Setup Menus in Admin Panel

Course Details

Interested in the field of Machine Learning? Then this course is for you!

This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. 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.
This course is structured in a fun and exciting way, but at the same time we dive deep into Machine Learning. In this course you will learn the following algorithms:
  • Linear Regression
  • Multiple Linear Regression
  • K-Means Clustering
  • Hierarchical Clustering
  • K-Nearest Neighbour
  • Decision Trees
  • Random Forest
Moreover, the course is packed with practical exercises which are based on live examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. And as a bonus, this course includes both R and Python code templates which you can download and use on your own projects.
What are the requirements?
  • Just some high school mathematics level.
What am I going to get from this course?
  • Master Machine Learning on Python & R
  • Have a great intuition of many Machine Learning models
  • Make accurate predictions
  • Make powerful analysis
  • Make robust Machine Learning models
  • Create strong added value to your business
  • Use Machine Learning for personal purpose
  • Handle specific topics like Reinforcement Learning, NLP and Deep Learning
  • Handle advanced techniques like Dimensionality Reduction
  • Know which Machine Learning model to choose for each type of problem
  • Build an army of powerful Machine Learning models and know how to combine them to solve any problem
What is the target audience?
  • Anyone interested in Machine Learning.
  • Students who have at least high school knowledge in math and who want to start learning Machine Learning.
  • Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
  • Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
  • Any students in college who want to start a career in Data Science.
  • Any data analysts who want to level up in Machine Learning.
  • Any people who are not satisfied with their job and who want to become a Data Scientist.
  • Any people who want to create added value to their business by using powerful Machine Learning tools.

Course Curriculum

