In this course, we will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

This course is packed with practical exercises which are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.

Course Curriculum

Section1: Welcome To The Course
Installing Python
Section2: Core Programming Principles
Types of Variables
Using Variables
Boolean Variable and Operators
The "While" Loop
The "For" Loop
The "If" Statement
Code Indentation in Python
Section3: Fundamentals of Python
Whats is a List?
Tuples in Python?
Functions in Python
Packages in Python
Numpy and Arrays in Python
Slicing Arrays
Section4:Matrices
Matrices
Dictionaries in Python
Matrix Operations
Creat Visualization
Section4: Data Frames
Importing Data into Python
Exploring Datasets
Renaming Columns of a Dataframe
Subsetting Dataframe in Python
Basic operations in Data Frame
Filtering in Data Frame
Using .at() and .iat()
Introduction in Seaborn
Visualizing with Seaborn
Section6: Advanced Visualzation
Histogram
Stacked Histogram
Creating a KDE plot
Working with subplots
Box and Plot
Section5: Data Frames
Section7: Data Science
Application of Machine Learning
Why Machine Learning is the future
Section 8: Data Preprocessing
Section 9: Regression
Simple Linear Regression
Multi Linear Regression
Polynomial Regression
Support Vector Regression
Decision Tree Regression
Random Forest Regression
Evaluation Model Performance
Section 10: Classification
Logistic Regression
K-Nearest Neighbors
Support Vector Machines
Kernel SVM
Naive Bayes
Decission Tree Classification
Random Forest Classification
Evaluating Classfication Models
Section 11: Clustering
K-Means Clustering
Hierarchical Clustering
Section 12: Association Rule
Aprior
Eclat
Section 13: Reinforcement Learning
Upper Confidence Bound
Thompson Sampling
Section 14: Natural Language Proocessing
Section 15: Deep Learning
Section 16: Artificial Neural Network
LIVE RECORDINGS
Day 1 – PYTHON 02:20:00
Day2 – Python 00:00:00
Day3 – Python 00:00:00

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ZS Associates

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