Machine Learning Course Description

  • Why Learn Machine Learning Certification?

     

    Machine Learning is the technology whose name can be now heard from all the corners of the world. The requirement for proficiency in this promising technology has never been so high.

    It is estimated that the market size of machine learning will increase from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022 at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.

    Why learn Machine Learning

  • What are the course objectives?

     

    Machine Learning takes Artificial Intelligence to the next level. It has changed the way how people look at data computing and digital transformations. Several innovative applications that will launch in the future are backed by machine learning technology. It is due to the fact that machine learning technology has the ability to process and interpret a large amount of complicated data automatically. Besides, it is cheap as well as fast technology. Machine Learning has found its applications in areas like self-driving cars, facial recognition, recommendation engines, and facial recognition.

    According to payscale.com, a Machine Learning Engineer earns an average salary of $134,293 (USD). As a rising demand has been observed for the skilled Machine Learning professionals, candidates can take up this Machine Learning course in Mumbai to get the job competency and hands-on experience with Machine Learning.

  • What skills you learn in Machine Learning Certification Training?

    The candidates taking the Machine Learning course in Mumbai will be able to do the following:

    • Be prepared to model a wide variety of robust machine learning algorithms including deep learning, clustering, and recommendation systems
    • Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
    • Enhance your knowledge of principles, algorithms, and applications of machine learning through a hands-on approach, which includes working on 28 projects and one Capstone project.
    • Learn the concepts of supervised, unsupervised, and reinforcement learning and modeling.
    • Explain the machine learning theoretical concepts with its practical aspects.
    • Explore the mathematical and heuristic aspects of machine learning.

  • Who should take Machine Learning Course?

    In all of the major industries, there is a considerable requirement for professionals who are proficient in Machine Learning. This Machine Learning Course is, therefore, ideal for professionals who have an intermediate level of working experience. The following professionals will specifically benefit from the Machine course:

    • Experienced professionals who would like to harness machine learning in their fields to get more insights
    • Analytics professionals who want to work in machine learning or artificial intelligence
    • Business analysts who want to understand data science techniques
    • Developers aspiring to be a data scientist or machine learning engineer
    • Graduates looking to build a career in data science and machine learning
    • Information architects who want to gain expertise in machine learning algorithms
    • Analytics managers who are leading a team of analysts

  • What Machine Learning Real-time Projects you will complete during the course?

    We make sure that our Machine Learning course provides coding experience along with hands-on projects. While beginning with the concepts, we also provide theoretical motivation and mathematical problem formulation.

    This course includes one primary capstone project and more than 25 ancillary exercises based on 17 machine learning algorithms.

    Capstone Project Details:

    Project Name: Predicting house prices in California

    Description: The project involves building a model that predicts median house values in Californian districts. You will be given metrics such as population, median income, median housing price and so on for each block group in California. Block groups are the smallest geographical unit for which the US Census Bureau publishes sample data (a lock group typically has a population of 600 to 3,000 people). The model you build should learn from this data and be able to predict the median housing price in any district.

    Concept covered: Techniques of Machine Learning

    Case Study 1: Predict whether the houses will be purchased or not by the consumers, from the given dataset, provided with their salary and age

    Project 1: In reference to the above problem statement, what issues can be observed in the plot generated by the code?

    Project  2: What is the estimated cost of the houses with areas 1700 and 1900?

    Concept covered: Data Preprocessing

    Case Study 2: Using the information provided in the dataset, demonstrate the methods to handle missing data, categorical data, and data standardization

    Project 3: Review the training dataset (Excel file). Observe that weight is missing for the fifth and eighth rows. For the mentioned rows, what are the values computed by the imputer?

    Project 4: In the tutorial code, find the call to the Imputer class. Replace the strategy parameter from “mean” to “median” and rerun it. What is the new value assigned to the blank fields Weight and Height for the two rows?

    Project 5: In the code snippet given below in the tutorial, why does the array X have 5 columns instead of 3 columns as before?

    Case Study 3: Demonstrate how to reduce data dimensions from 3D to 2D using the information provided

    Project 6: What does the hyperplane shadow represent in the PCA output chart on random data?

    Project 7: What is the reconstruction error after PCA transformation? Give interpretation.

    Concept Covered: Regression

    Case Study 4: Demonstrate how to reduce data dimensions from 3D to 2D using the information provided

    Project 8: Modify the degree of the polynomial from Polynomial Features (degree = 1) to 1, 2, 3, and interpret the resulting regression plot. Specify if it is under fitted, right-fitted, or overfitted?

    Project 9: Predict the insurance claims for age 70 with polynomial regression n with degree 2 and linear regression.

    Project 10: In the code snippet given below in the tutorial, why does the array X have 5 columns instead of 3 columns as before?

    Case Study 5: Predict insurance premium per year based on a person’s age using Decision Trees using the information provided in the dataset

    Project 11: Modify the code to predict insurance claim values for people over 55 years of age in the given dataset.

    Case Study 6: Generate random quadratic data and demonstrate Decision Tree regression

    Project 12: Modify the max_depth from 2 to 3 or 4, and observe the output.

    Project 13: Modify the max_depth to 20, and observe the output

    Project 14: What is the class prediction for petal_length = 3 cm and petal_width = 1 cm for the max_depth = 2?

    Project 15: Explain the Decision Tree regression graphs produced when max_depths are 2 and 3. How many leaf nodes exist in the two cases? What does the average value represent these two situations? Use the information provided

    Project 16: Modify the regularization parameter min_sample_leaf from 10 to 6, and check the output of Decision Tree regression. What result do you observe? Explain the reason.

    Case Study 7: Use Random Forests to predict insurance per year based on the age of a person.

    Project 17: What is the output insurance value for individuals aged 60 and with n_estimators = 10?

    Case Study 8:  Demonstrate various regression techniques over a random dataset using the information provided in the dataset

    Project 18: The program shows a learning process when the values of the learning rate η are 0.02, 0.1, and 0.5. Interpret these charts.

