Course
Understand Core Concepts of Machine Learning and Artificial IntelligenceDevelop Proficiency in Python for Data Analysis and ModelingApply Supervised and Unsupervised Learning TechniquesBuild and Train Deep Learning Models Using TensorFlow and PyTorchImplement Real-World Machine Learning Projects and Case StudiesAnalyze and Visualize Data for Predictive InsightsEvaluate and Optimize Machine Learning Models for AccuracyUnderstand Natural Language Processing and Reinforcement LearningDeploy Machine Learning Models into Real-Time ApplicationsGain Industry-Recognized Certification and Job-Ready Skills
Course Description
This course provides a comprehensive introduction to Machine Learning (ML), covering key concepts such as Supervised & Unsupervised Learning, Neural
Networks, Deep Learning, and Model Deployment. Students will learn practical ML techniques, implement algorithms, and build real-world projects.
Modules
- Introduction to Machine Learning & Data Preprocessing
- Supervised Learning – Regression & Classification
- Unsupervised Learning – Clustering & Dimensionality Reduction
- Neural Networks & Deep Learning
- Model Deployment & ML in Production
- Advanced Topics & Capstone Project
Projects
- Perform data cleaning & visualization on a real-world dataset using Pandas & Matplotlib
- Build a House Price Prediction Model using Linear Regression
- Implement a Spam Email Classifier using Logistic Regression
- Perform Customer Segmentation using K-Means Clustering
- Apply PCA for feature reduction on a dataset
- Implement a Digit Recognition System using CNN
- Train a Neural Network for Sentiment Analysis on movie reviews
- Deploy a Machine Learning model as a REST API using Flask
- Optimize a model using Grid Search & Hyperparameter Tuning
- Develop a real-world ML project on a dataset of your choice
- Present your Capstone Project with model performance evaluation
Assignments
- Perform data cleaning & visualization on a real-world dataset using Pandas & Matplotlib
- Build a House Price Prediction Model using Linear Regression
- Implement a Spam Email Classifier using Logistic Regression
- Perform Customer Segmentation using K-Means Clustering
- Apply PCA for feature reduction on a dataset
- Implement a Digit Recognition System using CNN
- Train a Neural Network for Sentiment Analysis on movie reviews
- Deploy a Machine Learning model as a REST API using Flask
- Optimize a model using Grid Search & Hyperparameter Tuning
- Develop a real-world ML project on a dataset of your choice
- Present your Capstone Project with model performance evaluation
Certificate
Practical AI-integrated Project (40%)
Final Online Test (60%)
Nxt Certified Machine Learning Program Certificate awarded
upon successful completion.
Career Opportunities
- Job Roles:
- Machine Learning Engineer – Build and optimize ML models.
- Data Scientist – Analyze complex data to extract insights.
- AI Research Scientist – Develop AI-based applications.
- Computer Vision Engineer – Work on AI-driven image
- recognition systems.
- Freelancing Opportunities:
- Building custom ML models for businesses.
- Providing data analysis & visualization services.
- Developing AI-based chatbots & automation tools.
- Startup Opportunities:
- Creating AI-powered recommendation systems.
- Developing AI-driven fraud detection & cybersecurity solutions.
- Launching a ML-based SaaS product.
Download Course Brochure
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