what will you learn
Apply advanced machine learning models to perform sentiment analysis and classify customer reviews such as Amazon Alexa products reviews
Understand the theory and intuition behind several machine learning algorithms
Implement classification algorithms in Scikit-Learn for K-Nearest Neighbors, (SVM), Decision Trees, Random Forest, Naive Bayes, and Logistic Regression
Build an e-mail spam classifier using Naive Bayes classification Technique
Apply machine learning models to Healthcare applications such as Cancer and Kyphosis diseases classification
Develop Models to predict customer behavior towards targeted Facebook Ads
Classify data using K-Nearest Neighbors, Support Vector Machines (SVM), Decision Trees, Random Forest, Naive Bayes, and Logistic Regression
Build an in-store feature to predict customer's size using their features
Develop a fraud detection classifier using Machine Learning Techniques
Master Python Seaborn library for statistical plots
Understand the difference between Machine Learning, Deep Learning and Artificial Intelligence
Perform feature engineering and clean your training and testing data to remove outliers
Master Python and Scikit-Learn for Data Science and Machine Learning
Learn to use Python Matplotlib library for data Plotting
Who this course is for
Data Science Enthusiasts wanting to enhance their machine learning skills
Python programmers curious about Machine Learning and Data Science
Programmers or developers who want to make a shift into the lucrative data science and machine learning career path
Technologists wanting to gain an understanding of how machine learning models work
Data analysts who want to transition into the Tech industry
Requirements
Basic knowledge of Python Programming
Experienced computer user
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