Machine Learning & Deep Learning In Python & R

Covers Regression, Decision Trees, SVM, Neural Networks, CNN, Time Series Forecasting and more using both Python & R

What you’ll learn

• Learn how to solve real life problem using the Machine learning techniques
• Machine Learning models such as Linear Regression, Logistic Regression, KNN etc.
• Advanced Machine Learning models such as Decision trees, XGBoost, Random Forest, SVM etc.
• Understanding of basics of statistics and concepts of Machine Learning
• How to do basic statistical operations and run ML models in Python
• Indepth knowledge of data collection and data preprocessing for Machine Learning problem
• How to convert business problem into a Machine learning problem

Requirements

• Students will need to install Anaconda software but we have a separate lecture to guide you install the same

Description

You’re looking for a complete Machine Learning and Deep Learning course that can help you launch a flourishing career in the field of Data Science & Machine Learning, right?

You’ve found the right Machine Learning course!

After completing this course you will be able to:

Confidently build predictive Machine Learning and Deep Learning models to solve business problems and create business strategy

Answer Machine Learning related interview questions

Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Machine learning in R

1. Its a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, its not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing.

2. Learning the data science basics is arguably easier in R. R has a big advantage: it was designed specifically with data manipulation and analysis in mind.

3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science.

4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, R has exploded with it, becoming one of the fastest-growing languages in the world (as measured by StackOverflow). That means its easy to find answers to questions and community guidance as you work your way through projects in R.

5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Adding R to your repertoire will make some projects easier and of course, itll also make you a more flexible and marketable employee when youre looking for jobs in data science.

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What is the difference between Data Mining, Machine Learning, and Deep Learning?

Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledgeand further automatically applies that information to data, decision-making, and actions.

Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.

Author(s): Start-Tech Academy

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