Machine Learning Deep Learning Python Practical Handson

Code, Develop, Validate & Deploy Machine Learning & Keras Deep Learning Neural Network Models.
Machine Learning Deep Learning Python Practical Handson
File Size :
4.80 GB
Total length :
10h 51m



Abilash Nair


Last update




Machine Learning Deep Learning Python Practical Handson

What you’ll learn

Basics to Advanced Machine Learning & Advanced Deep Learning Algorithms with Live Practice Interviews with Experts
Image Recognition & Keras Deep Learning Neural Network Model Implementation.
Automated Machine Learning Frameworks & Model Deployment Architectures
Basic to Advanced Python with Pandas and Flask API creation
Anomaly Detection Algorithms
Efficient Feature Engineering & Data Pre-Processing
Working with Multiple Data Sets and Algorithm building in Kaggle Cloud.

Machine Learning Deep Learning Python Practical Handson


Willingness to Learn
Basics of Python may be good to have but not mandatory


Interested in the field of Machine Learning? Then this course is for you!Designed & Crafted by AI Solution Expert with 15 + years of relevant and hands on experience into Training , Coaching and Development.Complete Hands-on AI Model Development with Python.  Course Contents are:Understand Machine Learning in depth and in simple process. Fundamentals of Machine LearningUnderstand the Deep Learning Neural Nets with Practical Examples.Understand Image Recognition and Auto Encoders.Machine learning project Life CycleSupervised & Unsupervised LearningData Pre-ProcessingAlgorithm SelectionData Sampling and Cross ValidationFeature EngineeringModel Training and ValidationK -Nearest Neighbor AlgorithmK- Means AlgorithmAccuracy DeterminationVisualization using SeabornYou will be trained to develop various algorithms for supervised & unsupervised methods such as  KNN , K-Means , Random Forest, XGBoost model development. Understanding the fundamentals and core concepts of machine learning model building process with validation and accuracy metric calculation. Determining the optimum model and algorithm. Cross validation and sampling methods would be understood. Data processing concepts with practical guidance and code examples provided through the course. Feature Engineering as critical machine learning process would be explained in easy to understand and yet effective manner.We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.


Section 1: Machine Learning Introduction & AI Project Life Cycle.

Lecture 1 Course Introduction – About the course

Lecture 2 Introduction to Machine Learning Concepts

Lecture 3 Types of Machine Learning

Lecture 4 Types of Supervised Methods – Regression & Classification

Lecture 5 More examples on Regression and Classification

Lecture 6 Machine Learning Algorithms

Lecture 7 Stages of Machine Learning Project Life Cycle

Lecture 8 Architecture : Machine Learning Model Deployment in Production

Section 2: Feature Engineering Process – Most Critical Process

Lecture 9 Feature Engineering Process – The Most Critical Process

Lecture 10 Categorical Feature Encoding

Lecture 11 Random Sampling & Cross Validation – Data Preparation for Modelling

Section 3: Determine Best Machine Learning Model – Accuracy & Error Understanding Process

