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.

Download Syllabus

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

Benefits

Data is transforming everything we do. All organizations, from startups to tech giants to Fortune 500 corporations, are racing to harness the immense amounts of data generated unknowingly every day and put it to use for key decisions. Big and small data is reshaping technology and business as we know it and will continue to do so (in the near future at least).

The state of Machine Learning in companies and in your daily life machine Learning is no more just a mere niche of the tech world but is a new field of work and research altogether. Tech experts have been increasingly making use of Machine Learning over the years. Surge pricing at Uber, Walmart product recommendations, Social media feeds displayed by both Facebook and Instagram, Google Maps, detecting financial fraud at financial institutions etc - all these and many more functionalities are now being performed with the help of powerful Machine Learning algorithms, increasingly without human interference.Every individual is making use of one or the other product of Machine Learning, whether he knows it or not. In such a scenario, learning about Machine Learning is an inevitable step that any professional, especially someone involved in the field of Information Technology and Data Science, must take in order to not become irrelevant.

Some of the benefits of learning Machine Learning include the following:

  1. It reels in better job opportunities: According to a report published by Tractica, services driven by Artificial Intelligence were worth $1.9 billion in the year 2016 and this number is expected to rise to the neighbourhood of around $19.9 billion by the end of the year 2025. Machine Learning is the bandwagon that every corporation in the world is hitching its wagon to. With each industry in the world looking at expansion in the domain of Machine Learning and Artificial Intelligence, a knowledge of the same is bound to attract more and brighter career opportunities in the present scenario and in the future as well.
     
  2. Machine Learning engineers earn a pretty penny: The valuation of a Machine Learning expert can be compared to that of a top NFL quarterback prospect. According to a study published by SimplyHired.com, the average income of a machine learning engineer is pegged at $142,000, whereas an experienced machine learning engineer can earn up to $195,752 per year.
     
  3. Demand for Machine Learning skills is only increasing:
    There exists a huge gap between the demand and the availability of Machine Learning engineers. This skill gap is increasingly being lamented by the Chief Information Officers (CIOs) of some of the biggest corporations around the world. What this means, essentially, is that the demand as well as the pay for professionals with Machine Learning skills is only going to increase in the future.
     
  4. Most of the industries are shifting to Machine Learning:
    Most industries in operation around the world are dealing with a humongous amount of data that is only increasing every single day. The benefits reaped by a thorough analysis of this data is a fact that companies are fast taking cognizance of. By gleaning insights from this data, companies are looking to work more efficiently and competently, as well as gaining an edge over their competitors.

    At such as time, all industries from the financial services sector to government agencies, the healthcare industry and oil and gas mega-corporations to the transportation sector - every industry is a ripe field for work in the field of Machine learning.

Course Content

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 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
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
Maxima and Minima 14:08 Play
Cost Function 14:08 Play
Learning Rate 14:08 Play
Optimization Techniques 14:08 Play
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
Clustering approaches 14:12 Play
K Means clustering 14:12 Play
Hierarchical clustering 14:13 Play
Case Study 14:13 Play
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
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

 

 

Course Info.

Lenght
50+ hours
Effort
3-4 Hours/week
Institution
Open Source
Language
English
Video Script
English

Training Options

Selfpaced 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

1200
  • 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

FAQs

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.