1.1 Welcome to the course!
1.1 Welcome to the course! 00:00:00
1.2 Applications of Machine Learning
1.2 Applications of Machine Learning 00:00:00
1.3 Why Machine Learning is the
1.3 Why Machine Learning is the 00:00:00
1.4 Installing R and R Studio (MAC & Windows)
1.4 Installing R and R Studio (MAC & Windows) 00:00:00
1.5 Update Recommended Anaconda Version
1.5 Update Recommended Anaconda Version 00:00:00
1.6 Installing Python and Anaconda (MAC & Windows)
1.6 Installing Python and Anaconda (MAC & Windows) 00:00:00
1.7 Bonus Meet your instructors
1.7 Bonus Meet your instructors 00:00:00
2.1 Welcome to Part 1 - Data Preprocessing
2.1 Welcome to Part 1 – Data Preprocessing 00:00:00
2.2 Get the dataset
2.2 Get the dataset 00:00:00
2.3 Importing the Libraries
2.3 Importing the Libraries 00:00:00
2.4 Importing the Dataset
2.4 Importing the Dataset 00:00:00
2.5 For Python learners, summary of Object-oriented programming classes & objects
2.5 For Python learners, summary of Object-oriented programming classes & objects 00:00:00
2.6 Missing Data
2.6 Missing Data 00:00:00
2.7 Categorical Data
2.7 Categorical Data 00:00:00
2.8 Splitting the Dataset into the Training set and Test set
2.8 Splitting the Dataset into the Training set and Test set 00:00:00
2.9 Feature Scaling
2.9 Feature Scaling 00:00:00
2.10 And here is our Data Preprocessing Template !
2.10 And here is our Data Preprocessing Template ! 00:00:00
3.1 Welcome to Part 2 - Regression
3.1 Welcome to Part 2 – Regression 00:00:00
4.1 How to get the dataset
4.1 How to get the dataset 00:00:00
4.2 Dataset + Business Problem Description
4.2 Dataset + Business Problem Description 00:00:00
4.3 Simple Linear Regression Intuition - Step 1
4.3 Simple Linear Regression Intuition – Step 1 00:00:00
4.4 Simple Linear Regression Intuition - Step 2
4.4 Simple Linear Regression Intuition – Step 2 00:00:00
4.5 Simple Linear Regression in Python - Step 1
4.5 Simple Linear Regression in Python – Step 1 00:00:00
4.6 Simple Linear Regression in Python - Step 2
4.6 Simple Linear Regression in Python – Step 2 00:00:00
4.7 Simple Linear Regression in Python - Step 3
4.7 Simple Linear Regression in Python – Step 3 00:00:00
4.8 Simple Linear Regression in Python - Step 4
4.8 Simple Linear Regression in Python – Step 4 00:00:00
4.9 Simple Linear Regression in R - Step 1
4.9 Simple Linear Regression in R – Step 1 00:00:00
4.10 Simple Linear Regression in R - Step 2
4.10 Simple Linear Regression in R – Step 2 00:00:00
4.11 Simple Linear Regression in R - Step 3
4.11 Simple Linear Regression in R – Step 3 00:00:00
4.12 Simple Linear Regression in R - Step 4
4.12 Simple Linear Regression in R – Step 4 00:00:00
5.1 How to get the dataset
5.1 How to get the dataset 00:00:00
5.2 Dataset + Business Problem Description
5.2 Dataset + Business Problem Description 00:00:00
5.3 Multiple Linear Regression Intuition - Step 1
5.3 Multiple Linear Regression Intuition – Step 1 00:00:00
5.4 Multiple Linear Regression Intuition - Step 2
5.4 Multiple Linear Regression Intuition – Step 2 00:00:00
5.5 Multiple Linear Regression Intuition - Step 3
5.5 Multiple Linear Regression Intuition – Step 3 00:00:00
5.6 Multiple Linear Regression Intuition - Step 4
5.6 Multiple Linear Regression Intuition – Step 4 00:00:00
5.7 Multiple Linear Regression Intuition - Step 5
5.7 Multiple Linear Regression Intuition – Step 5 00:00:00
5.8 Multiple Linear Regression in Python - Step 1
5.8 Multiple Linear Regression in Python – Step 1 00:00:00
5.9 Multiple Linear Regression in Python - Step 2
5.9 Multiple Linear Regression in Python – Step 2 00:00:00
5.10 Multiple Linear Regression in Python - Step 3
5.10 Multiple Linear Regression in Python – Step 3 00:00:00
5.11 Multiple Linear Regression in Python - Backward Elimination - Preparation
