Machine Learning Using Python Training

Learn to build intelligent systems using Python and unlock exciting career opportunities in Artificial Intelligence and Data Science.

Course Overview:

Our Machine Learning Using Python Training is designed for students, graduates, and professionals who want to explore the exciting world of Artificial Intelligence and Data Science. This course covers the fundamentals of Machine Learning algorithms, data preprocessing, model building, and model evaluation using Python and industry-standard libraries such as NumPy, Pandas, Matplotlib, Scikit-learn, and more.

The training emphasizes conceptual understanding, practical exercises, and hands-on coding sessions to help learners gain confidence in solving real-world problems using machine learning techniques.

Course Length: 40 Days

Course Description:

Learn the fundamentals of Machine Learning using Python and discover how intelligent systems are built. This course covers data analysis, machine learning algorithms, and model evaluation using popular Python libraries. Ideal for students and professionals looking to build a career in Artificial Intelligence, Data Science, and Machine Learning.

Who Should Attend:

This course is ideal for:

Students pursuing B.Tech, B.Sc, MCA, or M.Tech
Graduates looking for careers in AI & Data Science
Python developers interested in Machine Learning
Working professionals planning to upskill
Anyone passionate about Artificial Intelligence

Benefits of Attendance:

Machine Learning is transforming industries such as healthcare, finance, e-commerce, cybersecurity, and manufacturing. Learning Machine Learning with Python opens doors to some of the highest-paying and fastest-growing careers in technology.

Prerequisites:

Basic knowledge of Python programming
Familiarity with variables, functions, loops, and OOP concepts
Interest in data analysis and Artificial Intelligence

This background will help students understand Machine Learning concepts more effectively and progress smoothly through the course.

Career Outcomes:

100% job-oriented training with real-time project exposure

Job Roles:

Machine Learning Engineer
Data Scientist
AI Engineer
Data Analyst
Python Developer (AI/ML)
Business Intelligence Analyst
Research Associate
Junior Data Engineer

Course Outline:

Introduction to Machine Learning

About Data
Numerical Data or Quantitative Data
Categorical Data or Qualitative Data
Images, Audio, Video Data
Examples of Machine Learning
Examples of Deep Learning
Supervised Learning
Unsupervised Learning

Installation of Anaconda

Installation of Jupyter

Using Spyder IDE

Exploratory Data Analysis

Dataset used: cars.csv, titanic.csv
The process of EDA
Finding details about Data
Updating or deleting Unwanted data
Retrieving Data
Getting Statistical Information from Data set
Plotting Graphs

Outliers

Simple Linear Regression

Variable
Linear Regression
The r squared value
Practical use of simple linear regression
Example: homeprices.csv
Simple Linear Regression with Train and Test Data
Example: Salary_Data.csv
Example: Canada_per_capita_income.xlsx

Multiple Linear Regression

One Hot Encoding

Polynomial Linear Regression

Ridge Regression

Lasso Regression

Elastic net Regression

Logistic Regression

Classification Types
Binary Classification using Logistic Regression
Example: insurance_data.csv
Confusion Matrix
Multiclass Classification using Logistic Regression
Example: Hand written digits
Drawing Confusion Matrix
Using the model to identify our own hand_written Digit

Support Vector Machine

Naïve Bayes Classification

Baye’ s Theorem
Applying Naïve Bayes Model on New Data
Types of Naïve Bayes model
Example: cricket.csv
Count Vectorizer
Multinomial Naïve Bayes Classifier

KNN classifier

Decision Trees

Random Forest

Bootstrapping
Bagging (Bootstrap Aggregation)
Boosting (Ada Boost or Adaptive Boosting)
Random Forest
Example: digit data set from sklearn
Random Forest Regressor
Example: Salary-Experience.csv

K-Means Clustering

Supervised Learning vs Unsupervised Learning
K-Means Clusters
Dataset: income.csv
MinMax Scaler
Drawing the Elbow Plot
Accuracy of K-Means Clustering Model
Dataset: Iris

Apriori Algorithm

Principal Component Analysis

Eigen Vector or Principal Component
Covariance Matrix
Example on Covariance Matrix
Dataset: Iris flower
Reducing Dimensionality from 4D to 1D
Dataset: breast cancer

K-Fold Cross Validation

Under fitting and Overfitting
Cross Validation
K-Fold Cross Validation
Dataset: breast cancer
K-Fold Cross Validation for Logistic Regression Model
K-Fold Cross Validation for Decision Tree Classifier model

Model Selection

Getting Better Performance from the Model
How to select a Suitable Model
Selection Based on the output
Common Elements in the Features
Benchmark in the predictions
Loss/Objective Function
Tuning Parameters and Hyperparameters
Bias and Variance
Scalability
Compare the models
Selection of the Best Model for Hear Disease Dataset
Dataset: processed.cleaveland.data
Scores of Various Models on Heart Disease data

Student Experiences & Reviews

The course explained Machine Learning concepts in a very simple and structured way. I was able to understand algorithms and implement them confidently using Python. It gave me the motivation to pursue a career in Data Science.

– Ananya M., Business Analyst

I always wanted to learn Machine Learning but found it intimidating. This training made the subject approachable and engaging. The practical coding sessions helped me gain confidence and improve my problem-solving skills.

– Suresh P., Senior IT Consultant

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