FEATURES OF THIS COURSE
Artificial Intelligence (AI) is the simulation of human intelligence in machines that are programmed to think and act like humans. These machines are designed to perceive their environment, learn from the data they encounter, and make decisions based on their learning. AI encompasses a wide range of technologies, including machine learning, natural language processing, computer vision, and robotics.
Machine Learning (ML), a subset of AI, focuses on developing algorithms that allow computers to learn from data and improve their performance over time without being explicitly programmed. ML algorithms analyze large datasets to identify patterns and make predictions or decisions. By using techniques such as neural networks and deep learning, ML enables computers to perform tasks ranging from recognizing speech and images to recommending products and optimizing processes. Together, AI and ML are revolutionizing industries by automating tasks, enhancing decision-making, and enabling innovations that were once thought impossible.
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MAX STUDENT : 10 AT A TIME
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SKILL LEVEL: BASIC TO ADVANCE
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DURATION : 6 MONTHS
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COURSE FEE : INR 12,000
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CERTIFICATE : YES
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PRACTICAL TRAINING : YES
AI AND MACHINE LEARNING TRAINING PERIOD CONTENT
MONTH 1
Month 1: Introduction to AI and Machine Learning
- Introduction to Artificial Intelligence (AI) and Machine Learning (ML)
- Basics of Python Programming Language
- Understanding Data Types, Variables, and Operators in Python
- Introduction to NumPy and Pandas Libraries for Data Manipulation
- Introduction to Jupyter Notebooks for Interactive Coding
MONTH 2
Month 2: Data Preprocessing and Exploratory Data Analysis (EDA)
- Data Preprocessing Techniques: Data Cleaning, Imputation, and Transformation
- Exploratory Data Analysis (EDA): Data Visualization with Matplotlib and Seaborn
- Statistical Analysis and Descriptive Statistics
- Feature Engineering: Feature Selection and Extraction Techniques
MONTH 3
Month 3: Supervised Learning Algorithms
- Introduction to Supervised Learning and its Types
- Linear Regression: Theory, Implementation, and Evaluation Metrics
- Logistic Regression.
- Decision Trees and Ensemble Learning Techniques
- Support Vector Machines (SVM) for Classification and Regression
MONTH 4
Month 4: Unsupervised Learning Algorithms
- Introduction to Unsupervised Learning and its Types
- K-Means Clustering: Theory, Implementation, and Evaluation Metrics
- Hierarchical Clustering
- Principal Component Analysis (PCA) for Dimensionality Reduction
- Anomaly Detection Techniques
MONTH 5
Month 5: Neural Networks and Deep Learning
- Introduction to Neural Networks
- Building Neural Networks with TensorFlow and Keras
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs) for Sequence Prediction
- Transfer Learning and Fine-Tuning Pretrained Models
MONTH 6
Month 6: Advanced Topics and Capstone Project
- Introduction to Advanced Topics in AI and Machine Learning (Reinforcement Learning, Natural Language Processing, etc.)
- Real-world Applications and Case Studies of AI and ML
- Ethical and Social Implications of AI
- Capstone Project
- Project Presentation and Evaluation
IN ADDITION TO THAT, We will focus on specific topics and gradually build upon the foundational knowledge, allowing participants to gain a comprehensive understanding of AI and Machine Learning concepts and techniques by the end of the 6-month training period.