5.11 Multiple Linear Regression in Python – Backward Elimination – Preparation 00:00:00
5.12 Multiple Linear Regression in Python - Backward Elimination - Homework!
5.12 Multiple Linear Regression in Python – Backward Elimination – Homework! 00:00:00
5.13 Multiple Linear Regression in Python - Backward Elimination - Homework Solution
5.13 Multiple Linear Regression in Python – Backward Elimination – Homework Solution 00:00:00
5.14 Multiple Linear Regression in R - Step 1
5.14 Multiple Linear Regression in R – Step 1 00:00:00
5.15 Multiple Linear Regression in R - Step 2
5.15 Multiple Linear Regression in R – Step 2 00:00:00
5.16 Multiple Linear Regression in R - Step 3
5.16 Multiple Linear Regression in R – Step 3 00:00:00
5.17 Multiple Linear Regression in R - Backward Elimination - Homework!
5.17 Multiple Linear Regression in R – Backward Elimination – Homework! 00:00:00
5.18 Multiple Linear Regression in R - Backward Elimination - Homework Solution
5.18 Multiple Linear Regression in R – Backward Elimination – Homework Solution 00:00:00
6.1 Polynomial Regression Intuition
6.1 Polynomial Regression Intuition 00:00:00
6.2 How to get the dataset
6.2 How to get the dataset 00:00:00
6.3 Polynomial Regression in Python - Step 1
6.3 Polynomial Regression in Python – Step 1 00:00:00
6.4 Polynomial Regression in Python - Step 2
6.4 Polynomial Regression in Python – Step 2 00:00:00
6.5 Polynomial Regression in Python - Step 3
6.5 Polynomial Regression in Python – Step 3 00:00:00
6.6 Polynomial Regression in Python - Step 4
6.6 Polynomial Regression in Python – Step 4 00:00:00
6.7 Python Regression Template
6.7 Python Regression Template 00:00:00
6.8 Polynomial Regression in R - Step 1
6.8 Polynomial Regression in R – Step 1 00:00:00
6.9 Polynomial Regression in R - Step 2
6.9 Polynomial Regression in R – Step 2 00:00:00
6.10 Polynomial Regression in R - Step 3
6.10 Polynomial Regression in R – Step 3 00:00:00
6.11 Polynomial Regression in R - Step 4
6.11 Polynomial Regression in R – Step 4 00:00:00
6.12 R Regression Template
6.12 R Regression Template 00:00:00
7.1 How to get the dataset
7.1 How to get the dataset 00:00:00
7.2 SVR in Python
7.2 SVR in Python 00:00:00
7.3 SVR in R
7.3 SVR in R 00:00:00
8.1 Decision Tree Regression Intuition
8.1 Decision Tree Regression Intuition 00:00:00
8.2 How to get the dataset
8.2 How to get the dataset 00:00:00
8.3 Decision Tree Regression in Python
8.3 Decision Tree Regression in Python 00:00:00
8.4 Decision Tree Regression in R
8.4 Decision Tree Regression in R 00:00:00
9.1 Random Forest Regression Intuition
9.1 Random Forest Regression Intuition 00:00:00
9.2 How to get the dataset
9.2 How to get the dataset 00:00:00
9.3 Random Forest Regression in Python
9.3 Random Forest Regression in Python 00:00:00
9.4 Random Forest Regression in R
9.4 Random Forest Regression in R 00:00:00
10.1 R-Squared Intuition
10.1 R-Squared Intuition 00:00:00
10.2 Adjusted R-Squared Intuition
10.2 Adjusted R-Squared Intuition 00:00:00
10.3 Evaluating Regression Models Performance - Homework's Final Part
10.3 Evaluating Regression Models Performance – Homework’s Final Part 00:00:00
10.4 Interpreting Linear Regression Coefficients
10.4 Interpreting Linear Regression Coefficients 00:00:00
10.5 Conclusion of Part 2 - Regression
10.5 Conclusion of Part 2 – Regression 00:00:00
11.1 Welcome to Part 3 - Classification
11.1 Welcome to Part 3 – Classification 00:00:00
12.1 Logistic Regression Intuition
12.1 Logistic Regression Intuition 00:00:00
12.2 How to get the dataset
12.2 How to get the dataset 00:00:00
12.3 Logistic Regression in Python - Step 1
12.3 Logistic Regression in Python – Step 1 00:00:00
12.4 Logistic Regression in Python - Step 2
