MACHINE LEARNING Education Courses Professional Sessions
Introduction :
1. Getting Started with Machine Learning
2. An Introduction to Machine Learning
3. What is Machine Learning ?
4. Introduction to Data in Machine Learning
5. Demystifying Machine Learning
6. ML – Applications
7. Best Python libraries for Machine Learning
8. Artificial Intelligence | An Introduction
9. Machine Learning and Artificial Intelligence
10. Difference between Machine learning and Artificial Intelligence
11. Agents in Artificial Intelligence
12. 10 Basic Machine Learning Interview Questions
Data and It’s Processing:
1. Introduction to Data in Machine Learning
2. Understanding Data Processing
3. Python | Create Test DataSets using Sklearn
4. Python | Generate test datasets for Machine learning
5. Python | Data Preprocessing in Python
6. Data Cleansing
7. Feature Scaling – Part 1
8. Feature Scaling – Part 2
9. Python | Label Encoding of datasets
10. Python | One Hot Encoding of datasets
11. Handling Imbalanced Data with SMOTE and Near Miss Algorithm in Python
Supervised learning :
1. Getting started with Classification
2. Basic Concept of Classification
3. Types of Regression Techniques
4. Classification vs Regression
5. ML | Types of Learning – Supervised Learning
6. Multiclass classification using scikit-learn
7. Gradient Descent :
• Gradient Descent algorithm and its variants
• Stochastic Gradient Descent (SGD)
• Mini-Batch Gradient Descent with Python
• Optimization techniques for Gradient Descent
• Introduction to Momentum-based Gradient Optimizer
8. Linear Regression :
• Introduction to Linear Regression
• Gradient Descent in Linear Regression
• Mathematical explanation for Linear Regression working
• Normal Equation in Linear Regression
• Linear Regression (Python Implementation)
• Simple Linear-Regression using R
• Univariate Linear Regression in Python
• Multiple Linear Regression using Python
• Multiple Linear Regression using R
• Locally weighted Linear Regression
• Python | Linear Regression using sklearn
• Linear Regression Using Tensorflow
• A Practical approach to Simple Linear Regression using R
• Linear Regression using PyTorch
• Pyspark | Linear regression using Apache MLlib
• ML | Boston Housing Kaggle Challenge with Linear Regression
9. Python | Implementation of Polynomial Regression
10. Softmax Regression using TensorFlow
11. Logistic Regression :
• Understanding Logistic Regression
• Why Logistic Regression in Classification ?
• Logistic Regression using Python
• Cost function in Logistic Regression
• Logistic Regression using Tensorflow
12. Naive Bayes Classifiers
13. Support Vector:
• Support Vector Machines(SVMs) in Python
• SVM Hyperparameter Tuning using GridSearchCV
• Support Vector Machines(SVMs) in R
• Using SVM to perform classification on a non-linear dataset
14. Decision Tree:
• Decision Tree
• Decision Tree Regression using sklearn
• Decision Tree Introduction with example
• Decision tree implementation using Python
• Decision Tree in Software Engineering
15. Random Forest:
• Random Forest Regression in Python
• Ensemble Classifier
• Voting Classifier using Sklearn
• Bagging classifier
What is the Machine Learning Course?
The Machine Learning Course is designed to provide a comprehensive understanding of the principles and techniques used in machine learning. This course covers essential concepts, algorithms, and tools, enabling you to build predictive models and analyze data effectively.
Why Choose Machine Learning?
Machine learning is a rapidly growing field that powers many of today’s technological advancements. By mastering machine learning, you will be able to:
- Extract Insights from Data: Learn how to analyze large datasets and uncover hidden patterns.
- Develop Predictive Models: Create models that can make accurate predictions based on historical data.
- Enhance Your Career Opportunities: Machine learning skills are in high demand across various industries, from finance to healthcare.
Growing Demand for Machine Learning Professionals
As organizations increasingly rely on data-driven decision-making, the demand for skilled machine learning professionals continues to rise. Industry reports indicate that the job market for machine learning specialists is expanding rapidly, making this course a strategic investment in your career.
Skills You Will Acquire
In the Machine Learning Course, you will explore:
- Introduction to Machine Learning: Understand the fundamentals, including supervised, unsupervised, and reinforcement learning.
- Data Preprocessing: Learn techniques for cleaning and preparing data for analysis.
- Algorithms and Models: Explore various algorithms, including linear regression, decision trees, and neural networks.
- Model Evaluation: Gain skills in assessing model performance using metrics such as accuracy, precision, and recall.
- Deep Learning: Dive into advanced topics like neural networks and deep learning frameworks (e.g., TensorFlow, Keras).
- Real-World Applications: Discover how to apply machine learning in areas such as natural language processing, computer vision, and recommendation systems.
Career Opportunities After the Course
Completing this course can lead to various career paths, including:
- Machine Learning Engineer
- Data Scientist
- AI Researcher
- Data Analyst
- Business Intelligence Analyst
Salaries for machine learning professionals can range from $90,000 to over $150,000 annually, depending on experience and specialization.
Why Choose Our Institute?
At CampusBuddy, we provide:
- Hands-On Learning: Engage in practical projects that enhance your understanding and application of machine learning concepts.
- Expert Instructors: Learn from industry professionals who share valuable insights and best practices.
- Flexible Learning Options: Choose between online or in-person classes to fit your schedule.
- Career Support: Benefit from job placement assistance and a strong network of industry connections.
Start Your Machine Learning Journey Today!
Enroll in our Machine Learning Course and gain the essential skills needed to excel in this exciting and impactful field. With the knowledge you acquire, you’ll be well-prepared to tackle real-world challenges and advance your career in data science and artificial intelligence.