Complete Machine Learning Course in English > Getting Started

Getting Started with Machine Learning

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Overview

Hello,

I am Kushal from LearnVern. ( 6 seconds pause ; music )

Today with full excitement,

I am introducing you to our end to end programme on Machine Learning.

If you are a graduate, or you are a working professional in both cases this particular course would be really very useful for you all.

In this course, we will learn from its scratch,

starting with its basics to its end to end.

Meaning we will be learning about:-

Machine Learning's Optimization Techniques,

Machine Learning's Projects Development,

And, even uptil automatic machine learning packages

We have discussed everything.

I, have around 15 years of industry experience,

And I have Relevant Experiences of

Machine Learning,

Big Data,

Data Science,

And Artificial Intelligence.

Currently, I am heading a team of software developers in Pune

So, this entire course would be presented to you by me on behalf of LearnVern's team.

So,

Let's see What are the benefits that can be seeked through this course?

You might want to become a Machine Learning Engineer.

Or, You want to be a part of a team of Data Scientists.

Or, even if you are just searching for an Internship.

Or, maybe you just want to switch your career.

In all cases you can enrol with this course,

And Execute it really well from its end to end.

Let us see,

What are we going to Learn from this?

In this course, we will learn about Different kinds of Libraries

So, in this we will start with Python's

Numpy,

Pandas,

Matplotlib,

And Starting with these libraries.

Where we will see:-

How is pre pre-processing of data done?

Or,

How an exploratory analysis is done on data?

How different types of visualisation can be done on data?

So this will be our fundamental part.

Next,

After we know to handle the data.

Then we will see about

What are Machine Learning's different types?

It's paradigm?

What are they?

Like, Supervised Learning,

Unsupervised Learning,

Reinforcement Learning,

And,

now to implement this, we have different packages of Python.

For example there is sklearn (pronounce : S k learn ) or we have scipy, (pronounce : Sai pai) there is mlxtend. (pronounce : M L extend )

So, we will see about these different packages,

And then we will execute this.

Now, after this,

Our exercises on this,

will be completely practical based, scenario based and project based

With this, you will get an exposure, a flavour

To understand,

how if somebody is working in this industry will use these techniques or these tools to develop a product or a project.

Along with this, if at all you have the fear,

That my mathematics is weak, or my statistics is really poor.

Then also, you don't have to worry about that as well.

Because in each and every programme whenever we are executing it, we make sure that we cover the basic statistics.

That means even if you do not have a sound Knowledge of mathematics you can understand the concept and implement it really well.

Let us move ahead,

So, as I had said,

As you will move ahead with this course you will get to see that

you would be able to develop projects and along with the development of projects in the end you will also get to learn about,

How Mock Interviews are?

about interview questions and answers and also techniques.

So, with all this in full excitement,

Let's start and begin with this Machine Learning Course.

So, friends

Today we will stop our session here itself.

And now we will continue in the next session.

If you have any queries or comments, click the discussion button below the video and post there. This way, you will be able to connect to fellow learners and discuss the course. Also, Our Team will try to solve your query.

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Learner's Ratings

4.2

Overall Rating

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Reviews

A
Aryan Ambat
5

Yes

Z
zeyana Fathima
5

thanks for giving this wonderful course in a understandable way please provide the details from where can i get the datasets

L
Losika Nicholas
5

were can i get the dataset

K
Kumar Madduru
5

Thanks for giving this course

D
Dinesh Kumar
4

Your screen is very blur and it doesn't has clarity even in 720P.Please make sure that will not happen again.

D
DOGALA UDAYKUMAR
5

bettor

N
Naresh Kulunge
4

good learning but the content titles are jumbled up, like first title of this module is decision tree dichotomiser which is practical part ahead of theory part. Same with the SVM practical 1 title has

E
Eswar Veeranki
5

good

I
Isakki Alias Devi P
5

Wonderful course

S
sushma Yadla
5

yes, i am happy to learning for machine learning in LearnVern.it i s easily understanding for Beginners.

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Course Content

  1. Getting Started with Machine Learning
    4m 4s
  2. How to use LearnVern
    3m 8s

  1. Introduction to Machine Learning
    16m 19s
  2. Environment Setup Part 1
    3m 21s
  3. Environment Setup Part 2
    4m 52s
  4. Environment Setup Part 3
    2m 35s

  1. Data Wrangling
    12m 36s
  2. Importing Libraries and Dataset
    11m 35s
  3. Handling Missing Data
    12m 13s
  4. Handling Missing Data - Practical
    19m 46s
  5. Encoding Categorical Data
    7m 3s
  6. Encoding Catergorical Data - Practical
    8m 6s
  7. Splitting Dataset
    3m 21s
  8. Splitting Dataset - Practical
    9m 43s
  9. Normalizing the Data - Part 1
    7m 29s
  10. Normalizing the Data - Part 2
    7m 30s
  11. Finding Machine Learning Datasets
    8m 56s
  12. Exploratory Data Analysis
    9m 49s
  13. Plotting Graphs - Part 1
    17m 51s
  14. Plotting Graphs - Part 2
    21m 44s
  15. Distribution Models - Part 1
    29m 48s
  16. Distribution Models - Part 2
    10m 23s
  17. Assignment : Data Preprocessing for Machine Learning
    1m 54s

