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Practical Machine Learning using Python

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

  MANAS DASGUPTA

   Development, Data Science and AI ML, Machine Learning

Language - English Published on 11/2021

4.7
Ratings ( 3 )
  • Introduction to Machine Learning
    11:45
    Preview
  • Machine Learning Terminology
    13:35
    Preview
  • History of Machine Learning
    16:36
  • Machine Learning Use Cases and Types
    21:13
  • Role of Data in Machine Learning
    06:16
  • Challenges in Machine Learning
    19:11
  • Machine Learning Life Cycle and Pipelines
    19:54
  • Regression Problems
    10:29
  • Regression Models and Perforance Metrics
    11:54
  • Classification Problems and Performance Metrics
    13:14
  • Optmizing Classificaton Metrics
    09:24
  • Bias and Variance
    09:03

Description

Are you aspiring to become a Machine Learning Engineer or Data Scientist? if yes, then this course is for you.

In this course, you will learn about core concepts of Machine Learning, use cases, role of Data, challenges of Bias, Variance and Overfitting, choosing the right Performance Metrics, Model Evaluation Techniques, Model Optmization using Hyperparameter Tuning and Grid Search Cross Validation techniques, etc. 

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.

This course covers Python for Data Science and Machine Learning in great detail and is absolutely essential for the beginner in Python. 

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

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 on Image Classification using TensorFlow and Keras

What Will I Get ?

  • Machine Learning Core Concepts 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

Requirements

  • Some exposure to Programming Languages will be useful

4.7
Course Rating
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    Feedbacks (3)

  • Dr R K Gupta
    Dr R K Gupta

  • Arunkumar S
    Arunkumar S

  • Shailesh Sanjeeva Billava
    Shailesh Sanjeeva Billava

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