12.4 Logistic Regression in Python – Step 2 00:00:00
12.5 Logistic Regression in Python - Step 3
12.5 Logistic Regression in Python – Step 3 00:00:00
12.6 Logistic Regression in Python - Step 4
12.6 Logistic Regression in Python – Step 4 00:00:00
12.7 Logistic Regression in Python - Step 5
12.7 Logistic Regression in Python – Step 5 00:00:00
12.8 Python Classification Template
12.8 Python Classification Template 00:00:00
12.9 Logistic Regression in R - Step 1
12.9 Logistic Regression in R – Step 1 00:00:00
12.10 Logistic Regression in R - Step 2
12.10 Logistic Regression in R – Step 2 00:00:00
12.11 Logistic Regression in R - Step 3
12.11 Logistic Regression in R – Step 3 00:00:00
12.12 Logistic Regression in R - Step 4
12.12 Logistic Regression in R – Step 4 00:00:00
12.13 Logistic Regression in R - Step 5
12.13 Logistic Regression in R – Step 5 00:00:00
12.14 R Classification Template
12.14 R Classification Template 00:00:00
13.1 K-Nearest Neighbor Intuition
13.1 K-Nearest Neighbor Intuition 00:00:00
13.2 How to get the dataset
13.2 How to get the dataset 00:00:00
13.3 K-NN in Python
13.3 K-NN in Python 00:00:00
13.4 K-NN in R
13.4 K-NN in R 00:00:00
14.1 SVM Intuition
14.1 SVM Intuition 00:00:00
14.2 How to get the dataset
14.2 How to get the dataset 00:00:00
14.3 SVM in Python
14.3 SVM in Python 00:00:00
14.4 SVM in R
14.4 SVM in R 00:00:00
15.1 Kernel SVM Intuition
15.1 Kernel SVM Intuition 00:00:00
15.2 Mapping to a higher dimension
15.2 Mapping to a higher dimension 00:00:00
15.3 Kernel SVM in R
15.3 Kernel SVM in R 00:00:00
15.4 Types of Kernel Functions
15.4 Types of Kernel Functions 00:00:00
15.5 How to get the dataset
15.5 How to get the dataset 00:00:00
15.6 Kernel SVM in Python
15.6 Kernel SVM in Python 00:00:00
15.7 Kernel SVM in R
15.7 Kernel SVM in R 00:00:00
16.1 Bayes Theorem
16.1 Bayes Theorem 00:00:00
16.2 Naive Bayes Intuition
16.2 Naive Bayes Intuition 00:00:00
16.3 Naive Bayes Intuition (Challenge Reveal)
16.3 Naive Bayes Intuition (Challenge Reveal) 00:00:00
16.4 Naive Bayes Intuition (Extras)
16.4 Naive Bayes Intuition (Extras) 00:00:00
16.5 How to get the dataset
16.5 How to get the dataset 00:00:00
16.6 Naive Bayes in Python
16.6 Naive Bayes in Python 00:00:00
16.7 Naive Bayes in R
16.7 Naive Bayes in R 00:00:00
17.1 Decision Tree Classification Intuition
17.1 Decision Tree Classification Intuition 00:00:00
17.2 How to get the dataset
17.2 How to get the dataset 00:00:00
17.3 Decision Tree Classification in Python
17.3 Decision Tree Classification in Python 00:00:00
17.4 Decision Tree Classification in R
17.4 Decision Tree Classification in R 00:00:00
18.1 Random Forest Classification Intuition
18.1 Random Forest Classification Intuition 00:00:00
18.2 How to get the dataset
18.2 How to get the dataset 00:00:00
18.3 Random Forest Classification in Python
18.3 Random Forest Classification in Python 00:00:00
18.4 Random Forest Classification in R
18.4 Random Forest Classification in R 00:00:00
19.1 False Positives & False Negatives
19.1 False Positives & False Negatives 00:00:00
19.2 Confusion Matrix
19.2 Confusion Matrix 00:00:00
19.3 Accuracy Paradox
19.3 Accuracy Paradox 00:00:00
19.4 CAP Curve
19.4 CAP Curve 00:00:00
19.5 CAP Curve Analysis
19.5 CAP Curve Analysis 00:00:00
19.6 Conclusion of Part 3 - Classification
19.6 Conclusion of Part 3 – Classification 00:00:00
20.1 Welcome to Part 4 - Clustering
20.1 Welcome to Part 4 – Clustering 00:00:00
21.1 K-Means Clustering Intuition
21.1 K-Means Clustering Intuition 00:00:00
21.2 K-Means Random Initialization Trap
21.2 K-Means Random Initialization Trap 00:00:00
21.3 K-Means Selecting The Number Of Clusters
21.3 K-Means Selecting The Number Of Clusters 00:00:00
21.4 How to get the dataset
21.4 How to get the dataset 00:00:00
