Practical Machine Learning Using Python

4.3 ★★★★ ★

Practical Machine Learning Using Python

Name: Practical Machine Learning Training Using Python - Online Course
Rating: 4.3 (61 reviews)
Author: MANAS DASGUPTA

Build Machine Learning Models in Python using Scikit-Learn, Numpy, Pandas, and Statsmodel Libraries

updated on icon Updated on Jun, 2025

language icon Language - English

person icon MANAS DASGUPTA

English [CC]

category icon Development ,Data Science,Machine Learning

Lectures -93

Resources -2

Duration -34 hours

Lifetime Access

4.3 ★★★★ ★

Add to Cart Buy Now

Lifetime Access

30-days Money-Back Guarantee

Training 5 or more people ?

Get your team access to 10000+ top Tutorials Point courses anytime, anywhere.

Course Description

Are you aspiring to become a Machine Learning Engineer or Data Scientist? If yes, then this course is for you. This course covers Python for Data Science and Machine Learning in great detail and is absolutely essential for beginners in Python.

In this course, you will use cases and learn about:

Core concepts of Machine Learning.
The role of data and challenges of Bias.
Variance and Overfitting.
Choosing the right performance metrics.
Model evaluation techniques.
Model optimization using Hyperparameter tuning.
Grid Search Cross Validation Techniques.

Course Overview:

This course covers the use of Numpy and Pandas Libraries extensively for teaching Exploratory Data Analysis. There is also an introductory lesson included on Deep Neural Networks with a worked-out example of Image Classification using TensorFlow and Keras.

You will learn how to build Classification Models using a range of Algorithms, Regression Models, and Clustering Models. You will learn the scenarios and use cases of deploying Machine Learning models.

Most of this course is hands-on, through completely worked-out projects and examples taking you through Exploratory Data Analysis, Model development, Model Optimization, and Model Evaluation techniques.

Goals

Master core concepts of Machine Learning in detail.
Understand use-case scenarios for applying Machine Learning.
Detailed coverage of Python for Data Science and Machine Learning.
Regression Algorithm - Linear Regression.
Classification Problems and Classification Algorithms.
Unsupervised Learning using K-Means Clustering.
Exploratory Data Analysis Techniques.
Dimensionality Reduction Techniques (PCA).
Feature Engineering Techniques.
Model Optimization using Hyperparameter Tuning.
Model Optimization using Grid-Search Cross-Validation.
Introduction to Deep Neural Networks.

Prerequisites

Some exposure to Programming Languages will be useful.

Curriculum

Check out the detailed breakdown of what’s inside the course

Introduction to Machine Learning

12 Lectures

Introduction to Machine Learning 11:45 11:45
Machine Learning Terminology 13:35 13:35
History of Machine Learning 16:36 16:36
Machine Learning Use Cases and Types 21:13 21:13
Role of Data in Machine Learning 06:16 06:16
Challenges in Machine Learning 19:11 19:11
Machine Learning Life Cycle and Pipelines 19:54 19:54
Regression Problems 10:29 10:29
Regression Models and Perforance Metrics 11:54 11:54
Classification Problems and Performance Metrics 13:14 13:14
Optmizing Classificaton Metrics 09:24 09:24
Bias and Variance 09:03 09:03

Python for Data Science and Machine Learning

28 Lectures

Linear Regression

13 Lectures

Logistic Regression

8 Lectures

Naive Bayes Classification Algorithom

5 Lectures

Decision Tree Algorithm

6 Lectures

Random Forest Ensemble Algorithm

4 Lectures

Support Vector Machine

5 Lectures

Dimensionality Reduction - Principle Component Analysis (PCA)

4 Lectures

Unsupervised Learning with K-Means Clustering

6 Lectures

Introduction to Deep Learning

1 Lectures

Code Files

1 Lectures

Instructor Details

MANAS DASGUPTA

Hi there, I am Manas Dasgupta, from Bangalore, the Silicon Valley of India.

By qualification, I hold a Master's Degree (MSc) in AI from the Liverpool John Moores University (LJMU), UK.

My expertise area encompass Generative AI - RAG Application Development using Frameworks like LangChain and LlamaIndex, Machine Learning and Data Science / Predictive Analytics areas including various Supervised, Unsupervised, Deep Neural Networks, Clustering Techniques, etc.

My research areas in Masters were Natural Language Processing (NLP) using Deep Learning Methods such as Siamese Networks, Encoder-Decoder techniques, various Language Embedding methods such as BERT, areas such as Supervised Learning on Semantic Similarity and so on.

I have > 20 Years of experience in the IT Development mostly in the Financial Services domain, developing products and solutions. I am also the Founder of Teksands where me and my team develop Gen AI rich applications in the Talent space.