    Project 19: The program shows the learning process when the values of the learning rate η are 0.02, 0.1, and 0.5. Try modifying the values to 0.001, 0.25, and 0.9 and observe the output. Give your interpretation.

    Concept Covered: Classification

    Case Study 9: Predict if the houses will be purchased by the consumers, given their salary and age. Use the information provided in the dataset

    Project 20: Typically, the nearest_neighbors for testing class in KNN has the value 5. Modify the code with the value of nearest_neighbours to 2 and 20, and note down your observations.

    Case Study 10: Classify IRIS dataset using SVM, and demonstrate how Kernel SVMs can help classify non-linear data.

    Project 21: Modify the kernel trick to linear from RBF to check the type of classifier that is produced for the XOR data in this program. Interpret the data.

    Project 22:  For the Iris dataset, add new code at the end of this program to produce classification for RBF kernel trick with gamma = 1.0. Discuss the result.

    Case Study 11: Use Decision Trees to classify IRIS flower dataset. Use the information provided.

    Project 23: Run decision tree on the IRIS dataset with max depths of 3 and 4, and display the tree output.

    Project 24:  Predict and print class probability for Iris flower instance with petal_len 1 cm and petal_width 0.5 cm.

    Case Study 12: Classify the IRIS flower dataset using various classification algorithms. Use the information provided.

    Project 25: Add Logistic Regression classification to the program and compare classification output to previous algorithms?

    Concept Covered: Unsupervised Learning with Clustering

    Case Study 13: Demonstrate Clustering algorithm and the Elbow method on a random dataset.

    Project 26:  Change the number of clusters k to 2, and record the observations.

    Project 27:  Modify the n_samples from 150 to 15000 and the number of centers to 4 with n_clusters as 3. Find the output, and record the observations.

    Project 28:  Change the code to set the n_samples from 150 to 15000 and the number of centers to 4, keeping n_clusters at 4. Find the output.

    Project 29: Modify the number of clusters k to 6, and record your observations.

  • What are the prerequisites for attending Machine Learning Training?

     

    The prerequisites for this Machine Learning course are:

    • Knowledge of basic high school mathematics
    • Clarity on the concepts of Python programming
    • Fundamental understanding of statistics

    Simplilearn provides a free Python course along with the Machine Learning course to help you brush up your knowledge of statistics and mathematics concepts.

Machine Learning Course Preview

    • Lesson 01 Course Introduction

      06:41
      • Course Introduction
        05:31
      • Accessing Practice Lab
        01:10
    • Lesson 02 Introduction to AI and Machine Learning

      19:36
      • 2.1 Learning Objectives
        00:43
      • 2.2 Emergence of Artificial Intelligence
        01:56
      • 2.3 Artificial Intelligence in Practice
        01:48
      • 2.4 Sci-Fi Movies with the Concept of AI
        00:22
      • 2.5 Recommender Systems
        00:45
      • 2.6 Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part A
        02:47
      • 2.7 Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part B
        01:23
      • 2.8 Definition and Features of Machine Learning
        01:30
      • 2.9 Machine Learning Approaches
        01:48
      • 2.10 Machine Learning Techniques
        02:21
      • 2.11 Applications of Machine Learning: Part A
        01:34
      • 2.12 Applications of Machine Learning: Part B
        02:11
      • 2.13 Key Takeaways
        00:28
      • Knowledge Check
    • Lesson 03 Data Preprocessing

      36:19
      • 3.1 Learning Objectives
        00:38
      • 3.2 Data Exploration Loading Files: Part A
        02:52
      • 3.2 Data Exploration Loading Files: Part B
        01:56
      • 3.3 Demo: Importing and Storing Data
        01:27
      • Practice: Automobile Data Exploration - A
      • 3.4 Data Exploration Techniques: Part A
        02:56
      • 3.5 Data Exploration Techniques: Part B
        02:47
      • 3.6 Seaborn
        02:18
      • 3.7 Demo: Correlation Analysis
        02:38
      • Practice: Automobile Data Exploration - B
      • 3.8 Data Wrangling
        01:27
      • 3.9 Missing Values in a Dataset
        01:55
      • 3.10 Outlier Values in a Dataset
        01:49
      • 3.11 Demo: Outlier and Missing Value Treatment
        04:18
      • Practice: Data Exploration - C
      • 3.12 Data Manipulation
        00:47
      • 3.13 Functionalities of Data Object in Python: Part A
        01:49
      • 3.14 Functionalities of Data Object in Python: Part B
        01:33
      • 3.15 Different Types of Joins
        01:32
      • 3.16 Typecasting
        01:23
      • 3.17 Demo: Labor Hours Comparison
        01:54
      • Practice: Data Manipulation
      • 3.18 Key Takeaways
        00:20
      • Knowledge Check
      • Storing Test Results
    • Lesson 04 Supervised Learning

      1:21:04
      • 4.1 Learning Objectives
        00:31
      • 4.2 Supervised Learning
        02:17
      • 4.3 Supervised Learning- Real-Life Scenario
        00:53
      • 4.4 Understanding the Algorithm
        00:52
      • 4.5 Supervised Learning Flow
        01:50
      • 4.6 Types of Supervised Learning: Part A
        01:54
      • 4.7 Types of Supervised Learning: Part B
        02:03
      • 4.8 Types of Classification Algorithms
        01:01
      • 4.9 Types of Regression Algorithms: Part A
        03:20
      • 4.10 Regression Use Case
        00:34
      • 4.11 Accuracy Metrics
        01:23
      • 4.12 Cost Function
        01:48
      • 4.13 Evaluating Coefficients
        00:53
      • 4.14 Demo: Linear Regression
        13:47
      • Practice: Boston Homes - A
      • 4.15 Challenges in Prediction
        01:45
      • 4.16 Types of Regression Algorithms: Part B
        02:40
      • 4.17 Demo: Bigmart
        21:55
      • Practice: Boston Homes - B
      • 4.18 Logistic Regression: Part A
        01:58
      • 4.19 Logistic Regression: Part B
        01:38
      • 4.20 Sigmoid Probability
        02:05
      • 4.21 Accuracy Matrix
        01:36
      • 4.22 Demo: Survival of Titanic Passengers
        14:07
      • Practice: Iris Species
      • 4.23 Key Takeaways
        00:14
      • Knowledge Check
      • Health Insurance Cost
    • Lesson 05 Feature Engineering