21.5 K-Means Clustering in Python
21.5 K-Means Clustering in Python 00:00:00
21.6 K-Means Clustering in R
21.6 K-Means Clustering in R 00:00:00
22.1 Hierarchical Clustering Intuition
22.1 Hierarchical Clustering Intuition 00:00:00
22.2 Hierarchical Clustering How Dendrograms Work
22.2 Hierarchical Clustering How Dendrograms Work 00:00:00
22.3 Hierarchical Clustering Using Dendrograms
22.3 Hierarchical Clustering Using Dendrograms 00:00:00
22.4 How to get the dataset
22.4 How to get the dataset 00:00:00
22.5 HC in Python - Step 1
22.5 HC in Python – Step 1 00:00:00
22.6 HC in Python - Step 2
22.6 HC in Python – Step 2 00:00:00
22.7 HC in Python - Step 3
22.7 HC in Python – Step 3 00:00:00
22.8 HC in Python - Step 4
22.8 HC in Python – Step 4 00:00:00
22.9 HC in Python - Step 5
22.9 HC in Python – Step 5 00:00:00
22.10 HC in R - Step 1
22.10 HC in R – Step 1 00:00:00
22.11 HC in R - Step 2
22.11 HC in R – Step 2 00:00:00
22.12 HC in R - Step 3
22.12 HC in R – Step 3 00:00:00
22.14 HC in R - Step 5
22.14 HC in R – Step 5 00:00:00
22.16 Conclusion of Part 4 - Clustering
22.16 Conclusion of Part 4 – Clustering 00:00:00
23.1 Welcome to Part 5 - Association Rule Learning
23.1 Welcome to Part 5 – Association Rule Learning 00:00:00
24.1 Apriori Intuition
24.1 Apriori Intuition 00:00:00
24.2 How to get the dataset
24.2 How to get the dataset 00:00:00
24.3 Apriori in R - Step 1
24.3 Apriori in R – Step 1 00:00:00
24.4 Apriori in R - Step 2
24.4 Apriori in R – Step 2 00:00:00
24.5 Apriori in R - Step 3
24.5 Apriori in R – Step 3 00:00:00
24.6 Apriori in Python - Step 1
24.6 Apriori in Python – Step 1 00:00:00
24.7 Apriori in Python - Step 2
24.7 Apriori in Python – Step 2 00:00:00
24.8 Apriori in Python - Step 3
24.8 Apriori in Python – Step 3 00:00:00
25.1 Eclat Intuition
25.1 Eclat Intuition 00:00:00
25.2 How to get the dataset
25.2 How to get the dataset 00:00:00
25.3 Eclat in R
25.3 Eclat in R 00:00:00
26.1 Welcome to Part 6 - Reinforcement Learning
26.1 Welcome to Part 6 – Reinforcement Learning 00:00:00
27.1 The Multi-Armed Bandit Problem
27.1 The Multi-Armed Bandit Problem 00:00:00
27.2 Upper Confidence Bound (UCB) Intuition
27.2 Upper Confidence Bound (UCB) Intuition 00:00:00
27.3 How to get the dataset
27.3 How to get the dataset 00:00:00
27.4 Upper Confidence Bound in Python - Step 1
27.4 Upper Confidence Bound in Python – Step 1 00:00:00
27.5 Upper Confidence Bound in Python - Step 2
27.5 Upper Confidence Bound in Python – Step 2 00:00:00
27.6 Upper Confidence Bound in Python - Step 3
27.6 Upper Confidence Bound in Python – Step 3 00:00:00
27.7 Upper Confidence Bound in Python - Step 4
27.7 Upper Confidence Bound in Python – Step 4 00:00:00
27.8 Upper Confidence Bound in R - Step 1
27.8 Upper Confidence Bound in R – Step 1 00:00:00
27.9 Upper Confidence Bound in R - Step 2
27.9 Upper Confidence Bound in R – Step 2 00:00:00
27.10 Upper Confidence Bound in R - Step 3
27.10 Upper Confidence Bound in R – Step 3 00:00:00
27.11 Upper Confidence Bound in R - Step 4
27.11 Upper Confidence Bound in R – Step 4 00:00:00
28.1 Thompson Sampling Intuition
28.1 Thompson Sampling Intuition 00:00:00
28.2 Algorithm Comparison UCB vs Thompson Sampling
28.2 Algorithm Comparison UCB vs Thompson Sampling 00:00:00
28.3 How to get the dataset
28.3 How to get the dataset 00:00:00
28.4 Thompson Sampling in Python - Step 1
28.4 Thompson Sampling in Python – Step 1 00:00:00
28.5 Thompson Sampling in Python - Step 2
28.5 Thompson Sampling in Python – Step 2 00:00:00
28.6 Thompson Sampling in R - Step 1
28.6 Thompson Sampling in R – Step 1 00:00:00
28.7 Thompson Sampling in R - Step 2
28.7 Thompson Sampling in R – Step 2 00:00:00
29.1 Welcome to Part 7 - Natural Language Processing
