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Generative AI Course, Generative AI Training from Apponix Academy
Generative AI Course, Generative AI Training from Apponix Academy
This program equips you with the knowledge and skills needed to excel in the field of Generative AI. Gain practical insights and industry expertise to confidently develop, implement, and innovate with AI-driven solutions.
Generative AI Course Course Content
MODULE 1: Python Basics & Core Programming (15 Hours)
Introduction to Python (2 Hours)
- What is Python?
- Installation & Setup (Anaconda, Jupyter Notebook, VS Code)
- Running Python Programs (Interactive & Script Mode)
Python Fundamentals (5 Hours)
- Variables & Data Types
- Operators (Arithmetic, Logical, Relational)
- Conditional Statements (if-else)
- Loops (for, while)
- Functions (Types, Arguments, Lambda Functions)
Data Structures in Python (5 Hours)
- Strings: Operations, Slicing, Methods
- Lists: Accessing, Modifying, Methods, List Comprehensions
- Dictionaries: Key-Value Pairs, Operations
- Sets: Set Operations, Built-in Functions
Object-Oriented Programming (3 Hours)
- Classes & Objects
- Inheritance & Polymorphism
- Encapsulation & Data Hiding
MODULE 2: Python for Data Science (15 Hours)
Working with Files & Exception Handling (2 Hours)
- File Handling (Read, Write, Append)
- Exception Handling (try-except, finally)
NumPy for Data Manipulation (4 Hours)
- Arrays vs Lists
- Creating & Modifying Arrays
- Array Indexing & Slicing
- Mathematical Operations
Pandas for Data Handling (5 Hours)
- DataFrames & Series
- Importing Datasets (CSV, JSON, Excel)
- Data Cleaning & Handling Missing Data
Data Visualization using Matplotlib & Seaborn (4 Hours)
- Line Charts, Bar Graphs, Pie Charts
- Histograms, Scatter Plots, Heatmaps
MODULE 3: AI & Machine Learning (50 Hours)
Introduction to AI & ML (3 Hours)
- AI vs ML vs Deep Learning
- Supervised vs Unsupervised Learning
- Real-World Applications
Supervised Learning Algorithms (12 Hours)
- Linear & Logistic Regression
- Decision Trees & Random Forest
- Support Vector Machines (SVM)
- Naïve Bayes Classifier
Unsupervised Learning Algorithms (7 Hours)
- K-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis (PCA)
Deep Learning with TensorFlow & Keras (14 Hours)
- Introduction to Neural Networks
- Activation Functions
- Building Artificial Neural Networks (ANN)
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
Natural Language Processing (NLP) (8 Hours)
- Text Preprocessing (Tokenization, Lemmatization)
- Bag of Words & TF-IDF
- Named Entity Recognition (NER)
- Sentiment Analysis
Reinforcement Learning (6 Hours)
- Agents & Environment
- Q-Learning Algorithm