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Machine Learning
Mastering Machine Learning: From Basics to Advanced Applications
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Mastering Machine Learning: From Basics to Advanced Applications
This comprehensive course provides an in-depth exploration of Machine Learning (ML), equipping learners with the skills to design, build, and deploy intelligent systems. Starting with foundational concepts, the course covers key techniques such as supervised and unsupervised learning, reinforcement learning, and neural networks. Participants will gain hands-on experience with real-world datasets, implementing algorithms like regression, classification, clustering, and decision trees using popular tools such as Python, TensorFlow, and Scikit-learn.
The curriculum also dives into advanced topics like deep learning, model optimization, and natural language processing, making it suitable for both beginners and professionals. By the end of the course, learners will be capable of solving complex problems, developing innovative AI solutions, and contributing to cutting-edge projects in industries such as finance, healthcare, e-commerce, and more.
WHAT WE DO
We have a designed Flexible Program for You!
We get it—life can be unpredictable. That’s why our program is built to fit around your schedule, not the other way around. Whether you need to catch up on missed classes, take a break for personal reasons, or just want to revisit the material, we’re here to make learning work for you.
missed a class ?
Watch the recording later, with teaching assistants available to solve your doubts
Work / family needs time?
Pause your course and restart a month later with the next batch!
Have doubts?
Get them resolved over text / video by our expert teaching assistants!
Want to revise?
Access assignments/notes lifelong and recordings upto 6 months post course completion
Easy Registration
In <2 minutes, make a new account or login using social media / Interviewbit
Quick Evaluation
Simple 30 minutes MCQ test, focused on aptitude and basic coding to find the right course for you
Enroll in your course
Sign up with our various EMI options to swiftly kickstart your learning journey
Upgrade your batch
Want a shorter course? Take the coding challenge after enrollment
Why Enroll in This Course?
- Hands-on learning with Python, TensorFlow, and Scikit-learn
- Real-world datasets and industry-focused projects
- Learn from experts and gain global certification readiness
- Deploy AI models for practical business solutions
🚀 Start mastering Machine Learning today and build a future in AI!
Machine Learning Curriculum
- What is Machine Learning? Overview and applications
- Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
- Understanding AI, ML, and Deep Learning differences
- Setting up the ML environment (Python, Jupyter, Anaconda)
- Collecting and cleaning data
- Handling missing values, outliers, and categorical data
- Feature scaling and transformation techniques
- Feature selection and dimensionality reduction (PCA, LDA)
- Regression models: Linear Regression, Polynomial Regression, Ridge & Lasso Regression
- Classification models: Logistic Regression, Decision Trees, Random Forest, SVM, KNN
- Model evaluation techniques (Confusion Matrix, Precision-Recall, ROC-AUC)
- Hyperparameter tuning and optimization
- K-Means Clustering & Hierarchical Clustering
- DBSCAN and Gaussian Mixture Models
- Principal Component Analysis (PCA) for dimensionality reduction
- Market Basket Analysis & Association Rule Learning (Apriori, FP-Growth)
- Introduction to Artificial Neural Networks (ANN)
- Building Deep Neural Networks (DNN) using TensorFlow and Keras
- Convolutional Neural Networks (CNN) for image processing
- Recurrent Neural Networks (RNN) for time-series and NLP
- Introduction to Reinforcement Learning (Markov Decision Processes)
- Q-Learning and Deep Q-Networks (DQN)
- Generative Adversarial Networks (GANs)
- Transfer Learning & AutoML
- Text preprocessing and tokenization
- Sentiment analysis and text classification
- Named Entity Recognition (NER)
- Transformers & Large Language Models (BERT, GPT)
- Deploying models using Flask & FastAPI
- Using TensorFlow Serving & Docker for deployment
- ML model deployment on cloud (AWS, GCP, Azure)
- Real-world case studies (finance, healthcare, e-commerce)
- End-to-end ML project with real-world data
- Model evaluation, fine-tuning, and performance optimization
- Preparing for industry certifications (TensorFlow Developer, AWS ML, Microsoft AI)
- Resume building & job interview preparation