Lecture 12 Classification Model Accuracy Validation Metric – Part 1

Lecture 13 Regression Model Accuracy Validation Metric

Section 4: Code – Python Crash Course – Quick Deep Dive to Python Fundamentals

Lecture 14 Introduction to Anaconda & Jupiter – Python IDE

Lecture 15 Python Strings , Numbers and Data Types

Lecture 16 Python Logical Operations & Conditional Statements

Lecture 17 Python Logical Operations & Conditional Statements

Lecture 18 Python Reusable Code Functions

Lecture 19 API call pre data conditioning with JSON

Lecture 20 API call , Request and Response, Shallow & Deep Copy

Lecture 21 Exception Handling

Section 5: API creation with Web framework Flask

Lecture 22 Flask introduction

Lecture 23 Flask Request and Response

Lecture 24 Flask File Upload

Lecture 25 Model Prediction API Request with Flask

Section 6: Code – Data Processing with Powerful Pandas Framework

Lecture 26 Pandas Framework – Read Data Files – csv & excel Files using Pandas

Lecture 27 Pandas Framework – Filter Data in Data Frames

Lecture 28 Pandas Framework – Drop Columns & Datatypes

Lecture 29 Pandas Framework – Group Operations on Data

Lecture 30 Pandas Framework – Concat Operations on Data

Lecture 31 Database Query with Pandas

Section 7: Algorithm – K-Nearest Neighbor (KNN) – Supervised Learning

Lecture 32 Euclidian Distance between points

Lecture 33 Algorithm : K- Nearest Neighbor (KNN) Classification

Lecture 34 Algorithm : K- Nearest Neighbor (KNN) Regressor

Lecture 35 Kaggle Cloud Platform Introduction

Lecture 36 Project Code : KNN Classifier Model Training & Validation

Lecture 37 Project Code : KNN Regressor Model Training & Validation

Section 8: Algorithm: Linear Regression – Supervised Learning

Lecture 38 Algorithm : Linear Regression – Find the Best Linear Model Equation

Lecture 39 Algorithm : Linear Regression – Elements of Linear Model and Prediction Strategy

Lecture 40 Project Code : Develop Linear Regression Model

Section 9: Algorithm – Support Vector Machines (SVM) – Supervised Learning

Lecture 41 Algorithm – Support Vector Machines (SVM) Classifier – Supervised Learning

Lecture 42 Support Vector Machines (SVM) Classifier – Find the Best Hyperplane

Lecture 43 Support Vector Machines (SVM) Regression – Find the Best Regression Line

Lecture 44 Project Code : Application of SVM Classifier on Cancer Dataset

Section 10: Algorithm – K – Means – Unsupervised Learning

Lecture 45 Unsupervised Machine Learning Introduction

Lecture 46 Algorithm: K- Means Clustering

Lecture 47 Algorithm: K- Means Clustering Elbow Method

Lecture 48 Project Code : K-Means Clustering Model – Part 1 Training

Lecture 49 Project Code : K-Means Clustering Model – Part 2 Training

Section 11: Algorithm – Density-Based Spatial Clustering (DBSCAN) – Unsupervised Learning

Lecture 50 Algorithm – Density-Based Spatial Clustering (DBSCAN) – Working Principle

Lecture 51 Standardization of Data Prior to Clustering Algorithm

Lecture 52 Project Code : DBSCAN on Water Content Analysis & it’s Advantage

Section 12: Algorithm – Time Series Analytics – Univariate & Multivariate

Lecture 53 Introduction to Univariate Data

Lecture 54 Introduction to Univariate Time Series

Lecture 55 Time Series Composition – Part 1

Lecture 56 Time Series Composition – Part 2

Lecture 57 Univariate vs Multivariate Modelling

Lecture 58 Algorithm – Introduction to FBProphet

Lecture 59 FBProphet Algorithm Documentation – Official Page

Lecture 60 Project Code: FBProphet Algorithm – Model Training

Lecture 61 Time Series Training & Validation Process

Lecture 62 Project Code : FBProphet Model Validation Accuracy, Forecasting & Decomposition

Section 13: Deep Learning Neural Networks Design & Model Implementation

Lecture 63 Introduction to Deep Neural Network

Lecture 64 Deep Neural Network Activation Functions

Lecture 65 Single Neuron Processing in a Deep Neural Network Architecture

Lecture 66 Gradient of Loss

Lecture 67 Deep Neural Network Weights & Cost Functions

Lecture 68 Global Minimum & Gradient Descent with Weight Optimization

Lecture 69 Back Propagation & Chain Rule

Lecture 70 Code: Keras Deep Learning Classifier Implementation

Lecture 71 Code: Keras Deep Learning Regression Implementation

Section 14: based AutoML Frameworks & Deep Neural Networks Implementation

Lecture 72 AutoML Frameworks

Section 15: Image Recognition & Advanced Deep Learning Concepts & Implementations

Lecture 73 Image Recognition – Convolutional Neural Network Architecture & Processing

Lecture 74 Code: Multiclass Image Recognition Keras Implementation

Lecture 75 Deep Learning Auto Encoder Models Benefits

Lecture 76 Deep Learning Encoders-Decoders – Auto Encoder Models

Lecture 77 Code: Keras Deep Learning Auto Encoder Models

Lecture 78 Understanding Deep Learning Siamese Network

Lecture 79 Deep Learning Siamese Network Architecture

Section 16: Join LIVE Mock Interview – SWOT analysis & Recommendation for Success

Lecture 80 Book your Live Interview

Data Science Begineers,Researchers & PhD Scholars,Professionals

Course Information:

Udemy | English | 10h 51m | 4.80 GB
Created by: Abilash Nair

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