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Course / Course Details

Data Science & Machine Learning Complete Course

  • Super admin image

    By - Super admin

  • 1 students
  • 40 Hours 20 Min
  • (0)

Course Requirements

  • Basic knowledge of mathematics (algebra & statistics)

  • Basic programming knowledge (Python or R preferred)

  • No prior Machine Learning experience required

  • Laptop/PC with internet connection

  • Willingness to practice and work on projects

Course Description

This comprehensive Machine Learning course in Python and R is designed to help you master core ML concepts and real-world applications. Learn from industry experts and build a strong foundation in supervised and unsupervised learning, Deep Learning, Reinforcement Learning, NLP, and Dimensionality Reduction.

Through practical exercises, real-world case studies, and downloadable code templates, you will gain hands-on experience in building, evaluating, and combining multiple ML models to make accurate predictions and deliver data-driven business solutions.

Suitable for beginners and professionals looking to advance their Machine Learning skills.

Course Outcomes

By the end of this course, learners will be able to:

  • Understand core Machine Learning concepts and algorithms

  • Build and implement ML models using Python and R

  • Perform data preprocessing, feature selection, and dimensionality reduction

  • Apply Supervised, Unsupervised, Deep Learning, NLP, and Reinforcement Learning techniques

  • Evaluate and optimize model performance

  • Combine multiple models to improve prediction accuracy

  • Solve real-world business problems using data-driven solutions

  • Develop a strong portfolio of practical ML projects

Course Curriculum

  • 32 chapters
  • 262 lectures
  • 0 quizzes
  • 40 Hours 20 Min total length
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1 Applications of Machine Learning
4 Min