      27:52
      • 5.1 Learning Objectives
        00:27
      • 5.2 Feature Selection
        01:28
      • 5.3 Regression
        00:53
      • 5.4 Factor Analysis
        01:57
      • 5.5 Factor Analysis Process
        01:05
      • 5.6 Principal Component Analysis (PCA)
        02:31
      • 5.7 First Principal Component
        02:43
      • 5.8 Eigenvalues and PCA
        02:32
      • 5.9 Demo: Feature Reduction
        05:47
      • Practice: PCA Transformation
      • 5.10 Linear Discriminant Analysis
        02:27
      • 5.11 Maximum Separable Line
        00:44
      • 5.12 Find Maximum Separable Line
        03:12
      • 5.13 Demo: Labeled Feature Reduction
        01:53
      • Practice: LDA Transformation
      • 5.14 Key Takeaways
        00:13
      • Knowledge Check
      • Simplifying Cancer Treatment
    • Lesson 06 Supervised Learning Classification

      55:43
      • 6.1 Learning Objectives
        00:34
      • 6.2 Overview of Classification
        02:05
      • Classification: A Supervised Learning Algorithm
        00:52
      • 6.4 Use Cases of Classification
        02:37
      • 6.5 Classification Algorithms
        00:16
      • 6.6 Decision Tree Classifier
        02:17
      • 6.7 Decision Tree Examples
        01:45
      • 6.8 Decision Tree Formation
        00:47
      • 6.9 Choosing the Classifier
        02:55
      • 6.10 Overfitting of Decision Trees
        01:00
      • 6.11 Random Forest Classifier- Bagging and Bootstrapping
        02:22
      • 6.12 Decision Tree and Random Forest Classifier
        01:06
      • Performance Measures: Confusion Matrix
        02:21
      • Performance Measures: Cost Matrix
        02:06
      • 6.15 Demo: Horse Survival
        08:30
      • Practice: Loan Risk Analysis
      • 6.16 Naive Bayes Classifier
        01:28
      • 6.17 Steps to Calculate Posterior Probability: Part A
        01:44
      • 6.18 Steps to Calculate Posterior Probability: Part B
        02:21
      • 6.19 Support Vector Machines : Linear Separability
        01:05
      • 6.20 Support Vector Machines : Classification Margin
        02:05
      • 6.21 Linear SVM : Mathematical Representation
        02:04
      • 6.22 Non-linear SVMs
        01:06
      • 6.23 The Kernel Trick
        01:19
      • 6.24 Demo: Voice Classification
        10:42
      • Practice: College Classification
      • 6.25 Key Takeaways
        00:16
      • Knowledge Check
      • Classify Kinematic Data
    • Lesson 07 Unsupervised Learning

      28:26
      • 7.1 Learning Objectives
        00:29
      • 7.2 Overview
        01:48
      • 7.3 Example and Applications of Unsupervised Learning
        02:17
      • 7.4 Clustering
        01:49
      • 7.5 Hierarchical Clustering
        02:28
      • 7.6 Hierarchical Clustering Example
        02:01
      • 7.7 Demo: Clustering Animals
        05:39
      • Practice: Customer Segmentation
      • 7.8 K-means Clustering
        01:46
      • 7.9 Optimal Number of Clusters
        01:24
      • 7.10 Demo: Cluster Based Incentivization
        08:32
      • Practice: Image Segmentation
      • 7.11 Key Takeaways
        00:13
      • Knowledge Check
      • Clustering Image Data
    • Lesson 08 Time Series Modeling

      37:44
      • 8.1 Learning Objectives
        00:24
      • 8.2 Overview of Time Series Modeling
        02:16
      • 8.3 Time Series Pattern Types: Part A
        02:16
      • 8.4 Time Series Pattern Types: Part B
        01:19
      • 8.5 White Noise
        01:07
      • 8.6 Stationarity
        02:13
      • 8.7 Removal of Non-Stationarity
        02:13
      • 8.8 Demo: Air Passengers - A
        14:33
      • Practice: Beer Production - A
      • 8.9 Time Series Models: Part A
        02:14
      • 8.10 Time Series Models: Part B
        01:28
      • 8.11 Time Series Models: Part C
        01:51
      • 8.12 Steps in Time Series Forecasting
        00:37
      • 8.13 Demo: Air Passengers - B
        05:01
      • Practice: Beer Production - B
      • 8.14 Key Takeaways
        00:12
      • Knowledge Check
      • IMF Commodity Price Forecast
    • Lesson 09 Ensemble Learning

      35:41
      • 9.01 Ensemble Learning
        00:24
      • 9.2 Overview
        02:41
      • 9.3 Ensemble Learning Methods: Part A
        02:28
      • 9.4 Ensemble Learning Methods: Part B
        02:37
      • 9.5 Working of AdaBoost
        01:43
      • 9.6 AdaBoost Algorithm and Flowchart
        02:28
      • 9.7 Gradient Boosting
        02:36
      • 9.8 XGBoost
        02:23
      • 9.9 XGBoost Parameters: Part A
        03:15
      • 9.10 XGBoost Parameters: Part B
        02:30
      • 9.11 Demo: Pima Indians Diabetes
        04:14
      • Practice: Linearly Separable Species
      • 9.12 Model Selection
        02:08
      • 9.13 Common Splitting Strategies
        01:45
      • 9.14 Demo: Cross Validation
        04:18
      • Practice: Model Selection
      • 9.15 Key Takeaways
        00:11
      • Knowledge Check
      • Tuning Classifier Model with XGBoost
    • Lesson 10 Recommender Systems

