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Artificial Intelligence Expert!
Upgrade your coding skills with Placement Point Solutions’ Best-Selling Machine Learning Course With Artificial Intelligence
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- Learn from Industry Experts
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- Chennai’s #1 Machine Learning Course With Artificial Intelligence Training institute
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Discover a diverse range of courses designed to equip you with real-world skills and industry-relevant knowledge.
Why Study Machine Learning Artificial Intelligence
ML & AI drive automation, innovation, and smart decision-making
across industries. With growing demand in tech, healthcare, finance,
and more, mastering AI opens doors to exciting career opportunities!
Benefits of Learning Machine Learning Artificial Intelligence
✨ High Demand – Growing job opportunities across industries.
✨ Automation & Efficiency – Enhances productivity and decisionmaking.
✨ Innovation & Future Tech – Powers AI, robotics, and smart applications.
✨ Big Data & Analytics – Helps analyze and interpret massive datasets.
✨ Career Growth – High-paying roles in tech, healthcare, and finance.
✨ Problem-Solving – Solves complex real-world challenges.
✨ Global Impact – Drives advancements in science, business, and society
Future Scope of Learning Machine Learning Artificial Intelligence
✨ High Demand – Expanding in healthcare, finance, and automation.
✨ AI-Powered Innovation – Driving advancements in robotics, NLP, and deep learning.
✨ Big Data & Predictive Analytics – Essential for data-driven decisionmaking.
✨ Smart Automation – Transforming industries with AI-driven solutions.
✨ IOT & Edge Computing – Enhancing real-time AI applications.
✨ Ethical AI & Regulation – Growing focus on responsible AI development.
✨ Endless Career Opportunities – AI specialists are among the most sought-after professionals.
Kickstart Your Career with Machine Learning Artificial Intelligence
With its endless applications and growing demand, Machine Learning
& Artificial Intelligence are the keys to a successful tech career. Enroll
in a course today and step into a world of innovation and
opportunity!
key features
Course Duration : 40 hours
Intensive, focused training designed to quickly upskill you in a short timeframe.
100% Job-Oriented Training
Curriculum tailored to industry requirements, ensuring you gain the skills employers are looking for.
Industry Expert Faculties
Learn from seasoned professionals with real-world experience.
Flexible Demo Class Available
Opportunity to attend a demo session and experience the training style before enrolling.
Completed 400+ Batches
Proven track record with hundreds of successful batches, demonstrating program reliability and effectiveness.
Certification Guidance
Support throughout your journey to help you earn relevant certifications that boost your career.
Live Practical Sessions (if needed)
Hands-on, interactive sessions to reinforce learning and apply concepts in real-time.
Peaceful Environment
A conducive learning atmosphere that helps you focus and maximize your learning potential.
Syllabus of Machine Learning Artificial Intelligence
- Overview & History:
Evolution of Machine Learning and AI
Key milestones and breakthroughs - Fundamental Concepts:
Definitions and differences between ML, AI, Deep Learning, and Data Science
AI paradigms: Symbolic AI vs. Statistical AI - Tools & Environment:
Setting up Python/Anaconda, Jupyter Notebooks, and relevant libraries
Overview of popular frameworks (TensorFlow, PyTorch, Scikit-Learn)
- Data Collection & Storage:
Data sources and data types (structured, unstructured)
Introduction to databases, APIs, and web scraping - Data Cleaning & Preprocessing:
Handling missing data, outliers, and noise
Feature scaling, normalization, and encoding categorical variables - Exploratory Data Analysis (EDA):
Statistical summaries and visualizations (using Matplotlib, Seaborn, Plotly)
Data visualization best practices
- Regression Techniques:
Linear Regression, Polynomial Regression
Performance metrics (MSE, RMSE, MAE, R²) - Classification Techniques:
Logistic Regression, k-Nearest Neighbors (k-NN)
Decision Trees, Random Forests
Evaluation metrics (Accuracy, Precision, Recall, F1-Score, ROC Curve) - Hands-on Implementation:
Building and validating models using Scikit-Learn PPS
Case studies and real-world examples
- Clustering Techniques:
K-Means, Hierarchical Clustering, DBSCAN - Dimensionality Reduction:
Principal Component Analysis (PCA)
t-Distributed Stochastic Neighbor Embedding (t-SNE) - Association Rule Learning:
Apriori and market basket analysis - Practical Exercises:
Uncovering hidden patterns in data through clustering and reduction techniques
- Ensemble Methods:
Bagging, Boosting (AdaBoost, Gradient Boosting, XGBoost)
Stacking and blending - Support Vector Machines (SVM):
Theory, kernel functions, and applications - Model Optimization:
Cross-validation techniques
Hyperparameter tuning (Grid Search, Random Search) - Real-world Applications:
Implementing advanced models on complex datasets
- Introduction to Neural Networks:
Perceptrons, activation functions, and backpropagation
Network architectures and learning dynamics - Deep Learning Frameworks:
Overview of TensorFlow and PyTorch - Convolutional Neural Networks (CNNs):
Architecture, use cases in image recognition, and transfer learning - Recurrent Neural Networks (RNNs):
Sequence modeling, Long Short-Term Memory (LSTM) networks, and GRUs - Hands-on Projects: PPS
Building and training deep neural networks for image and text data
- Reinforcement Learning (RL):
Core concepts: Agents, Environment, Rewards, Policies
Key algorithms: Q-Learning, Deep Q-Networks (DQN), Policy Gradients - Advanced AI Topics:
Generative Adversarial Networks (GANs)
Transfer Learning and Meta-Learning - Application Areas:
Robotics, game-playing AI, and decision-making systems - Practical Sessions:
Developing simple RL environments and experimenting with state-of-the-art algorithms
- NLP Fundamentals:
Text preprocessing, tokenization, and vectorization (Bag-of-Words, TF-IDF)
Word embeddings (Word2Vec, GloVe) and sequence modeling - NLP Applications:
Sentiment analysis, text classification, and topic modeling
Introduction to transformer models (BERT, GPT) - Computer Vision:
Image processing techniques, feature extraction, and object detection
Hands-on with CNNs for image classification and segmentation - Integration Projects:
Building end-to-end NLP or Computer Vision pipelines
-
Ethics in AI:
Fairness, transparency, and bias mitigation
Ethical considerations and societal impacts of AI systems Explainable AI (XAI):
Methods for interpreting model predictions
Tools and techniques (LIME, SHAP)Deployment Strategies: PPS
Model serialization and serving (using Flask, FastAPI, or cloud services)
MLOps basics for continuous integration and delivery of ML modelsMonitoring & Maintenance:
Tracking model performance in production and retraining strategies
- Project Planning:
Problem identification, data collection, and scope definition - Project Execution:
End-to-end project development incorporating data preprocessing, model building, and evaluation - Deployment & Presentation:
Deploying the final solution and preparing a comprehensive project report
Peer reviews and presentations - Industry Trends & Future Directions:
Emerging trends (AutoML, Explainable AI, MLOps) and research directions in AI
Career guidance and portfolio development
Upcoming Batches
Online Classroom
Corporate Training
Be a Certified Expert in Machine Learning Artificial Intelligence
Accredited by all major Global Companies around the world. We provide after completion of the theoretical and practical sessions to fresher's as well as corporate trainees.
Our certification is accredited worldwide. It increases the value of your resume and you can attain leading job posts with the help of this certification in leading MNC's of the world. The certification is only provided after successful completion of our training and practical based projects.
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