29.1 Welcome to Part 7 – Natural Language Processing 00:00:00
29.2 How to get the dataset
29.2 How to get the dataset 00:00:00
29.3 Natural Language Processing in Python - Step 1
29.3 Natural Language Processing in Python – Step 1 00:00:00
29.4 Natural Language Processing in Python - Step 2
29.4 Natural Language Processing in Python – Step 2 00:00:00
29.5 Natural Language Processing in Python - Step 3
29.5 Natural Language Processing in Python – Step 3 00:00:00
29.6 Natural Language Processing in Python - Step 4
29.6 Natural Language Processing in Python – Step 4 00:00:00
29.7 Natural Language Processing in Python - Step 5
29.7 Natural Language Processing in Python – Step 5 00:00:00
29.8 Natural Language Processing in Python - Step 6
29.8 Natural Language Processing in Python – Step 6 00:00:00
29.9 Natural Language Processing in Python - Step 7
29.9 Natural Language Processing in Python – Step 7 00:00:00
29.10 Natural Language Processing in Python - Step 8
29.10 Natural Language Processing in Python – Step 8 00:00:00
29.11 Natural Language Processing in Python - Step 9
29.11 Natural Language Processing in Python – Step 9 00:00:00
29.12 Natural Language Processing in Python - Step 10
29.12 Natural Language Processing in Python – Step 10 00:00:00
29.13 Homework Challenge
29.13 Homework Challenge 00:00:00
29.14 Natural Language Processing in R - Step 1
29.14 Natural Language Processing in R – Step 1 00:00:00
29.15 Natural Language Processing in R - Step 2
29.15 Natural Language Processing in R – Step 2 00:00:00
29.16 Natural Language Processing in R - Step 3
29.16 Natural Language Processing in R – Step 3 00:00:00
29.17 Natural Language Processing in R - Step 4
29.17 Natural Language Processing in R – Step 4 00:00:00
29.18 Natural Language Processing in R - Step 5
29.18 Natural Language Processing in R – Step 5 00:00:00
29.19 Natural Language Processing in R - Step 6
29.19 Natural Language Processing in R – Step 6 00:00:00
29.20 Natural Language Processing in R - Step 7
29.20 Natural Language Processing in R – Step 7 00:00:00
29.21 Natural Language Processing in R - Step 8
29.21 Natural Language Processing in R – Step 8 00:00:00
29.22 Natural Language Processing in R - Step 9
29.22 Natural Language Processing in R – Step 9 00:00:00
29.23 Natural Language Processing in R - Step 10
29.23 Natural Language Processing in R – Step 10 00:00:00
29.24 Homework Challenge
29.24 Homework Challenge 00:00:00
30.1 Welcome to Part 8 - Deep Learning
30.1 Welcome to Part 8 – Deep Learning 00:00:00
30.2 What is Deep Learning
30.2 What is Deep Learning 00:00:00
31.1 Plan of attack
31.1 Plan of attack 00:00:00
31.2 The Neuron
31.2 The Neuron 00:00:00
31.3 The Activation Function
31.3 The Activation Function 00:00:00
31.4 How do Neural Networks work
31.4 How do Neural Networks work 00:00:00
31.5 How do Neural Networks learn
31.5 How do Neural Networks learn 00:00:00
31.6 Gradient Descent
31.6 Gradient Descent 00:00:00
31.7 Stochastic Gradient Descent
31.7 Stochastic Gradient Descent 00:00:00
31.8 Back Propagation
31.8 Back Propagation 00:00:00
31.9 How to get the dataset
31.9 How to get the dataset 00:00:00
31.10 Business Problem Description
31.10 Business Problem Description 00:00:00
31.11 ANN in Python - Step 1 - Installing Theano, Tensorflow and Keras
31.11 ANN in Python – Step 1 – Installing Theano, Tensorflow and Keras 00:00:00
31.12 ANN in Python - Step 2
31.12 ANN in Python – Step 2 00:00:00
31.13 ANN in Python - Step 3
31.13 ANN in Python – Step 3 00:00:00
31.14 ANN in Python - Step 4
31.14 ANN in Python – Step 4 00:00:00
31.15 ANN in Python - Step 5
31.15 ANN in Python – Step 5 00:00:00
31.16 ANN in Python - Step 6
31.16 ANN in Python – Step 6 00:00:00
31.17 ANN in Python - Step 7
31.17 ANN in Python – Step 7 00:00:00