2 Why Machine Learning is the Future
7 Min

3 Updates on Udemy Reviews
3 Min

4 Installing Python and Anaconda (Mac, Linux & Windows)
8 Min

5 Installing R and R Studio (Mac, Linux & Windows)
6 Min

1 False Positives & False Negatives
8 Min

2 Confusion Matrix
5 Min

3 Accuracy Paradox
3 Min

4 CAP Curve
12 Min

5 CAP Curve Analysis
7 Min

1 Random Forest Classification Intuition
5 Min

2 How to get the dataset
4 Min

3 Random Forest Classification in Python
20 Min

4 Random Forest Classification in R
20 Min

1 Decision Tree Classification Intuition
9 Min

2 How to get the dataset
4 Min

3 Decision Tree Classification in Python
13 Min

4 Decision Tree Classification in R
20 Min

1 Bayes Theorem
21 Min

2 Naive Bayes Intuition
15 Min

3 Naive Bayes Intuition (Challenge Reveal)
6 Min

4 Naive Bayes Intuition (Extras)
10 Min

5 How to get the dataset
4 Min

6 Naive Bayes in Python
10 Min

7 Naive Bayes in R
15 Min

1 Kernel SVM Intuition
4 Min

2 Mapping to a higher dimension
8 Min

3 The Kernel Trick
13 Min

4 Types of Kernel Functions
4 Min

5 How to get the dataset
4 Min

6 Kernel SVM in Python
18 Min

7 Kernel SVM in R
17 Min

1 SVM Intuition
10 Min

2 How to get the dataset
4 Min

3 SVM in Python
13 Min

4 SVM in R
13 Min

1 K-Nearest Neighbor Intuition
5 Min

2 How to get the dataset
4 Min

3 K-NN in Python
15 Min

4 K-NN in R
16 Min

1 Logistic Regression Intuition
18 Min

2 How to get the dataset
4 Min

3 Logistic Regression in Python - Step 1
6 Min

4 Logistic Regression in Python - Step 2
4 Min

5 Logistic Regression in Python - Step 3
3 Min

6 Logistic Regression in Python - Step 4
5 Min

7 Logistic Regression in Python - Step 5
20 Min

8 Python Classification Template
4 Min

9 Logistic Regression in R - Step 1
6 Min

10 Logistic Regression in R - Step 2
3 Min

11 Logistic Regression in R - Step 3
6 Min

12 Logistic Regression in R - Step 4
3 Min

13 Logistic Regression in R - Step 5
20 Min

14 R Classification Template
5 Min

1 R-Squared Intuition
6 Min

2 Adjusted R-Squared Intuition
10 Min

3 Evaluating Regression Models Performance - Homework's Final Part
9 Min

4 Interpreting Linear Regression Coefficients
10 Min

1 Random Forest Regression Intuition
7 Min

2 How to get the dataset
4 Min

3 Random Forest Regression in Python
17 Min

4 Random Forest Regression in R
18 Min

1 Decision Tree Regression Intuition
12 Min

2 How to get the dataset
4 Min

3 Decision Tree Regression in Python
15 Min

4 Decision Tree Regression in R
20 Min

1 How to get the dataset
4 Min

2 SVR Intuition
9 Min

3 SVR in Python
20 Min

4 SVR in R
12 Min

1 Polynomial Regression in R - Step 3
20 Min

2 Polynomial Regression in R - Step 4
10 Min

3 R Regression Template
12 Min

4 Polynomial Regression Intuition
5.1 Min

5 How to get the dataset
4 Min

6 Polynomial Regression in Python - Step 1
12 Min

7 Polynomial Regression in Python - Step 2
12 Min

8 Polynomial Regression in Python - Step 3
20 Min

9 Polynomial Regression in Python - Step 4
6 Min

10 Python Regression Template
11 Min

11 Polynomial Regression in R - Step 1
10 Min

12 Polynomial Regression in R - Step 2
10 Min

1 How to get the dataset
4 Min

2 Dataset + Business Problem Description
4 Min

3 Multiple Linear Regression Intuition - Step 1
2 Min

4 Multiple Linear Regression Intuition - Step 2
1 Min

5 Multiple Linear Regression Intuition - Step 3
8 Min

6 Multiple Linear Regression Intuition - Step 4
3 Min

7 Multiple Linear Regression Intuition - Step 5
16 Min

8 Multiple Linear Regression in Python - Step 1
16 Min

9 Multiple Linear Regression in Python - Step 2
3 Min

10 Multiple Linear Regression in Python - Step 3
6 Min

11 Multiple Linear Regression in Python - Backward Elimination - Preparation
10 Min

12 Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !
13 Min

