Projects And Case Studies On Machine Learning With Python

Programming In Python For Data Analytics And Machine Learning. Learn Statistical Analysis, Data Mining And Visualization

What you'll learn

You will learn how to use data science and machine learning with Python.

You will be able to analyze your own data sets and gain insights through data science.

Master critical data science skills.

Replicate real-world situations and data reports.

Requirements

No prior knowledge of machine learning required

Basic knowledge of Python

Description

There are lots of Python courses and lectures out there. However, Python has a very steep learning curve and students often get overwhelmed. This course is different. This course is truly step-by-step. In every new tutorial we build on what had already learned and move one extra step forward. After every video you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples. This training is packed with real-life analytical challenges which you will learn to solve. Some of these we will solve together, some you will have as homework exercises. In summary, this course has been designed for all skill levels and even if you have no programming or statistical background you will be successful in this course. Apply Data Science using Python, Statistical Techniques, EDA, Numpy, Pandas, Scikit Learn, Statsmodel Libraries.Are you aspiring to become a Data Scientist or Machine Learning Engineer? if yes, then this course is for you. In this course, you will learn about core concepts of Data Science, Exploratory Data Analysis, Statistical Methods, role of Data, Python Language, 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 perform detailed Data Analysis using Pythin, Statistical Techniques, Exploratory Data Analysis, using various Predictive Modelling Techniques such as a range of Classification Algorithms, Regression Models and Clustering Models. You will learn the scenarios and use cases of deploying Predictive 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.

Overview

Section 1: Projects and Case Studies on Machine Learning with Python

Lecture 1 Introduction to Machine Learning Case Studies

Lecture 2 Environmental SetUp

Lecture 3 Problem Statement for Linear Regression

Lecture 4 Starting with Normal linear Regression

Lecture 5 Polynomial Regression

Lecture 6 Backward Elimination

Lecture 7 Robust Regression

Lecture 8 Logistic Regression

Lecture 9 Logistic Regression Continue

Lecture 10 Introduction to k-Means Clustering

Lecture 11 Creating Scattered Plots

Lecture 12 Euclidean Distance Calculator

Lecture 13 Printing Centroid Values

Lecture 14 Analysing Face Detection

Lecture 15 Problem Statement

Lecture 16 Creating Model of time Series

Lecture 17 Training and Testing Data

Lecture 18 Analysing Output

Lecture 19 Time Series Bitcoin Data

Lecture 20 Classification

Lecture 21 Fruit type Distribution

Lecture 22 Create Training and Test Sets

Lecture 23 Building Logistic Regression

Lecture 24 Building Decision Tree

Lecture 25 K-Nearest Neighbors

Lecture 26 Linear Discriminant Analysis

Lecture 27 Gaussian Naive Bayes

Lecture 28 Plot the Decision Boundary

Lecture 29 Plot the Decision Boundary Continue

Lecture 30 Defining the Problem Statement

Lecture 31 Data Preparation

Lecture 32 Clean up

Lecture 33 Payment Delays

Lecture 34 Standing Credit

Lecture 35 Payments in the Previous Months

Lecture 36 Explore Defaulting

Lecture 37 Absolute Statistics

Lecture 38 Starting with Feature Engineering

Lecture 39 From Variables to Train

Lecture 40 Visualization-Confusion Matrices and AUC Curves

Lecture 41 Creating SNS Plot

Anyone who wants to learn about data and analytics, Data Engineers, Analysts, Architects, Software Engineers, IT operations, Technical managers