Machine Learning
Unlock the potential of data with our comprehensive Machine Learning course. Dive into the world of algorithms, models, and predictive analytics, mastering techniques to extract insights and drive decision-making. From beginner fundamentals to advanced applications, empower yourself with the skills to harness the power of machine learning for real-world solutions.
Original price was: ₹2,500.00.₹1,199.00Current price is: ₹1,199.00.
About This Course
A Machine Learning course provides an in-depth understanding of algorithms and statistical models that enable computers to learn from and make predictions based on data. The curriculum typically covers foundational concepts such as supervised and unsupervised learning, regression, classification, clustering, and neural networks.
Additionally, Students will explore advanced topics like deep learning, natural language processing, and reinforcement learning, preparing them for complex problem-solving in diverse fields such as finance, healthcare, marketing, and robotics. By the end of the course, graduates will be equipped with the skills needed to pursue careers as Machine Learning Engineers, Data Scientists, or AI Researchers, contributing to innovative solutions and data-driven decision-making
Course Content
Module 1 - Introduction to Machine Learning
Definition and basic concepts
Types of machine learning: supervised, unsupervised, and reinforcement learning
Module 2 - Mathematics for Machine Learning
Linear algebra
Calculus
Probability and statistics
Module 3 – Python Programming
Basics of Python
Libraries for data manipulation and visualization: NumPy, Pandas, matplotlib, and Seaborn
Module 4 - Data Preprocessing
Data cleaning
Feature scaling
Handling missing data
Module 5 - Supervised Learning
Linear regression
Logistic regression
Decision trees
Random forests
Module 6 - Model Evaluation and Validation
Cross-validation
Bias-variance tradeoff
Evaluation metrics: accuracy, precision, recall, F1-score
Module 7 - Unsupervised Learning
K-means clustering
Hierarchical clustering
Principal Component Analysis (PCA)
Module 8 - Feature Engineering
Feature selection
Feature extraction
Dimensionality reduction techniques
Module 9 - Advanced Regression Techniques
Ridge regression
Lasso regression
ElasticNet regression
Module 10 - Natural Language Processing (NLP)
Text preprocessing
Bag-of-Words model
Word embeddings: Word2Vec, GloVe
Module 11 - Deep Learning Fundamentals
Neural network architecture
Activation functions
Backpropagation algorithm
Module 12 - Convolutional Neural Networks (CNNs)
Architecture of CNNs
Convolutional layers
Pooling layers
Module 13 - Recurrent Neural Networks (RNNs)
Architecture of RNNs
Long Short-Term Memory (LSTM)
Gated Recurrent Units (GRU)
Module 14 - Generative Adversarial Networks (GANs)
Introduction to GANs
Training GANs
Applications of GANs
Module 15 - Deployment and Model Optimization
Model deployment using frameworks like TensorFlow Serving or Flask
Model optimization techniques
Scaling machine learning models
Module 16 - Special Topics in Machine Learning
Reinforcement learning algorithms: Q-learning, Deep Q Network (DQN)
Time series forecasting
Anomaly detection techniques
Module 17 - How to use Kaggle
How to use Kaggle
Project
Spam Classifier
Sentiment Analysis
Earn A Certificate
Earning a certificate from Vital Skills enhances your professional credentials and expertise, boosting your career opportunities. It also fosters personal growth and confidence in your abilities
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Practical Demosntration
Project Based Learning
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FAQs (Frequently Asked Questions)
Do I need Prior experience in programming?
Courses are designed from scratch by professionals. No prior knowledge is needed.
How is the course delivered?
The course is usually delivered through a combination of video lectures and projects.
What are the career prospects after completing the course?
Career prospects for machine learning students include roles such as Machine Learning Engineer, Data Scientist, and AI Research Scientist, where they design and implement algorithms to solve complex problems. Opportunities span various industries, including technology, finance, healthcare, and e-commerce, often offering high salaries and demand for skilled professionals.
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Total Duration
Total Lessons
Course Validity
Level
Job Opportunities
Target Audience
- Students
- Business Analysts
- Marketing Professionals
- Financial Analysts
- Healthcare
- Professionals
- HR Professionals
- Researchers
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