Machine Learning With Python

1. Course description:

Machine Learning with Python has been designed for the provision of having strong hold in creating Machine learning algorithms with the base of Python. This has been preferred as the best and robust platform for having Machine Learning systems.

2. Why to pursue Machine Learning?

1. Machine learning is almost acquiring the globe in terms of resource requirement for tech companies, who are into the same domain and execute the projects on Machine Learning.
2. There is a tentative estimation for machine learning market expansion from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022.

3. What are the insights of machine learning with python online course?

After course completion, a participant can get entitled with the below:

1. Strong knowledge on AI, Data Science and Machine Learning.
2. Implementation and having strong holds on principles, algorithms and applications of Machine Learning through unique teaching approach.
3. Having depth knowledge of Machine Learning models with the base of mathematical and heuristic aspects of Machine Learning.
4. Comprehensive relation between theory and practical for Machine Learning.
5. Problem solving ability for different real world scenarios on Machine Learning as we will be covering various real time use cases and you need to accomplish 7+ ML projects.

4. What advantages will you have with this machine learning with python certification training?

Pursuing this course will make you being able to perform below:

1. Identify different applications for machine learning based algorithms.
2. Different learnings, which includes supervised and unsupervised.
3. Performing supervised learning techniques such as linear and logistic regression.
4. Understanding on data classifications and models.
5. Development of robust Machine Learning models.
6. Top algorithms among many for any given Machine Learning problem
7. Create accurate predictions and powerful analysis.
8. Usage of unsupervised learning algorithms as Clustering & Segmentation.

5. Who should take this machine learning training course?

There is an increasing demand for skilled Machine Learning Engineers across all industries, making this Machine Learning certification course well-suited for participants at the intermediate level of experience. We recommend this Machine Learning Training particularly for the following professionals:

1. Enthusiastic Software Developers to be a Data Scientist or Machine Learning Engineers.
2. Managers from Analytics background.
3. BA profile holders, who are willing to know about Data Science techniques.
4. Software/Solution Architects, who wants to have strong hold on Machine Learning algorithms.
5. Analytics candidates, who are opting to work in the domain of Machine Learning or Artificial Intelligence.
6. Graduates seeking for building their career in Data Science and Machine Learning.

6. What projects are included in this machine learning online training course?

Some of the real-life projects included in the Course are like,

1. Predicting Housing Price.
2.  Predicting a business deal outcome (win or loss).
3. Predicting sales performance of newly on-boarded dealer/distributors.
4. Predicting credit card defaulter (Yes or No).
5.  Predicting commodity price.
6. Customer segmentation.
7. Time series forecasting.

The objective of the projects is to provide a perspective of the technical challenges faced by any data scientist in the real life scenarios.

7. What are some of the best books on machine learning?

1. David Barber’s Bayesian Reasoning and Machine Learning.
2. Kevin Murphy’s Machine learning: a Probabilistic Perspective.
3. Hastie, Tibshirani, and Friedman’s The Elements of Statistical Learning.
4. Bishop’s Pattern Recognition and Machine Learning.
5. Mitchell’s Machine Learning.

8. Done with machine learning. What’s next?

Deep Learning & Artificial Intelligence (AI). These are surely going to be the next waves in the data science space. As we have started living in a ‘smart’ environment, AI is going to play an important role to build all the smart and autonomous systems; and potentially creating significant job opportunities in this field.