      25:45
      • 10.1 Learning Objectives
        00:28
      • 10.2 Introduction
        02:17
      • 10.3 Purposes of Recommender Systems
        00:45
      • 10.4 Paradigms of Recommender Systems
        02:45
      • 10.5 Collaborative Filtering: Part A
        02:14
      • 10.6 Collaborative Filtering: Part B
        01:58
      • 10.7 Association Rule Mining
        01:47
      • Association Rule Mining: Market Basket Analysis
        01:43
      • 10.9 Association Rule Generation: Apriori Algorithm
        00:53
      • 10.10 Apriori Algorithm Example: Part A
        02:11
      • 10.11 Apriori Algorithm Example: Part B
        01:18
      • 10.12 Apriori Algorithm: Rule Selection
        02:52
      • 10.13 Demo: User-Movie Recommendation Model
        04:19
      • Practice: Movie-Movie recommendation
      • 10.14 Key Takeaways
        00:15
      • Knowledge Check
      • Book Rental Recommendation
    • Lesson 11 Text Mining

      43:58
      • 11.1 Learning Objectives
        00:22
      • 11.2 Overview of Text Mining
        02:11
      • 11.3 Significance of Text Mining
        01:26
      • 11.4 Applications of Text Mining
        02:23
      • 11.5 Natural Language ToolKit Library
        02:35
      • 11.6 Text Extraction and Preprocessing: Tokenization
        00:33
      • 11.7 Text Extraction and Preprocessing: N-grams
        00:55
      • 11.8 Text Extraction and Preprocessing: Stop Word Removal
        01:24
      • 11.9 Text Extraction and Preprocessing: Stemming
        00:44
      • 11.10 Text Extraction and Preprocessing: Lemmatization
        00:35
      • 11.11 Text Extraction and Preprocessing: POS Tagging
        01:17
      • 11.12 Text Extraction and Preprocessing: Named Entity Recognition
        00:54
      • 11.13 NLP Process Workflow
        00:53
      • 11.14 Demo: Processing Brown Corpus
        10:05
      • Practice: Wiki Corpus
      • 11.15 Structuring Sentences: Syntax
        01:54
      • 11.16 Rendering Syntax Trees
        00:55
      • 11.17 Structuring Sentences: Chunking and Chunk Parsing
        01:38
      • 11.18 NP and VP Chunk and Parser
        01:39
      • 11.19 Structuring Sentences: Chinking
        01:44
      • 11.20 Context-Free Grammar (CFG)
        01:56
      • 11.21 Demo: Structuring Sentences
        07:46
      • Practice: Airline Sentiment
      • 11.22 Key Takeaways
        00:09
      • Knowledge Check
      • FIFA World Cup
    • Lesson 12 Project Highlights

      02:40
      • Project Highlights
        02:40
      • Uber Fare Prediction
      • Amazon - Employee Access
    • Lesson 00 - Course Overview

      04:34
      • 0.001 Course Overview
        04:34
    • Lesson 01 - Data Science Overview

      20:27
      • 1.001 Introduction to Data Science
        08:42
      • 1.002 Different Sectors Using Data Science
        05:59
      • 1.003 Purpose and Components of Python
        05:02
      • 1.4 Quiz
      • 1.005 Key Takeaways
        00:44
    • Lesson 02 - Data Analytics Overview

      18:20
      • 2.001 Data Analytics Process
        07:21
      • 2.2 Knowledge Check
      • 2.3 Exploratory Data Analysis(EDA)
      • 2.4 EDA-Quantitative Technique
      • 2.005 EDA - Graphical Technique
        00:57
      • 2.006 Data Analytics Conclusion or Predictions
        04:30
      • 2.007 Data Analytics Communication
        02:06
      • 2.8 Data Types for Plotting
      • 2.009 Data Types and Plotting
        02:29
      • 2.11 Quiz
      • 2.012 Key Takeaways
        00:57
      • 2.10 Knowledge Check
    • Lesson 03 - Statistical Analysis and Business Applications

      23:53
      • 3.001 Introduction to Statistics
        01:31
      • 3.2 Statistical and Non-statistical Analysis
      • 3.003 Major Categories of Statistics
        01:34
      • 3.4 Statistical Analysis Considerations
      • 3.005 Population and Sample
        02:15
      • 3.6 Statistical Analysis Process
      • 3.007 Data Distribution
        01:48
      • 3.8 Dispersion
      • 3.9 Knowledge Check
      • 3.010 Histogram
        03:59
      • 3.11 Knowledge Check
      • 3.012 Testing
        08:18
      • 3.13 Knowledge Check
      • 3.014 Correlation and Inferential Statistics
        02:57
      • 3.15 Quiz
      • 3.016 Key Takeaways
        01:31
    • Lesson 04 - Python Environment Setup and Essentials

      23:58
      • 4.001 Anaconda
        02:54
      • 4.2 Installation of Anaconda Python Distribution (contd.)
      • 4.003 Data Types with Python
        13:28
      • 4.004 Basic Operators and Functions
        06:26
      • 4.5 Quiz
      • 4.006 Key Takeaways
        01:10
    • Lesson 05 - Mathematical Computing with Python (NumPy)

      30:31
      • 5.001 Introduction to Numpy
        05:30
      • 5.2 Activity-Sequence it Right
      • 5.003 Demo 01-Creating and Printing an ndarray
        04:50
      • 5.4 Knowledge Check
      • 5.5 Class and Attributes of ndarray
      • 5.006 Basic Operations
        07:04
      • 5.7 Activity-Slice It
      • 5.8 Copy and Views
      • 5.009 Mathematical Functions of Numpy
        05:01
      • 5.10 Assignment 01
      • 5.011 Assignment 01 Demo
        03:55
      • 5.12 Assignment 02
      • 5.013 Assignment 02 Demo
        03:16
      • 5.14 Quiz
      • 5.015 Key Takeaways
        00:55
    • Lesson 06 - Scientific computing with Python (Scipy)

