Specialization Course in Machine Learning

Unlock the power of artificial intelligence and transform your career with our comprehensive Specialization Course in Machine Learning. This cutting-edge program is designed to equip you with the essential skills and knowledge needed to excel in the rapidly evolving field of machine learning and data science.

30- Days | 30+ hours of learning | Training by best Industry Experts

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Covered Topics

About this Specialization

Throughout this intensive course, you'll dive deep into the fundamentals of machine learning algorithms, statistical analysis, and predictive modeling. Our expert instructors will guide you through hands-on projects using popular frameworks like TensorFlow, PyTorch, and scikit-learn, ensuring you gain practical experience with real-world applications. You'll learn to develop sophisticated machine learning models, implement neural networks, and master the art of data preprocessing and feature engineering.

The curriculum covers crucial topics including supervised and unsupervised learning, deep learning, natural language processing, and computer vision. You'll also explore advanced concepts such as reinforcement learning and ensemble methods, setting you apart in the competitive job market. Our course emphasizes practical implementation, allowing you to build a robust portfolio of projects that demonstrate your expertise to potential employers.

Whether you're a professional looking to upgrade your skills or a newcomer eager to break into the field of AI, this specialization course provides the perfect blend of theoretical knowledge and practical application. With flexible learning options, one-on-one mentoring sessions, and a supportive community of fellow learners, you'll have everything you need to succeed.

Don't miss this opportunity to become a skilled machine learning practitioner. Join our Specialization Course in Machine Learning and position yourself at the forefront of the AI revolution. Enroll now to secure your future in this high-demand, lucrative field!

How the Specialization Works

Take Courses

Prutor.ai Specialization is a series of courses that help you master a skill. To begin, enroll in the Specialization directly, or review its courses and choose the one you'd like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. It’s okay to complete just one course — you can pause your learning or end your subscription at any time. Visit your learner dashboard to track your course enrollments and your progress.

Hands-on Project

Every Specialization includes a hands-on project. You'll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you'll need to finish each of the other courses before you can start it.

Earn a Certificate

When you finish every course and complete the hands-on project, you'll earn a Certificate that you can share with prospective employers and your professional network.

What You Benefit from This Program

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Courses in this Specialization.  Day-wise Plan

Day 1. Introduction to Machine Learning
Day 2. Data Collection and Cleaning
Day 3. Exploratory Data Analysis(EDA)
Day 4. Feature Engineering
Day 5. Model Selection and Training
Day 6. Model Evaluation
Day 7. Model Hyperparameter Tuning
Day 8. Linear Regression
Day 9. Polynomial Regression
Day 10. Ridge Lasso and ElasticNet Regularization
Day 11. Logistic Regression
Day 12. Decision Tree
Day 13. Ensemble Learning
Day 14. Random Forest
Day 15. Support Vector Machines(SVM)

 

 

 

Day 16. K-Nearest Neighbors(KNN)
Day 17. Naive Bayes
Day 18. Gradiant Boosting
Day 19. K Means Clustering
Day 20. DBSCAN
Day 21. Hierarchical Clustering
Day 22. Principal Component Analysis (PCA)
Day 23. T-Distributed Stochastic Neighbor Embedding (t-SNE)
Day 24. Perceptrons & Multi-Layer Perceptrons (MLPs)
Day 25. Activation Functions
Day 26. Forward and Backward Propagation
Day 27. Optimizers
Day 28. Convolutional Neural Networks(CNNs)
Day 29. Recurrent Neural Networks(RNNs)