Get ready to dive into the world of Machine Learning (ML) by using Python! This course is for you whether you want to advance your Data Science career or get started in Machine Learning and Deep Learning.

This course will begin with a gentle introduction to Machine Learning and what it is, with topics like supervised vs unsupervised learning, linear & non-linear regression, simple regression and more. You will then dive into classification techniques using different classification algorithms, namely K-Nearest Neighbors (KNN), decision trees, and Logistic Regression. You’ll also learn about the importance and different types of clustering such as k-means, hierarchical clustering, and DBSCAN. With all the many concepts you will learn, a big emphasis will be placed on hands-on learning. You will work with Python libraries like SciPy and scikit-learn and apply your knowledge through labs. In the final project, you will demonstrate your skills by building, evaluating and comparing several Machine Learning models using different algorithms. By the end of this course, you will have job-ready skills to add to your resume and a certificate in machine learning to prove your competency.

DATA SCIENCE (PYTHON WITH ML) SKILLS COVERED PYTHON CORE & ADV. Core Python  Data Science requires Python  Jupyter Notebook  Python Syntax  Conditional Operators, Arithmetic, Scope, and Lambda Functions. Adv. Python Oops concepts (inheritance, polymorphism, Encapsulation, multithreading, classes, objects)  Advance python for ML Numpy module  Introduction to Array  Creation and Printing of ndarray  Basic Operations in Numpy  Indexing  Mathematical Functions of Numpy Panda’s module  Series and Data Frames  Data Importing and Exporting through Excel, CSV Files  Data Understanding Operations,  Descriptive Statistics, Removing Duplicates, String Manipulation Data visualization Matplotlib, Basic Plotting, Properties of plotting, About Subplots, Line plots, Pie chart and Bar Graph, Histograms. Box and Violin Plots, Scatter plot Machine Learning  Top 7 Algorithms in Supervised  Top 3 Algorithms in Unsupervised