13 Multiple Linear Regression in Python - Backward Elimination - Homework Solution
10 Min

14 Multiple Linear Regression in R - Step 1
8 Min

15 Multiple Linear Regression in R - Step 2
11 Min

16 Multiple Linear Regression in R - Step 3
5 Min

17 Multiple Linear Regression in R - Backward Elimination - HOMEWORK !
5 Min

18 Multiple Linear Regression in R - Backward Elimination - Homework Solution
8 Min

1 How to get the dataset
4 Min

2 Dataset + Business Problem Description
3 Min

3 Simple Linear Regression Intuition - Step 1
6 Min

4 Simple Linear Regression Intuition - Step 2
4 Min

5 Simple Linear Regression in Python - Step 1
10 Min

6 Simple Linear Regression in Python - Step 2
9 Min

7 Simple Linear Regression in Python - Step 3
7 Min

8 Simple Linear Regression in Python - Step 4
15 Min

9 Simple Linear Regression in R - Step 1
5 Min

10 Simple Linear Regression in R - Step 2
6 Min

11 Simple Linear Regression in R - Step 3
4 Min

12 Simple Linear Regression in R - Step 4
16 Min

1 Welcome to Part 1 - Data Preprocessing
2 Min

2 Get the dataset
7 Min

3 Importing the Libraries
6 Min

4 Importing the Dataset
12 Min

5 Missing Data
16 Min

6 Categorical Data
18 Min

7 Splitting the Dataset into the Training set and Test set
18 Min

8 Feature Scaling
16 Min

9 And here is our Data Preprocessing Template!
9 Min

1 K-Means Clustering Intuition
15 Min

2 K-Means Random Initialization Trap
8 Min

3 K-Means Selecting The Number Of Clusters
12 Min

4 How to get the dataset
4 Min

5 K-Means Clustering in Python
18 Min

6 K-Means Clustering in R
12 Min

1 Hierarchical Clustering Intuition
9 Min

2 Hierarchical Clustering How Dendrograms Work
9 Min

3 Hierarchical Clustering Using Dendrograms
12 Min

4 How to get the dataset
4 Min

5 HC in Python - Step 1
5 Min

6 HC in Python - Step 2
7 Min

7 HC in Python - Step 3
6 Min

8 HC in Python - Step 4
5 Min

9 HC in Python - Step 5
5 Min

10 HC in R - Step 1
4 Min

11 HC in R - Step 2
6 Min

12 HC in R - Step 3
4 Min

13 HC in R - Step 4
3 Min

14 HC in R - Step 5
3 Min

1 Apriori Intuition
19 Min

2 How to get the dataset
4 Min

3 Apriori in R - Step 1
20 Min

4 Apriori in R - Step 2
15 Min

5 Apriori in R - Step 3
20 Min

6 Apriori in Python - Step 1
18 Min

7 Apriori in Python - Step 2
15 Min

8 Apriori in Python - Step 3
12 Min

1 Eclat Intuition
6 Min

2 How to get the dataset
4 Min

3 Eclat in R
11 Min

1 1. The Multi-Armed Bandit Problem
16 Min

2 Upper Confidence Bound (UCB) Intuition
15 Min

3 How to get the dataset
4 Min

4 Upper Confidence Bound in Python - Step 1
15 Min

5 Upper Confidence Bound in Python - Step 2
19 Min

6 Upper Confidence Bound in Python - Step 3
19 Min

7 Upper Confidence Bound in Python - Step 4
4 Min

8 Upper Confidence Bound in R - Step 1
14 Min

9 Upper Confidence Bound in R - Step 2
16 Min

10 Upper Confidence Bound in R - Step 3
18 Min

11 Upper Confidence Bound in R - Step 4
14 Min

1 Thompson Sampling Intuition
20 Min

2 Algorithm Comparison UCB vs Thompson Sampling
9 Min

3 How to get the dataset
4 Min

4 Thompson Sampling in Python - Step 1
20 Min

5 Thompson Sampling in Python - Step 2
4 Min

6 Thompson Sampling in R - Step 1
20 Min

7 Thompson Sampling in R - Step 2
4 Min

1 Natural Language Processing Intuition
6 Min

2 How to get the dataset
4 Min

3 Natural Language Processing in Python - Step 1
13 Min

4 Natural Language Processing in Python - Step 2
11 Min

5 Natural Language Processing in Python - Step 3
2 Min

6 Natural Language Processing in Python - Step 1
13 Min

7 Natural Language Processing in Python - Step 5
8 Min

8 Natural Language Processing in Python - Step 6
4 Min

9 Natural Language Processing in Python - Step 7
8 Min

10 Natural Language Processing in Python - Step 8
17 Min

11 Natural Language Processing in Python - Step 9
6 Min

