Machine Learning Course with Python

4.5 (8761) 8781 Learners

Varied approaches to Machine Learning: Supervised & Unsupervised, Regression & Classifications, more.

Learn various advanced machine learning algorithms like KNN, Decision Trees, SVM, Clustering in detail.

Learn deep learning techniques, data visualization and how to build and deploy deep learning models.

Immersive applied learning to transform your theoretical knowledge into practical skill.

Duration
50+ hours
Institution
Open Source
Language
English
Video Script
English

Overview

Machine learning came into its own in the late 1990s, when data scientists hit upon the concept of training computers to think. Machine learning gives computers the capability to automatically learn from data without being explicitly programmed, and the capability of completing tasks on their own. This means in other words that these programs change their behaviour by learning from data. Machine learning enthusiasts are today among the most sought after professionals. Learn to build incredibly smart solutions that positively impact people’s lives, and make businesses more efficient! With Payscale putting average salaries of Machine Learning engineers at $115,034, this is definitely the space you want to be in!

What you will learn

  • Gain insight into the 'Roles' played by a Machine Learning Engineer
  • Automate data analysis using python
  • Describe Machine Learning
  • Work with real-time data
  • Learn tools and techniques for predictive modeling
  • Discuss Machine Learning algorithms and their implementation
  • Validate Machine Learning algorithms
  • Explain Time Series and it’s related concepts
  • Gain expertise to handle business in future, living the present

Syllabus

International industry expertise at your disposal as you deep-dive into the research topic and sector of your choice.

Course Content

Statistical Learning (5 Lectures)

Statistical analysis concepts

14:02 Play

Descriptive statistics

14:02 Play

Introduction to probability and Bayes theorem

14:02 Play

Probability distributions

14:02 Play

Hypothesis testing & scores

14:02 Play

Python for Machine Learning (5 Lectures)

Python Overview

14:03 Play

Pandas for pre-Processing and Exploratory Data Analysis

14:03 Play

Numpy for Statistical Analysis

14:03 Play

Matplotlib & Seaborn for Data Visualization

14:03 Play

Scikit Learn

14:03 Play

Introduction to Machine Learning (6 Lectures)

Machine Learning Modelling Flow

14:06 Play

How to treat Data in ML

14:06 Play

Types of Machine Learning

14:06 Play

Performance Measures

14:06 Play

Bias-Variance Trade-Off

14:07 Play

Overfitting & Underfitting

14:07 Play

Optimization (4 Lectures)

Maxima and Minima

14:08 Play

Cost Function

14:08 Play

Learning Rate

14:08 Play

Optimization Techniques

14:08 Play

Supervised Learning (10 Lectures)

Linear Regression

14:11 Play

Case Study

14:11 Play

Logistic Regression

14:11 Play

Case Study

14:11 Play

KNN Classification

14:11 Play

Case Study

14:11 Play

Naive Bayesian classifiers

14:11 Play

Case Study

14:11 Play

SVM - Support Vector Machines

14:11 Play

Case Study

14:12 Play

Unsupervised Learning (4 Lectures)

Clustering approaches

14:12 Play

K Means clustering

14:12 Play

Hierarchical clustering

14:13 Play

Case Study

14:13 Play

Ensemble Techniques (10 Lectures)

Decision Trees

14:15 Play

Case study

14:15 Play

Introduction to Ensemble Learning

14:15 Play

Different Ensemble Learning Techniques

14:15 Play

Bagging

14:15 Play

Boosting

14:15 Play

Random Forests

14:15 Play

Case Study

14:15 Play

PCA (Principal Component Analysis) and Its Applications

14:15 Play

Case Study

14:16 Play

Recommendation System (7 Lectures)

Introduction to Recommendation Systems

14:17 Play

Types of Recommendation Techniques

14:17 Play

Collaborative Filtering

14:17 Play

Content based Filtering

14:18 Play

Hybrid RS

14:18 Play

Performance measurement

14:18 Play

Case Study

14:18 Play

Course Details

Wissenhive's Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. Machine Learning training will provide a deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in the python programming language. Furthermore, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real-life scenarios using Machine Learning Algorithms. Towards the end of the course, we will be discussing various practical use cases of Machine Learning in the python programming language to enhance your learning experience.

Wissenhive's Python Machine Learning Certification Course is a good fit for the below professionals:

  • Developers aspiring to be a ‘Machine Learning Engineer'
  • Analytics Managers who are leading a team of analysts
  • Business Analysts who want to understand Machine Learning (ML) Techniques
  • Information Architects who want to gain expertise in Predictive Analytics
  • 'Python' professionals who want to design automatic predictive models

Prerequisites

  • Sufficient knowledge of at least one coding language is required
  • Minimalistic and intuitive, Python is the perfect choice

 

 

Enquiry

Training Options

Self-paced Training

299
  • Lifetime access to high-quality self-paced eLearning content curated by industry experts
  • 3 simulation test papers for self-assessment
  • Lab access to practice live during sessions
  • 24x7 learner assistance and support

Live Virtual Classes

499
  • Online Classroom Flexi-Pass
  • Lifetime access 
  • Practice lab and projects with integrated Azure labs
  • Access to Microsoft official content aligned to examination

One on One Training

Enquiry Now
  • Customized learning delivery model (self-paced and/or instructor-led)
  • Flexible pricing options
  • Enterprise grade learning management system (LMS)
  • Enterprise dashboards for individuals and teams
  • 24x7 learner assistance and support

Exam & Certification

No exam required.

The candidate will complete a project at the end of the training. On successful completion of the project the candidate will receive a certificate of training.

Machine Learning Course with Python

Frequently Asked Questions

Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning.

The concept of Machine Learning deals with computers and systems taking in a huge amount of data, analyzing it and solving problems through training on that data in order to obtain the best possible outcome for a task or problem. It is a way for humans to be able to solve problems, without having to actually know and understand what the problem really is, as well as why a particular approach to a problem actually works.

  • It's easy and it works: Machines are able to work faster than human brains do and as such, are able to solve problems faster than we ever can. If, for example, there exist a million options, answers or approaches to a problem, a machine is able to systematically work out, resolve and simultaneously evaluate all the options in order to obtain the best possible outcome or result.
  • Being used in a wide range of applications today: Machine Learning has several practical applications in real life. It is the very solution that the world was looking for a variety of problems. Machine learning drives businesses in that it helps them save time and money as well as effort. It is allowing people to get more things done in a more efficient, effective and appropriate manner. Every industry, starting from health care, nursing, transport, customer service to government and financial institutions are benefitting from Machine Learning, which is what makes it an indispensable part of our society as it stands today.