31.18 ANN in Python - Step 8
31.18 ANN in Python – Step 8 00:00:00
31.19 ANN in Python - Step 9
31.19 ANN in Python – Step 9 00:00:00
31.20 ANN in Python - Step 10
31.20 ANN in Python – Step 10 00:00:00
31.21 ANN in R - Step 1
31.21 ANN in R – Step 1 00:00:00
31.22 ANN in R - Step 2
31.22 ANN in R – Step 2 00:00:00
31.23 ANN in R - Step 3
31.23 ANN in R – Step 3 00:00:00
31.24 ANN in R - Step 4 (Last step)
31.24 ANN in R – Step 4 (Last step) 00:00:00
32.1 Plan of attack
32.1 Plan of attack 00:00:00
32.2 What are Convolutional Neural Networks
32.2 What are Convolutional Neural Networks 00:00:00
32.3 Step 1 - Convolution Operation
32.3 Step 1 – Convolution Operation 00:00:00
32.4 Step 1(b) - ReLU Layer
32.4 Step 1(b) – ReLU Layer 00:00:00
32.5 Step 2 - Pooling
32.5 Step 2 – Pooling 00:00:00
32.6 Step 3 - Flattening
32.6 Step 3 – Flattening 00:00:00
32.7 Step 4 - Full Connection
32.7 Step 4 – Full Connection 00:00:00
32.8 Summary
32.8 Summary 00:00:00
32.9 Softmax & Cross-Entropy
32.9 Softmax & Cross-Entropy 00:00:00
32.10 How to get the dataset
32.10 How to get the dataset 00:00:00
32.11 CNN in Python - Step 1
32.11 CNN in Python – Step 1 00:00:00
32.12 CNN in Python - Step 2
32.12 CNN in Python – Step 2 00:00:00
32.13 CNN in Python - Step 3
32.13 CNN in Python – Step 3 00:00:00
32.14 CNN in Python - Step 4
32.14 CNN in Python – Step 4 00:00:00
32.15 CNN in Python - Step 5
32.15 CNN in Python – Step 5 00:00:00
32.16 CNN in Python - Step 6
32.16 CNN in Python – Step 6 00:00:00
32.17 CNN in Python - Step 7
32.17 CNN in Python – Step 7 00:00:00
32.18 CNN in Python - Step 8
32.18 CNN in Python – Step 8 00:00:00
32.19 CNN in Python - Step 9
32.19 CNN in Python – Step 9 00:00:00
32.20 CNN in Python - Step 10
32.20 CNN in Python – Step 10 00:00:00
32.21 CNN in R
32.21 CNN in R 00:00:00
33.1 Welcome to Part 9 - Dimensionality Reduction
33.1 Welcome to Part 9 – Dimensionality Reduction 00:00:00
34.1 How to get the dataset
34.1 How to get the dataset 00:00:00
34.2 PCA in Python - Step 1
34.2 PCA in Python – Step 1 00:00:00
34.3 PCA in Python - Step 2
34.3 PCA in Python – Step 2 00:00:00
34.4 PCA in Python - Step 3
34.4 PCA in Python – Step 3 00:00:00
34.5 PCA in R - Step 1
34.5 PCA in R – Step 1 00:00:00
34.6 PCA in R - Step 2
34.6 PCA in R – Step 2 00:00:00
34.7 PCA in R - Step 3
34.7 PCA in R – Step 3 00:00:00
35.1 How to get the dataset
35.1 How to get the dataset 00:00:00
35.2 LDA in Python
35.2 LDA in Python 00:00:00
35.3 LDA in R
35.3 LDA in R 00:00:00
36.1 How to get the dataset
36.1 How to get the dataset 00:00:00
36.2 Kernel PCA in Python
36.2 Kernel PCA in Python 00:00:00
36.3 Kernel PCA in R
36.3 Kernel PCA in R 00:00:00
37.1 Welcome to Part 10 - Model Selection & Boosting
37.1 Welcome to Part 10 – Model Selection & Boosting 00:00:00
38.1 How to get the dataset
38.1 How to get the dataset 00:00:00
38.2 k-Fold Cross Validation in Python
38.2 k-Fold Cross Validation in Python 00:00:00
38.3 k-Fold Cross Validation in R
38.3 k-Fold Cross Validation in R 00:00:00
38.4 Grid Search in Python - Step 1
38.4 Grid Search in Python – Step 1 00:00:00
38.5 Grid Search in Python - Step 2
38.5 Grid Search in Python – Step 2 00:00:00
38.6 Grid Search in R
38.6 Grid Search in R 00:00:00
39.1 How to get the dataset
39.1 How to get the dataset 00:00:00
39.2 XGBoost in Python - Step 1
39.2 XGBoost in Python – Step 1 00:00:00
39.3 XGBoost in Python - Step 2
39.3 XGBoost in Python – Step 2 00:00:00
39.4 XGBoost in R
39.4 XGBoost in R 00:00:00
40.1 Your Special Bonus
40.1 Your Special Bonus 00:00:00

Course Reviews

N.A

ratings
  • 5 stars0
  • 4 stars0
  • 3 stars0
  • 2 stars0
  • 1 stars0

No Reviews found for this course.

PRIVATE COURSE
  • PRIVATE
  • 1 week, 3 days
0 STUDENTS ENROLLED
    Copyright @2019