Machine Learning using Python​

Machine learning is gaining popularity across various industries, such as finance, healthcare, marketing, manufacturing, automotive, e-commerce, and media. Like doctors reading your past medical history and current symptoms can predict illness, stock brokers see past trends to invest in stocks, and machines can also predict situations by considering past experiences with machine learning algorithms. 

This course covers the basic understanding of machine learning, different types of machine learning algorithms, the use of contemporary tools and technologies, and implementing knowledge into real-time projects. With proper knowledge of machine learning, learners can create fraud detection and cyber resilience solutions, digital assistants, image recognition tools, spam filters, sentiment analysis tools and more, solving existing problems in the market.

What you will learn

• Basic understanding of machine learning ( history, types, and machine learning activities)
• Knowledge of feature engineering (feature engineering methods, construction methods, standardisation, handling categorical variables)
• Concepts of probability and statistics (density function, continuous distribution, exponential distribution, Poisson distribution)
• Understanding the difference between supervised and unsupervised learning
• Knowledge of linear models, decision trees, random forests, K-nearest neighbours, Bayesian concept
• Hands-on training on building two mobile applications for Linear Classifier and NativeBayesClassifier

What’s covered in this online Machine Learning using Python course​

1. Machine Learning Landscape – 5 Lessons, 1 Quiz
2. Feature Engineering – 9 Lessons, 1 Quiz
3. Thinking Statistically in Machine Learning – 8 Lessons, 1 Quiz
4. Modelling and Evaluation – 3 Lessons, 1 Quiz
5. Supervised Learning: Regression – 10 Lessons, 1 Quiz
6. Regularized Linear Models – 10 Lessons, 1 Quiz
7. Supervised Learning: Classification – 10 Lessons, 1 Quiz
8. Decision Trees – 7 Lessons, 1 Quiz
9. Ensemble Learning and Random Forests – 18 Lessons, 0 Quizzes
10. Support Vector Machines – 8 Lessons, 0 Quizzes
11. Bayesian Concept Learning – 5 Lessons, 0 Quizzes
12. K-Nearest Neighbors – 4 Lessons, 0 Quizzes
13. Unsupervised Learning – 14 Lessons, 0 Quizzes
14. Project 1: Building Mobile Application for Linear Classifier – 6 Lessons, 0 Quizzes
15. Project 2: Building Mobile App for NaiveBayesClassifier – 4 Lessons, 0 Quizzes