  1. Machine Learning Paradigms
    10m 37s
  2. Assignment : Machine Learning Paradigms
    3m 22s

  1. Decision Tree Iterative Dichotomiser 3
    20m 2s
  2. Random Forest
    5m 25s
  3. Support Vector Machine Classifier
    6m 22s
  4. Support Vector Machine Classifier - Practical 1
    16m 16s
  5. Support Vector Machine Classifier - Practical 2
    10m 25s
  6. Naive Bayes Classifier
    6m 36s
  7. Naive Bayes Classifier - Practical 1
    14m 34s
  8. Naive Bayes Classifier - Practical 2
    9m 42s
  9. Evaluating Classification Models Performance
    9m 8s
  10. Evaluating Classification Models Performance - Practical
    29m 33s
  11. Overview of Classification
    6m 16s
  12. Logistic Regression
    5m 35s
  13. Logistic Regression - Practical - 1
    30m 22s
  14. Logistic Regression - Practical - 2
    13m 28s
  15. KNN
    7m 34s
  16. KNN Practical - 1
    29m 39s
  17. KNN - Practical 2
    9m 53s
  18. Decision Trees for Classification
    6m 1s
  19. Decision Trees for Classification - Practical 1
    23m 27s
  20. Decision Trees for Classification - Practical 2
    18m 39s
  21. Assignment : Supervised Learning Algorithms
    1m 34s

  1. Simple Linear Regression
    10m 4s
  2. Simple Linear Regression - Practical
    11m 46s
  3. Salary Prediction using Linear Regression
    19m 25s
  4. Multi-Linear Regression
    2m 56s
  5. Startup Prediction using Multiple Regression
    20m 34s
  6. Support Vector Regressor
    11m 15s
  7. Support Vector Regressor - Practical 1
    7m 28s
  8. Support Vector Regressor - Practical 2
    14m 44s
  9. Decision Tree Regressor
    5m 31s
  10. Decision Tree Regressor - Practical 1
    5m 33s
  11. Decision Tree Regressor - Practical 2
    16m 31s
  12. Regressor Model Selection
    11m 8s
  13. Evaluating Regression Model Performance
    10m 45s
  14. Evaluating Regression Model Performance - Practical
    12m 11s
  15. Assignment : Regression Algorithms
    1m 5s

  1. Distance Metrics
    6m 1s
  2. K-Means Clustering
    11m 40s
  3. K-Means Clustering - Practical
    9m 32s
  4. Mall Customers Prediction using K Means Clustering
    16m 42s
  5. Hierarchical Clustering - Agglomerative , Divisive
    5m 25s
  6. Agglomerative Clustering - Practical
    4m 37s
  7. Divisive Clustering - Practical
    13m 43s
  8. DBscan Spatial Clustering
    10m 53s
  9. Mall Customers Prediction using Hierarchical Clustering
    17m 48s
  10. Assignment : Unsupervised Learning Algorithms
    3m 11s

  1. Association Rule Learning - Apriori, FP Growth
    8m 17s
  2. Association Rule Learning - Apriori Practical
    7m 41s
  3. Market Basket Analysis using Apriori
    19m 30s
  4. FP Growth
    8m
  5. Market Basket Analysis using FP Growth
    12m 20s
  6. Assignment : Association Rule Mining
    56s

  1. Reinforcement Learning Theory - Multi Armed Bandits
    27m 30s
  2. Upper Confidence Bound - Practical
    10m 53s
  3. Thompson Sampling - Practical
    5m 34s
  4. Q Learning
    32m 24s
  5. Assignment : Reinforcement Learning
    1m 43s

  1. Overview of Dimensoionality Reduction
    4m 40s
  2. Princinpal Component Analysis
    39m 4s
  3. Principal Component Analysis - Practical
    6m 37s
  4. Linear Discriminant Analysis
    19m 9s
  5. Linear Discriminant Analysis - Practical
    6m 29s
  6. Assignment : Dimensionality Reduction
    52s

  1. Basics of Regularization and Optimization
    7m 58s
  2. Cross Validation
    20m 3s
  3. Hyperparameter Tuning
    20m 7s
  4. Sampling Methods
    19m 8s
  5. Underfitting and Overfitting in Models
    5m 33s
  6. Variance and Bias
    9m 33s
  7. Assignment : Regularization and Optimization
    1m 1s

  1. Advance Trends in Machine Learning
    12m 48s

  1. Introduction to Keras and Deep Learning
    9m 2s
  2. Practical Demonstration -Keras
    18m 35s

  1. Reinforcement Learning Project - Teach a Taxi Part 1
    18m 10s
  2. Reinforcement Learning Project - Teach a Taxi Part 2
    15m 11s
  3. Reinforcement Learning Project - Teach a Taxi Part 3
    14m 35s
  4. Reinforcement Learning Project - Teach a Taxi Part 4
    17m 23s
  5. Loan Prediction Project Part 1
    32m 16s
  6. Loan Prediction Project Part 2
    28m 42s

  1. Course Summary
    3m 53s

  1. Interview Questions Part 1
    18m 22s
  2. Interview Questions Part 2
    13m 23s
  3. Interview Questions Part 3
    16m 2s

  1. Career Guidelines
    6m 14s
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