      23:35
      • 6.001 Introduction to SciPy
        06:57
      • 6.002 SciPy Sub Package - Integration and Optimization
        05:51
      • 6.3 Knowledge Check
      • 6.4 SciPy sub package
      • 6.005 Demo - Calculate Eigenvalues and Eigenvector
        01:36
      • 6.6 Knowledge Check
      • 6.007 SciPy Sub Package - Statistics, Weave and IO
        05:46
      • 6.8 Assignment 01
      • 6.009 Assignment 01 Demo
        01:20
      • 6.10 Assignment 02
      • 6.011 Assignment 02 Demo
        00:55
      • 6.12 Quiz
      • 6.013 Key Takeaways
        01:10
    • Lesson 07 - Data Manipulation with Pandas

      47:34
      • 7.001 Introduction to Pandas
        12:29
      • 7.2 Knowledge Check
      • 7.003 Understanding DataFrame
        05:31
      • 7.004 View and Select Data Demo
        05:34
      • 7.005 Missing Values
        03:16
      • 7.006 Data Operations
        09:56
      • 7.7 Knowledge Check
      • 7.008 File Read and Write Support
        00:31
      • 7.9 Knowledge Check-Sequence it Right
      • 7.010 Pandas Sql Operation
        02:00
      • 7.11 Assignment 01
      • 7.012 Assignment 01 Demo
        04:09
      • 7.13 Assignment 02
      • 7.014 Assignment 02 Demo
        02:34
      • 7.15 Quiz
      • 7.016 Key Takeaways
        01:34
    • Lesson 08 - Machine Learning with Scikit–Learn

      1:02:10
      • 8.001 Machine Learning Approach
        03:57
      • 8.002 Steps 1 and 2
        01:00
      • 8.3 Steps 3 and 4
      • 8.004 How it Works
        01:24
      • 8.005 Steps 5 and 6
        01:54
      • 8.006 Supervised Learning Model Considerations
        00:30
      • 8.7 Knowledge Check
      • 8.008 Scikit-Learn
        02:10
      • 8.9 Knowledge Check
      • 8.010 Supervised Learning Models - Linear Regression
        11:19
      • 8.011 Supervised Learning Models - Logistic Regression
        08:43
      • 8.012 Unsupervised Learning Models
        10:40
      • 8.013 Pipeline
        02:37
      • 8.014 Model Persistence and Evaluation
        05:45
      • 8.16 Assignment 01
      • 8.15 Knowledge Check
      • 8.017 Assignment 01
        05:45
      • 8.18 Assignment 02
      • 8.019 Assignment 02
        05:14
      • 8.20 Quiz
      • 8.021 Key Takeaways
        01:12
    • Lesson 09 - Natural Language Processing with Scikit Learn

      49:03
      • 9.001 NLP Overview
        10:42
      • 9.2 NLP Applications
      • 9.3 Knowledge Check
      • 9.004 NLP Libraries-Scikit
        12:29
      • 9.5 Extraction Considerations
      • 9.006 Scikit Learn-Model Training and Grid Search
        10:17
      • 9.7 Assignment 01
      • 9.008 Demo Assignment 01
        06:32
      • 9.9 Assignment 02
      • 9.010 Demo Assignment 02
        08:00
      • 9.11 Quiz
      • 9.012 Key Takeaway
        01:03
    • Lesson 10 - Data Visualization in Python using matplotlib

      32:43
      • 10.001 Introduction to Data Visualization
        08:01
      • 10.2 Knowledge Check
      • 10.3 Line Properties
      • 10.004 (x,y) Plot and Subplots
        10:01
      • 10.5 Knowledge Check
      • 10.006 Types of Plots
        09:32
      • 10.7 Assignment 01
      • 10.008 Assignment 01 Demo
        02:23
      • 10.9 Assignment 02
      • 10.010 Assignment 02 Demo
        01:47
      • 10.11 Quiz
      • 10.012 Key Takeaways
        00:59
    • Lesson 11 - Web Scraping with BeautifulSoup

      52:26
      • 11.001 Web Scraping and Parsing
        12:50
      • 11.2 Knowledge Check
      • 11.003 Understanding and Searching the Tree
        12:56
      • 11.4 Navigating options
      • 11.005 Demo3 Navigating a Tree
        04:22
      • 11.6 Knowledge Check
      • 11.007 Modifying the Tree
        05:37
      • 11.008 Parsing and Printing the Document
        09:05
      • 11.9 Assignment 01
      • 11.010 Assignment 01 Demo
        01:55
      • 11.11 Assignment 02
      • 11.012 Assignment 02 demo
        04:57
      • 11.13 Quiz
      • 11.014 Key takeaways
        00:44
    • Lesson 12 - Python integration with Hadoop MapReduce and Spark

      40:39
      • 12.001 Why Big Data Solutions are Provided for Python
        04:55
      • 12.2 Hadoop Core Components
      • 12.003 Python Integration with HDFS using Hadoop Streaming
        07:20
      • 12.004 Demo 01 - Using Hadoop Streaming for Calculating Word Count
        08:52
      • 12.5 Knowledge Check
      • 12.006 Python Integration with Spark using PySpark
        07:43
      • 12.007 Demo 02 - Using PySpark to Determine Word Count
        04:12
      • 12.8 Knowledge Check
      • 12.9 Assignment 01
      • 12.010 Assignment 01 Demo
        02:47
      • 12.11 Assignment 02
      • 12.012 Assignment 02 Demo
        03:30
      • 12.13 Quiz
      • 12.014 Key takeaways
        01:20
    • Math Refresher

