Masterclass of Machine Learning with Python
What you'll learn
- The course provides path to start career in Data Science , Artificial Intelligence, Machine Learning
- Problem Solving Approach
- Impress interviewers by showing an understanding of the Machine Learning Algorithm concept
- Python Basic to Advance Concept with Numpy, Pandas, Matplotlib, Seaborn, Plotly Library
- Scikit Learn Library in Depth
- Machine Learning Algorithms such as Linear, Logistic, SVM, KNN, K Mean, Naïve Bayes, Decision Tree and Random Forest
- Machine Learning Types Such as Supervise Learning, Unsupervised Learning, Reinforcement Learning
- Machine Learning concept such as Train Test Split, Machine Learning Models, Model Evaluation
Requirements
- It start with Basics
- Only a passion for Learning
- All software used in this course is either available for Free or as a Demo version
- This course is intended for absolute beginners in programming
Description
This Course will design to understand Machine Learning Algorithms with case Studies using Scikit Learn Library. The Machine Learning Algorithms such as Linear Regression, Logistic Regression, SVM, K Mean, KNN, Naïve Bayes, Decision Tree and Random Forest are covered with case studies using Scikit Learn library. The course provides path to start career in Data Science , Artificial Intelligence, Machine Learning. Machine Learning Types such as Supervise Learning, Unsupervised Learning, Reinforcement Learning are also covered. Machine Learning concept such as Train Test Split, Machine Learning Models, Model Evaluation are also covered.
A subfield of artificial intelligence (AI) and computer science called machine learning focuses on using data and algorithms to simulate how humans learn, gradually increasing the accuracy of the system.
With the use of machine learning (ML), which is a form of artificial intelligence (AI), software programmes can predict outcomes more accurately without having to be explicitly instructed to do so. In order to forecast new output values, machine learning algorithms use historical data as input.
Machine learning is frequently used in recommendation engines. Business process automation (BPA), predictive maintenance, spam filtering, malware threat detection, and fraud detection are a few additional common uses.
Machine learning is significant because it aids in the development of new goods and provides businesses with a picture of trends in consumer behaviour and operational business patterns. A significant portion of the operations of many of today's top businesses, like Facebook, Google, and Uber, revolve around machine learning. For many businesses, machine learning has emerged as a key competitive differentiation.
Who this course is for:
- The course is ideal for beginners, as it starts from the fundamentals and gradually builds up your skills in Machine Learning Algorithms
- People interested to learn Machine Learning Algorithms using Scikit Learning Library and Python
Instructor
Hello, I’m Piyush Dave, and I’m passionate about financial data analysis and teaching others about it. I have extensive experience in Python, R, Machine Learning, Tableau, Derivative Trading, Capital Markets, Commodity Markets, Debt Markets, and Foreign Exchange Market.
On Udemy, I’ve had the pleasure of teaching over 5000 students across five courses, each boasting an average rating of 4.5+. Join me in exploring the intricacies of financial analysis, data science, and effective trading strategies in my courses.
My educational background includes Master of Engineering (ME) / Bachelor of Engineering (BE). I also hold certifications in Equity Derivatives, Python, R, Deep Learning, and Machine Learning.
I’ve worked as a Research Associate at the esteemed Indian Institute of Management (IIM), Mumbai, and as a Derivative Trading Analyst at KIFS Trade Capital Ltd. Additionally, I’ve served as a Visiting Trainer at reputable institutions such as IIM Mumbai, BSE Institute Ltd., Imarticus Learning, and IT Vedanta, among others