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  • 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

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 Algorithms
 
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

  • Some exposure to Programming Languages will be useful
  • Aspiring Machine Learning Engineers
  • Aspiring Data Science Professionals
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  • Section 1 : Introduction to Machine Learning 12 Lectures 02:12:44

    • Lecture 1 :
    • Introduction to Machine Learning Preview
    • Lecture 2 :
    • Machine Learning Terminology.
    • Lecture 3 :
    • History of Machine Learning
    • Lecture 4 :
    • Machine Learning Use Cases and Types
    • Lecture 5 :
    • Role of Data in Machine Learning
    • Lecture 6 :
    • Challenges in Machine Learning
    • Lecture 7 :
    • Machine Learning Life Cycle and Pipelines
    • Lecture 8 :
    • Regression Problems
    • Lecture 9 :
    • Regression Models and Perforance Metrics
    • Lecture 10 :
    • Classification Problems and Performance Metrics
    • Lecture 11 :
    • Optmizing Classificaton Metrics
    • Lecture 12 :
    • Bias and Variance
  • Section 2 : Statistical Techniques 8 Lectures 01:42:25

    • Lecture 1 :
    • Types of Data and Descriptive Statistics
    • Lecture 2 :
    • Random Variables and Normal Distribution
    • Lecture 3 :
    • Histograms and Normal Approximation
    • Lecture 4 :
    • Central Limit Theorem
    • Lecture 5 :
    • Probability Theory
    • Lecture 6 :
    • Binomial Theory - Expected Value and Standard Error
    • Lecture 7 :
    • Hypothesis Testing
    • Lecture 8 :
    • Statistics and Experiments
  • Section 3 : Python for Data Science and Machine Learning 28 Lectures 05:07:06

    • Lecture 1 :
    • Introduction to Python
    • Lecture 2 :
    • Starting with Python with Jupyter Notebook
    • Lecture 3 :
    • Python Variables and Conditions
    • Lecture 4 :
    • Python Iterations 1
    • Lecture 5 :
    • Python Iterations 2
    • Lecture 6 :
    • Python Lists
    • Lecture 7 :
    • Python Tuples
    • Lecture 8 :
    • Python Dictionaries 1
    • Lecture 9 :
    • Python Dictionaries 2
    • Lecture 10 :
    • Python Sets 1
    • Lecture 11 :
    • Python Sets 2
    • Lecture 12 :
    • Numpy Arrays 1
    • Lecture 13 :
    • Numpy Arrays 2
    • Lecture 14 :
    • Numpy Arrays 3
    • Lecture 15 :
    • Pandas Series 1
    • Lecture 16 :
    • Pandas Series 2
    • Lecture 17 :
    • Pandas Series 3
    • Lecture 18 :
    • Pandas Series 4
    • Lecture 19 :
    • Pandas DataFrame 1
    • Lecture 20 :
    • Pandas DataFrame 2
    • Lecture 21 :
    • Pandas DataFrame 3
    • Lecture 22 :
    • Pandas DataFrame 4
    • Lecture 23 :
    • Pandas DataFrame 5
    • Lecture 24 :
    • Pandas DataFrame 6
    • Lecture 25 :
    • Python User Defined Functions
    • Lecture 26 :
    • Python Lambda Functions
    • Lecture 27 :
    • Python Lambda Functions and Date-Time Operations
    • Lecture 28 :
    • Python String Operations
  • Section 4 : Exploratory Data Analysis 8 Lectures 01:35:36

    • Lecture 1 :
    • Exploratory Data Analysis
    • Lecture 2 :
    • Tools and Processes of EDA
    • Lecture 3 :
    • EDA-Project-1
    • Lecture 4 :
    • EDA-Project-2
    • Lecture 5 :
    • EDA-Project-3
    • Lecture 6 :
    • EDA-Project-4
    • Lecture 7 :
    • EDA-Project-5
    • Lecture 8 :
    • EDA-Project-6
  • Section 5 : Linear Regression 13 Lectures 02:47:27