      30:36
      • Math Refresher
        30:36
    • Lesson 1 Introduction

      02:55
      • 1.1 Introduction
        02:55
    • Lesson 2 Sample or population data

      03:56
      • 2.1 Sample or population data
        03:56
    • Lesson 3 The fundamentals of descriptive statistics

      21:18
      • 3.1 The fundamentals of descriptive statistics
        03:18
      • 3.2 Levels of measurement
        02:57
      • 3.3 Categorical variables. Visualization techniques for categorical variables
        04:06
      • 3.4 Numerical variables. Using a frequency distribution table
        03:24
      • 3.5 Histogram charts
        02:27
      • 3.6 Cross tables and scatter plots
        05:06
    • Lesson 4 Measures of central tendency, asymmetry, and variability

      25:17
      • 4.1 Measures of central tendency, asymmetry, and variability
        04:24
      • 4.2 Measuring skewness
        02:43
      • 4.3 Measuring how data is spread out calculating variance
        05:58
      • 4.4 Standard deviation and coefficient of variation
        04:54
      • 4.5 Calculating and understanding covariance
        03:31
      • 4.6 The correlation coefficient
        03:47
    • Lesson 5 Practical example descriptive statistics

      14:30
      • 5.1 Practical example descriptive statistics
        14:30
    • Lesson 6 Distributions

      16:17
      • 6.1 Distributions
        01:02
      • 6.2 What is a distribution
        03:40
      • 6.3 The Normal distribution
        03:45
      • 6.4 The standard normal distribution
        02:51
      • 6.5 Understanding the central limit theorem
        03:40
      • 6.6 Standard error
        01:19
    • Lesson 7 Estimators and Estimates

      23:36
      • 7.1 Estimators and Estimates
        02:36
      • 7.2 Confidence intervals - an invaluable tool for decision making
        06:31
      • 7.3 Calculating confidence intervals within a population with a known variance
        02:30
      • 7.4 Student’s T distribution
        03:14
      • 7.5 Calculating confidence intervals within a population with an unknown variance
        04:07
      • 7.6 What is a margin of error and why is it important in Statistics
        04:38
    • Lesson 8 Confidence intervals advanced topics

      14:27
      • 8.1 Confidence intervals advanced topics
        04:47
      • 8.2 Calculating confidence intervals for two means with independent samples (part One)
        04:36
      • 8.3 Calculating confidence intervals for two means with independent samples (part two)
        03:40
      • 8.4 Calculating confidence intervals for two means with independent samples (part three)
        01:24
    • Lesson 9 Practical example inferential statistics

      09:37
      • 9.1 Practical example inferential statistics
        09:37
    • Lesson 10 Hypothesis testing Introduction

      12:36
      • 10.1 Hypothesis testing Introduction
        04:56
      • 10.2 Establishing a rejection region and a significance level
        04:20
      • 10.3 Type I error vs Type II error
        03:20
    • Lesson 11 Hypothesis testing Let's start testing!

      26:39
      • 11.1 Hypothesis testing Let's start testing!
        06:07
      • 11.2 What is the p-value and why is it one of the most useful tool for statisticians
        03:55
      • 11.3 Test for the mean. Population variance unknown
        04:26
      • 11.4 Test for the mean. Dependent samples
        04:45
      • 11.5 Test for the mean. Independent samples (Part One)
        03:38
      • 11.6 Test for the mean. Independent samples (Part Two)
        03:48
    • Lesson 12 Practical example hypothesis testing

      06:31
      • 12.1 Practical example hypothesis testing
        06:31
    • Lesson 13 The fundamentals of regression analysis

      18:32
      • 13.1 The fundamentals of regression analysis
        01:02
      • 13.2 Correlation and causation
        04:06
      • 13.3 The linear regression model made easy
        05:02
      • 13.4 What is the difference between correlation and regression
        01:28
      • 13.5 A geometrical representation of the linear regression model
        01:18
      • 13.6 A practical example - Reinforced learning
        05:36
    • Lesson 14 Subtleties of regression analysis

      23:25
      • 14.1 Subtleties of regression analysis
        02:04
      • 14.2 What is Rsquared and how does it help us
        05:00
      • 14.3 The ordinary least squares setting and its practical applications
        02:08
      • 14.4 Studying regression tables
        04:34
      • 14.5 The multiple linear regression model
        02:42
      • 14.6 Adjusted R-squared
        04:57
      • 14.7 What does the F-statistic show us and why we need to understand it
        02:00
    • Lesson 15 Assumptions for linear regression analysis

      19:16
      • 15.1 Assumptions for linear regression analysis
        02:11
      • 15.2 Linearity
        01:40
      • 15.3 No endogeneity
        03:43
      • 15.4 Normality and homoscedasticity
        05:09
      • 15.5 No autocorrelation
        03:11
      • 15.6 No multicollinearity
        03:22
    • Lesson 16 Dealing with categorical data

      05:20
      • 16.1 Dealing with categorical data
        05:20
    • Lesson 17 Practical example regression analysis

      14:42
      • 17.1 Practical example regression analysis
        14:42
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Machine Learning Exam & Certification

  • What do I need to do to unlock my Simplilearn certificate?

    Depending on the learning mode, Simplilearn's Machine Learning Course Completion Certificate can be obtained if:

    • The candidate completes one batch of Online Classroom training and finishes one project
    • The candidate completes 85% of the Self-Paced Course and finishes one project

  • What are the prerequisites for learning Machine Learning?

     

    The prerequisites for this Machine Learning course are:

    • Knowledge of basic high school mathematics
    • Clarity on the concepts of Python programming
    • Fundamental understanding of statistics

    Simplilearn provides a free Python course along with the Machine Learning course to help you brush up your knowledge of statistics and mathematics concepts.

  • Who provides the certification?