12 Natural Language Processing in Python - Step 10
10 Min

13 Natural Language Processing in R - Step 1
16 Min

14 15. Natural Language Processing in R - Step 2
9 Min

15 Natural Language Processing in R - Step 3
7 Min

16 Natural Language Processing in R - Step 4
3 Min

17 Natural Language Processing in R - Step 5
3 Min

18 Natural Language Processing in R - Step 6
6 Min

19 Natural Language Processing in R - Step 7
4 Min

20 Natural Language Processing in R - Step 8
6 Min

21 Natural Language Processing in R - Step 9
13 Min

22 Natural Language Processing in R - Step 10
18 Min

1 What is Deep Learning
13 Min

1 Plan of attack
3 Min

2 The Neuron
17 Min

3 The Activation Function
9 Min

4 How do Neural Networks work
13 Min

5 How do Neural Networks learn
13 Min

6 Gradient Descent
11 Min

7 Stochastic Gradient Descent
9 Min

8 Backpropagation
6 Min

9 How to get the dataset
4 Min

10 Business Problem Description
5 Min

11 ANN in Python - Step 1
13 Min

12 ANN in Python - Step 2
19 Min

13 ANN in Python - Step 3
4 Min

14 ANN in Python - Step 4
3 Min

15 ANN in Python - Step 5
13 Min

16 ANN in Python - Step 6
6 Min

17 ANN in Python - Step 7
4 Min

18 ANN in Python - Step 8
7 Min

19 ANN in Python - Step 9
7 Min

20 ANN in Python - Step 10
7 Min

21 ANN in R - Step 1
18 Min

22 ANN in R - Step 2
7 Min

23 ANN in R - Step 3
13 Min

24 ANN in R - Step 4
15 Min

1 Plan of attack
4 Min

2 What are convolutional neural networks
16 Min

3 Step 1 - Convolution Operation
17 Min

4 Step 1(b) - ReLU Layer
7 Min

5 Step 2 - Pooling
15 Min

6 Step 3 - Flattening
2 Min

7 Step 4 - Full Connection
20 Min

8 Summary.mp4
5 Min

9 Softmax & Cross-Entropy.mp4
19 Min

10 How to get the dataset.mp4
4 Min

11 CNN in Python - Step 1.mp4
13 Min

12 CNN in Python - Step 2.mp4
3 Min

13 CNN in Python - Step 3.mp4
2 Min

14 CNN in Python - Step 4.mp4
13 Min

15 CNN in Python - Step 5
5 Min

16 CNN in Python - Step 6.mp4
5 Min

17 CNN in Python - Step 7.mp4
6 Min

18 CNN in Python - Step 8.mp4
3 Min

19 19. CNN in Python - Step 9.mp4
20 Min

20 CNN in Python - Step 10.mp4
9 Min

1 Principal Component Analysis (PCA) Intuition
4 Min

2 How to get the dataset
4 Min

3 PCA in Python - Step 1
12 Min

4 PCA in Python - Step 2
9 Min

5 PCA in Python - Step 3
10 Min

6 PCA in R - Step 1
13 Min

7 PCA in R - Step 2
12 Min

8 PCA in R - Step 3
14 Min

1 Linear Discriminant Analysis (LDA) Intuition
4 Min

2 How to get the dataset.mp4
4 Min

3 LDA in Python.p4
19 Min

1 How to get the dataset
4 Min

2 Kernel PCA in Python
15 Min

3 Kernel PCA in R
21 Min

1 How to get the dataset
4 Min

2 k-Fold Cross Validation in Python
14 Min

3 k-Fold Cross Validation in R
20 Min

4 Grid Search in Python - Step 1
16 Min

5 Grid Search in Python - Step 2
11 Min

6 Grid search in R.mp4
14 Min

1 How to get the dataset
4 Min

2 XGBoost in Python - Step 1
10 Min

3 XGBoost in Python - Step 2
13 Min

4 XGBoost in R
19 Min

5 THANK YOU bonus video
3 Min

Instructor

Super admin

As the Super Admin of our platform, I bring over a decade of experience in managing and leading digital transformation initiatives. My journey began in the tech industry as a developer, and I have since evolved into a strategic leader with a focus on innovation and operational excellence. I am passionate about leveraging technology to solve complex problems and drive organizational growth. Outside of work, I enjoy mentoring aspiring tech professionals and staying updated with the latest industry trends.

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  • Duration 40 Hours 20 Min

  • Lectures 262 lessons

  • Enrolled 1 students

  • Category: Data

  • 0 Quizzes

  • Language: English

  • Skill Level Intermediate

  • Certificate of Completion

  • 120 Days Access

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