About the course
This course on Machine Learning through Python will help you to understand how problem-solving occurs in real life machine learning applications. In this course, you will use a Python-based toolbox known as scikits learn, to perform the hands-on practice. In this course, you will understand Linear Regression, Logistic Regression models with the help of python programming exercises. Then, you will understand the decision trees along with k nearest neighbor and principal component analysis with the help of Python exercises. After that, you will understand naive Bayes followed by support vector machine with the help of exercises on python. Finally, you will learn about neural network and k means clustering through exercises on python programming.
After completing this course, you will be able to:
- Understand about the problem-solving in real-world machine learning applications.
- Understand machine learning principles and concepts through python.
- Implement these learning in real life machine learning applications.
- Boost your hireability through innovative and independent learning.
- Get a certificate on successful completion of the course.
The course can be taken by:
Students: All students who are pursuing any technical/professional courses related to IT/Machine Learning.
Teachers/Faculties: All teachers/faculties who wish to acquire new skills or improve their efficiency in Machine Learning.
Professionals: All working professionals from the field of Machine Learning domain.
Why learn Machine Learning through Python?
Machine Learning is currently one of the hottest topics in IT. Technologies such as digital, big data, Artificial Intelligence, automation, and machine learning are increasingly shaping the future of work and jobs. It is a specific set of techniques that enable machines to learn from data, and make predictions. When the biases of our past and present fuel the predictions of the future, it's a tall order to expect AI to operate independently of human flaws. Whether financial institutions are looking for improved customer service, risk management, fraud prevention, investment prediction or cybersecurity, the scopes of machine learning and artificial intelligence are limitless. In the modern era of the digital economy, technological advancements are no longer a luxury for the organizations, but a necessity to outsmart their competitors and business growth. With the technological advancements in the recent times, the impact of Machine Learning (ML) and Artificial Intelligence (AI) are very critical than ever before. According to KellyOCG India, demand for Artificial Intelligence and Machine Learning specialists in the country are expected to see a 60% rise by 2018 due to increasing adoption of automation.
- 24X7 Access: You can view lectures as per your own convenience.
- Online lectures: 6 hours of online lectures with high-quality videos.
- Updated Quality content: Content is latest and gets updated regularly to meet the current industry demands.
There will be a final test containing a set of multiple choice questions. Your evaluation will include the scores achieved in the final test.
Certification requires you to complete the final test. Your certificate will be generated online after successful completion of course.
Topics to be covered
- Module 1: Python Exercise on Decision Tree and Linear Regression
- Python exercise on linear regression
- Python exercise on logistic regression
- Python exercise on decision tree regression
- Module 2: Tutorial I
- How to solve a sample problem in Linear Regression?
- How to solve problems related to Decision Trees?
- How to find the entropy of a set and use in decision trees?
- What is the information gain?
- Module 3: Python Exercise on KNN and PCA
- How do we use K-Neighbors Classifier in Python?
- How do we use Randomized PCA in Python?
- How can we do Face recognition using PCA and KNN?
- Module 4: Tutorial II
- What is the curse of dimensionality?
- What is feature selection?
- What is feature reduction and PCA? (principal component analysis)
- How do you calculate the eigenvalues and Eigenvector of a matrix?
- What is K-NN (K Nearest Neighbour) Classification?
- Module 5: Python Exercise on Naive Bayes
- How to use the Naïve Bayes classifier?
- What is the Naive Bayes Classifier?
- How is the Naive Bayes Classifier relevant in the context of email spam classification?
- Module 6: Tutorial III
- How do we estimate the probabilities using the frequency distribution of probability
- How do we use Bayes rule
- What is MAP inference
- What is the Naive Bayes assumption
- What is Bayesian networks (the structures), inference and marginalization?
- Module 7: Python Exercise on SVM
- Support vector classification
- Visualize the decision boundaries
- Load data
- Module 8: Python Exercise on Neural Network
- How can we create an artificial neural network using TensorFlow and TFLearn to recognize handwritten digits?
- How do we Load dependencies (to recognize handwritten digits)?
- How do we Load the data (to recognize handwritten digits)?
- How do we make the model (to recognize handwritten digits)?
- How do we train the model (to recognize handwritten digits)?
- What is our takeaway from this exercise (to recognize handwritten digits)?
- Module 9: Tutorial IV
- What is a Perceptron?
- What is Perceptron learning rule?
- How do we represent a Boolean function using a Perceptron?
- What is the forward and backward pass algorithm or backpropagation algorithm?
- Stochastic gradient descent and Batch gradient descent
- The quick overview of some deep learning algorithms
- Module 10: Python Exercise on K-means Clustering
- Can we look at python code for K Means algorithm?
- Can we look at python code for Gaussian mixture model?
- Hierarchical Agglomerative Clustering
- Module 11: Tutorial V
- What is K-means clustering?
- Solving a sample problem n K-means clustering?
- What is Agglomerative Hierarchical clustering?
- What is the Gaussian Mixture Model?