     

    The Machine Learning Certification will be provided by Simplilearn to the candidates who complete the course successfully.

  • Is this course accredited?

     

    No, the Machine Learning course offered by Simplilearn is not accredited officially.

  • How long does it to take to complete the Machine Learning certification course?

     

    Candidates need to spend 45 - 50 hours of learning for the successful completion of the Machine Learning certification course.

  • How long does Simplilearn's certificate for machine learning course valid for?

     

    Simplilearn’s Machine Learning Certification is effective for the lifetime.

  • What do I need to do to unlock my Simplilearn certificate?

    Online Classroom:

    • Attend one complete batch
    • Submit at least one completed project.

    Online Self-Learning:

    • Complete 85% of the course
    • Submit at least one completed project.

  • Do you provide any practice tests as part of this course?

    Yes, we provide 1 practice test as part of our course to help you prepare for the actual certification exam. You can try this Machine Learning Multiple Choice Questions - Free Practice Test to understand the type of tests that are part of the course curriculum.

Machine Learning Course Reviews

Jaya Raghavendra
Jaya Raghavendra Mumbai

I am a B.Sc Computers graduate. I had always attended physical classroom sessions, but this is the first time I experienced online classes. Simplilearn allowed learning from different mentors. Big thanks to the support team.

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Siddhant Vibhute
Siddhant Vibhute M.Tech Scholar at VJTI, Mumbai

Simplilearn provides a platform to explore the subject in depth. The way it connects every problem with the real world makes the subject even more interesting. The trainers and support staff act promptly to each query with every possible help. Machine Learning course is definitely one of my best experiences and is highly recommended for every data scientist aspirant.

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Asmita Wankhade
Asmita Wankhade Warangal

Course content is excellent. You can learn and understand, even if you are only a beginner. I am delighted to have joined and successfully finished the 'Certified Machine Learning’course. All thanks to Simplilearn.

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Sarita Visavalia
Sarita Visavalia Gyanam, Anand

I have enrolled in Data Science using Python and Machine Learning courses on Simplilearn. Course content is very well structured and relates to the current trends in technology and markets, as well. Each session is designed with real-time examples. Instructor-led training has given us excellent exposure. The trainer provides an understanding of all topics through practical exercises. The support team is very responsive. Thanks to Simplilearn team for the wonderful platform provided.

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Mahesh Gaonkar
Mahesh Gaonkar Software Engineer, Bangalore

Simplilearn is a great start for the beginner as well for the experienced person who wants to get into a data science job. Trainers are well experienced and we get more detailed ideas on the concepts and exercises. I could finish my Machine Learning advance course very easily with good project exercise.

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Somil Gadhwal
Somil Gadhwal Application Engineer, Hyderabad

Simplilearn's course content is designed in a way that every session is closely connected to the next. There is no need to mug up the lessons. The instructors put thought into training and motivating students. I am really happy I joined the course.

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Afrid Mondal
Afrid Mondal Nagpur

The training was fantastic. Thank you for providing a great platform to learn.

Roveena Sebastian
Roveena Sebastian R & D Manager, Bangalore

It was a great learning experience. The hands-on assignments and the various resources provided by Simplilearn is excellent. The live sessions were simply awesome. The trainer was so successful in keeping the remote class active, covered all topics in such a short span and majorly ensured that the entire class was moving forward together in spite of the known time constraints. My special thanks to the trainer for his interest and dedication. Thanks once again to Simplilearn.

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Deboleena Paul
Deboleena Paul Solution Architect, Meerut

I really liked the trainer. He is very patient, well organized, and interactive. I had an awesome learning experience with Simplilearn that was beyond my expectation for an online classroom.

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Happy Snehal
Happy Snehal Data Science Intern at ABB, Bangalore

My experience with Simplilearn has been amazing. This is the fifth course that I have done from here and all the courses provide the best quality knowledge. Machine Learning course content was wide and deep. It covered algorithms, Python programming, Mathematics, and Statistics. It also provided project support. My customer support experience has been fantastic as within seconds or minutes, I have been provided with solutions and all my issues have been resolved to my full satisfaction. The faculties are well educated, well experienced, humble, kind and eager to teach things.

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Aditi Dalal
Aditi Dalal Analyst (Data Analytics) at The Smart Cube, Noida

I have enrolled in Machine Learning from Simplilearn. The content of the course is elaborate and easy to understand. The faculty has clarity in his way of explaining, maintains a very good balance between theory and the practical process. It has been a great learning experience for me.

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Ujjwal Seth
Ujjwal Seth Data Analyst at Hewlett Packard Enterprise, Chennai

I have completed Machine Learning certification recently from Simplilearn. It felt amused how I was able to this skill when I was finding it super-hard when learning through some of the other online platforms. No Doubt that I feel Simplilearn is the Best Online Platform for learning Computer Science Skills! The Online Lab access gives complete tech resources using which you can execute Computer code and don't need to install the software on your laptop. The whole system is both simplistic and 1uality wise absolutely to the point and makes the user experience simple and beautiful. The content of the course was interesting and it used a lot of real-life application which helped me to understand better. The customer support was very supportive and always ready to help us. In fact, they always assured that our problem will be solved and the response was quick. Hence, A curious mind should not miss a chance to enroll in his preferred course at Simplilearn.

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Leela Krishna
Leela Krishna Senior Operations Professional at IBM INDIA PVT. LTD., Bangalore

The course was very informative. The study material provided by the trainer was extremely helpful and very easy to understand.

Rajendra Kumar Rana
Rajendra Kumar Rana Senior Software Engineer RPA at Tech Mahindra, Pune

The course material was very engaging and helpful. The Trainer's in-depth knowledge helped to understand Machine Learning better.

Anuvrat Kulkarni
Anuvrat Kulkarni Development Analyst in Social Media at Accenture, Bangalore

My experience with Simplilearn has been very enriching. The faculty was quite experienced and had a deep knowledge of the subject. I am happy with Simplilearn and would definitely recommend others.

