MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 90 lectures (23h 31m) | Size: 7.75 GB
Build Machine Learning Models in Python using Scikit-Learn, Numpy, Pandas, Statsmodel Libraries
What you’ll learn:
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
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. In addition, it also covers Marplotlib and Seaborn Libraries for creating Visualizations.
There is also an introductory lesson included on Deep Neural Networks with a worked out example on Image Classification using TensorFlow and Keras.
Course Sections:
Introduction to Machine Learning
Types of Machine Learning
Use cases of Machine Learning
Role of Data in Machine Learning
Understanding the process of Training or Learning
Understanding Validation and Testing
Introduction to Python
Setting up your ML Development Environment
Python internal Data Structures
Python Language Elements
Pandas Data Structure – Series and DataFrames
Exploratory Data Analysis (EDA)
Learning Linear Regression Model using the House Price Prediction case study
Learning Logistic Model using the Credit Card Fraud Detection case study
Evaluating your model performance
Fine Tuning your model
Hyperparameter Tuning
Cross Validation
Learning SVM through an Image Classification project
Understanding Decision Trees
Understanding Ensemble Techniques using Random Forest
Dimensionality Reduction using PCA
K-Means Clustering with Customer Segmentation Project
Introduction to Deep Learning
Who this course is for
Aspiring Machine Learning Engineers
Aspiring Data Science Professionals
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