    • Lecture 1 :
    • Linear Regression Introduction
    • Lecture 2 :
    • Training and Cost Function
    • Lecture 3 :
    • Cost Functions and Gradient Descent
    • Lecture 4 :
    • Linear Regression - Practical Approach
    • Lecture 5 :
    • Feature Scaling and Cost Functions
    • Lecture 6 :
    • OLS Assumptions and Testing
    • Lecture 7 :
    • Car Price Prediction
    • Lecture 8 :
    • Data Preparation and Analysis 1
    • Lecture 9 :
    • Data Preparation and Analysis 2
    • Lecture 10 :
    • Data Preparation and Analysis 3
    • Lecture 11 :
    • Model Building
    • Lecture 12 :
    • Model Evaluation and Optmization
    • Lecture 13 :
    • Model Optimization
  • Section 6 : Logistic Regression 8 Lectures 01:47:03

    • Lecture 1 :
    • Logistic Regression Introduction
    • Lecture 2 :
    • Logit Model
    • Lecture 3 :
    • Telecom Churn Case Study
    • Lecture 4 :
    • Data Analysis and Feature Engineering
    • Lecture 5 :
    • Build the Logistic Model
    • Lecture 6 :
    • Model Evaluation - AUC-ROC
    • Lecture 7 :
    • Model Optimization
    • Lecture 8 :
    • Model Optimization
  • Section 7 : Naive Bayes Classification Algorithom 4 Lectures 01:05:38

    • Lecture 1 :
    • Naive Bayes Probability Model
    • Lecture 2 :
    • Naive Bayes Probability Computation
    • Lecture 3 :
    • Employee Attrition Case Study
    • Lecture 4 :
    • Model Building and Optmization
  • Section 8 : Decision Tree Algorithm 6 Lectures 00:59:35

    • Lecture 1 :
    • Decision Tree - Model Concept
    • Lecture 2 :
    • Decision Tree - Learning Steps
    • Lecture 3 :
    • Gini Index and Entropy Measures
    • Lecture 4 :
    • Pruning and Hyperparameter Tuning
    • Lecture 5 :
    • Iris Dataset Case Study
    • Lecture 6 :
    • Model Optimization using Grid Search Cross Validation
  • Section 9 : Random Forest Ensemble Algorithm 4 Lectures 01:06:57

    • Lecture 1 :
    • Ensemble Techniques Bagging and Random Forest
    • Lecture 2 :
    • Random Forest Steps Pruning and Optimization
    • Lecture 3 :
    • Model Building and Hyperparameter Tuning using Grid Search CV
    • Lecture 4 :
    • Optimization Continued
  • Section 10 : Support Vector Machine 5 Lectures 00:46:24

    • Lecture 1 :
    • Support Vector Machine Concepts
    • Lecture 2 :
    • Support Vector Machine Metrics and Polynomial SVM
    • Lecture 3 :
    • 84 Support Vector Machine Project 1
    • Lecture 4 :
    • Support Vector Machine Predictions
    • Lecture 5 :
    • Support Vector Machine - Classifying Polynomial Data
  • Section 11 : Dimensionality Reduction - Principle Component Analysis (PCA) 4 Lectures 00:50:16

    • Lecture 1 :
    • Pricipal Component Analysis - Concepts
    • Lecture 2 :
    • Principal Component Analysis - Computations 1
    • Lecture 3 :
    • Principal Component Analysis - Computations 2
    • Lecture 4 :
    • Principal Component Analysis Practicals
  • Section 12 : Unsupervised Learning with K-Means Clustering 5 Lectures 01:23:53

    • Lecture 1 :
    • Unsupervised Learning - K-Mean Clustering
    • Lecture 2 :
    • K-Means Clustering Computation
    • Lecture 3 :
    • K-Means Clustering Optimization
    • Lecture 4 :
    • K-Means - Data Preparation and Modelling
    • Lecture 5 :
    • K-Means - Model Optimization
  • Section 13 : Deep Learning 1 Lectures 00:00:00

    • Lecture 1 :
    • Introduction to Deep Learning
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I hold a Master's Degree (MSc) from Liverpool John Moores University (LJMU), UK on Artificial Intelligence and Machine Learning (AI/ML). My specialization and research areas are Natural Language Processing (NLP) using Deep Learning Methods such as Siamese Networks, Encoder-Decoder techniques, various Language Embedding methods such as BERT, areas such as Supervised Learning on Semantic Similarity and so on. My expertise area also encompass an array of Machine Learning and Data Science / Predictive Analytics areas including various Supervised, Unsupervised and Clustering methods. I have > 20 Years of experience in the IT Industry, mostly with the Financial Services domain. Starting as a Developer to being an Architect for a number of Years to Leadership position. The key focus and passion is to increase technical breadth and innovation.
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