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Machine Learning Training FAQs

  • What is the average salary for a Machine Learning Engineer in Mumbai?

    As per the survey conducted by Payscale, it is found that the average salary of Machine Learning engineers in Mumbai is Rs 1,047,479 per annum. It can increase significantly for professionals taking a Machine Learning training program.

  • What are different Machine Learning jobs & roles available in Mumbai?

    There are many roles belonging to the Machine Learning and Artificial Intelligence domain in Mumbai such as:

    • Senior Software Engineer ML
    • Data Scientist
    • ML Engineer
    • ML Specialist
    • Automation and Tool Development

  • Who are the top Employers Hiring Machine Learning Engineers in Mumbai?

     

    Machine Learning professionals are in high demand in Mumbai nowadays with companies like Morgan Stanley, Accenture, Ubisoft, Sutherland, JP Morgan Chase among the many creating opportunities for them.  

  • What is Machine Learning?

     

    Machine Learning is the next step of Artificial Intelligence in which systems are designed to learn from past experiences and provide improved results without programming it specifically. Machine Learning has the ability to analyze huge amounts of data and generate results with high speed and better accuracy giving profitable advantages. Basically, machine learning involves observation of data patterns, gathering crucial information, and making decisions that are efficient for the growth of any company.

  • If I need to cancel my enrollment, can I get a refund?

     

    After a deduction of an amount equivalent to the administration fees, the remaining amount is refunded to the candidate on canceling the enrollment. For further queries, read our Refund Policy.

  • Are there any group discounts for classroom training programs?

     

    Yes, Simplilearn provides group discounts for its training programs. To get more details, visit the Simplilearn website and contact our support team via the Contact Us form or Live Chat option.

  • What are different payment options available to enroll for this course now?

     

    Candidates need to pay online to get themselves enrolled in our Machine Learning Course in Mumbai. The payment options available are:

    • American Express
    • PayPal
    • Visa Credit or Debit Card
    • Diner’s Club
    • MasterCard

    An automatically generated receipt will be sent to the candidate via email once the payment is successful.

  • Who can I contact to learn more about this Machine Learning course?

     

    We provide a Contact Us form on the right of every page of our website. There is also an option of Live Chat where candidates can contact our support team or use the Help & Support portal.

  • What is Global Teaching Assistance?

     

    The faculty at Simplilearn enrich the learning experience of the students through interactive sessions and making sure that students have a firm grasp of the subject being taught. Teaching assistance is provided during business hours by our faculty who support the candidates right from class onboarding to project completion and job assistance.

  • What is covered under the 24/7 Support promise?

     

    Our support team is available for the candidates via calls, chat, or email throughout the day. There is a community forum too that can be accessed 24/7 by the candidates. Moreover, it comes with permanent access.

  • What if I miss a class?

     

    Simplilearn makes it easier for its candidates to complete the Machine Learning course by providing the Flexi-pass. The Flexi-pass gives candidates an option to access 15 training sessions for 90 days that are conducted by highly experienced faculty. The candidates will get the benefit from both online classroom training and self-paced learning.

  • How will I execute projects in this Machine Learning training course?

     

    The Machine Learning Course offered by Simplilearn involves completion of industry-oriented projects. To execute the projects, Simplilearn provides a cloud-based Python environment called as CloudLab. Candidates do not require a virtual machine to install and maintain Python and its libraries. They can use the browser to access a pre-configured environment on CloudLab.

    The CloudLab platform is accessible throughout the course completion via the Simplilearn Learning Management System (LMS)

     

     

  • I am not able to access the online course. Who can help me?

     

    Candidates can either fill the Contact Us form or select the Live Chat option to get in touch with the support team. Both of the options are available on Simplilearn website. You can also click on Help & Support option.

  • Do you provide a money back guarantee for the training programs?

     

    Yes, Simplilearn has a money-back guarantee for most of its training programs. Read our Refund Policy to know more. You can submit refund requests through our Help & Support portal.

  • What is online classroom training?

     

    Simplilearn conducts the Machine Learning Certification classes via live online streaming. These are the interactive sessions led by trainers with over 15 years of relevant experience, and the candidates can communicate with them to get their queries resolved.

  • Is this live training, or will I watch pre-recorded videos?

     

    Simplilearn provides two learning methodologies. For self-paced e-learning, pre-recorded videos are provided to the candidates. With Flexi Pass learning, the candidates get the advantage of instructor-led live online training sessions along with pre-recorded videos.

  • Are the training and course material effective in preparing me for the Machine Learning certification?

     

    Yes, for successful completion of the Machine Learning Certification, we provide state-of-the-art training and course material.

  • Who are the instructors and how are they selected?

     

    We select trainers only after a strict recruitment procedure along with a high alumni rating. The process involves technical evaluation, profile screening as well as training demo. We ensure that the domain experts having several years of relevant teaching experience are only allowed to become the mentors.

  • Who can I contact to learn more about this Machine Learning course?

    Please contact us using the form on the right of any page on the Simplilearn website, or select the Live Chat link. Our customer service representatives will be able to give you more details.

  • * Disclaimer

    * The projects have been built leveraging real publicly available data-sets of the mentioned organizations.

Maching Learning Course Advisor

Mike Tamir
Mike Tamir No. 1 AI and Machine Learning Influencer, Head of Data Science - Uber ATG

​Named by Onalytica as the No.1 influencer in AI and Machine Learning space, Mike serves as Head of Data Science for Uber ATG self-driving engineering team and as UC Berkeley data science faculty.

    Our Mumbai Correspondence / Mailing address

    Simplilearn Solutions Pvt Ltd, 74/II, “C” Cross Road, Opp Gate No 2, Seepz, Andheri East, Mumbai- 400093, Maharashtra, India, Call us at 1800-212-7688

    • Disclaimer
    • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.