Nested Lists, Comprehensions
Tuples, Sets : Creation, Indexing, Methods.
Dictionaries: Access, Methods, Ordered Dicts
Defining Functions, Parameters, Lambdas, Modules, Recursion
File Operations, Text/Binary, pickle, csv, Random Access
Connect, CRUD Operations, Python with MySql/MongoDB
Module 2: Data Handling and Preprocessing with Pandas and NumPy
- Handling Data with Pandas (DataFrames, Series)
- Array Operations with NumPy (Matrices, Linear Algebra)
- Data Loading (CSV, Excel, Databases)
- Handling Missing Values, Outliers
- Data Cleaning, Transformation, and Normalization
- Feature Engineering and Scaling Data
Module 3: Data Visualization using Matplotlib and Seaborn
- Line, Bar, Scatter, Histogram Plots
- Customizing Graphs (Titles, Axes, Legends)
- Heatmaps, Pairplots, Distribution Plots, Violin Plots
- Correlation and Multivariate Data Visualization
Module 4: Machine Learning with Scikit-learn
- Supervised vs. Unsupervised Learning
- ML Workflow: Data -> Model -> Evaluation
- Data Splitting: Train-Test and Cross-Validation
- Model Training and Evaluation
- Regression: Linear, Logistic, Ridge, Lasso
- Classification: KNN, SVM, Decision Trees, Random Forest
- Model Evaluation Metrics: Accuracy, Precision, Recall, F1-Score, AUC-ROC
- Clustering: K-Means, Hierarchical Clustering
- Dimensionality Reduction: PCA, t-SNE
Module 5: Deep Learning with TensorFlow and Keras
- Perceptron Model, Activation Functions
- Forward and Backward Propagation
- Installing and Setting up TensorFlow and Keras
- Sequential and Functional Models in Keras
- Dense, Convolutional, Recurrent Layers
- Loss Functions, Optimizers (SGD, Adam), Backpropagation
- CNN Architecture: Convolutions, Pooling, Fully Connected Layers
- Image Classification with CNN
Module 6: Natural Language Processing (NLP) with NLTK and spaCy
- Tokenization, Stemming, Lemmatization
- Stopwords Removal, POS Tagging
- Text Preprocessing, Frequency Distribution
- Sentiment Analysis, Named Entity Recognition (NER)
- spaCy for Tokenization, NER, and Dependency Parsing
- Custom NLP Pipelines with spaCy
- Bag of Words (BoW), TF-IDF Vectorization
- Text Classification using Naive Bayes, SVM
Module 7: Computer Vision with OpenCV
- Basics of Image Processing
- Image Representation and Formats
- Image Reading, Writing, and Display
- Image Manipulation (Resize, Crop, Rotate)
- Edge Detection (Canny, Sobel)
- Image Filtering (Gaussian, Median)
- Face Detection using Haar Cascades
- Object Detection and Recognition with Pre-trained Models
- Case Study: Implementing AI-driven Object Detection System
Module 8: Reinforcement Learning with Gym and TensorFlow
- RL Concepts: Agents, Environment, Actions, Rewards
- Types of RL: Model-based, Model-free
- Setting up Gym Environment
- Customizing RL Tasks
- Q-Value Function and Bellman Equation
- Building a Q-Learning Agent
- Policy Gradient Methods
Module 9: AI Ethics and Interpretability
- Bias, Fairness, and Transparency in AI
- Responsible AI Development
- AI for Social Good and Global Impact
- SHAP (SHapley Additive exPlanations)
- LIME (Local Interpretable Model-agnostic Explanations)
- Case Study: Interpreting AI Decisions in Healthcare and Finance
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Our Courses
Data Analyst using Python
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Placement Assistance
Placement assistance offered for a successful career.
Membership
Membership provided until the final examination.
Personalized Attention
Personalized attention provided to each student.
Get Course Certificate
Certificate awarded upon completion of the course.
Monthly Tests
Regular monthly test series for progress evaluation.
Latest CBSE Syllabus
Training modules aligned with the latest CBSE syllabus.
Frequently Asked Questions
Anyone with basic Python knowledge and interest in AI can join.
Pandas, NumPy, Scikit-learn, TensorFlow, Keras, NLTK, spaCy, OpenCV, and Gym.
No, prior AI experience is not necessary but helpful.
AI Engineer, Data Scientist, NLP Specialist, or Machine Learning Engineer.
The course typically lasts 5-6 months.
Entry-level positions can offer salaries ranging from $60,000 to $90,000 annually, while experienced professionals can earn $100,000 or more.
Graduates can pursue roles such as AI Engineer, Data Scientist, Machine Learning Engineer, and NLP Specialist.
The demand for AI professionals is rapidly growing, with many industries adopting AI technologies, ensuring a robust job market.
Yes, the course includes hands-on projects that allow students to apply theoretical knowledge to real-world AI challenges.
