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| [TGx]Downloaded from torrentgalaxy.to .txt | 585 B | ||
| [TutsNode.com] - 2021 Python for Data Science & Machine Learning from A-Z | |||
| 1. Introduction | |||
| 1. Who is This Course For.mp4 | 17.16 MB | ||
| 1. Who is This Course For.srt | 3.95 KB | ||
| 2. Data Science + Machine Learning Marketplace.mp4 | 46.94 MB | ||
| 2. Data Science + Machine Learning Marketplace.srt | 10.4 KB | ||
| 3. Data Science Job Opportunities.mp4 | 29.42 MB | ||
| 3. Data Science Job Opportunities.srt | 6.86 KB | ||
| 4. Data Science Job Roles.mp4 | 79.8 MB | ||
| 4. Data Science Job Roles.srt | 15.73 KB | ||
| 5. What is a Data Scientist.mp4 | 127.47 MB | ||
| 5. What is a Data Scientist.srt | 26.86 KB | ||
| 6. How To Get a Data Science Job.mp4 | 131.19 MB | ||
| 6. How To Get a Data Science Job.srt | 30.62 KB | ||
| 7. Data Science Projects Overview.mp4 | 79.48 MB | ||
| 7. Data Science Projects Overview.srt | 19.09 KB | ||
| 10. Data Loading & Exploration | |||
| 1. Exploratory Data Analysis.mp4 | 50.56 MB | ||
| 1. Exploratory Data Analysis.srt | 19.07 KB | ||
| 11. Data Cleaning | |||
| 1. Feature Scaling.mp4 | 19.38 MB | ||
| 1. Feature Scaling.srt | 11.58 KB | ||
| 2. Data Cleaning.mp4 | 30.21 MB | ||
| 2. Data Cleaning.srt | 11.53 KB | ||
| 12. Feature Selecting and Engineering | |||
| 1. Feature Engineering.mp4 | 18.41 MB | ||
| 1. Feature Engineering.srt | 9.42 KB | ||
| 13. Linear and Logistic Regression | |||
| 1. Linear Regression Intro.mp4 | 30.8 MB | ||
| 1. Linear Regression Intro.srt | 12.16 KB | ||
| 2. Gradient Descent.mp4 | 15.93 MB | ||
| 2. Gradient Descent.srt | 8.44 KB | ||
| 3. Linear Regression + Correlation Methods.mp4 | 110.38 MB | ||
| 3. Linear Regression + Correlation Methods.srt | 38.61 KB | ||
| 4. Linear Regression Implementation.mp4 | 17.86 MB | ||
| 4. Linear Regression Implementation.srt | 6.88 KB | ||
| 5. Logistic Regression.mp4 | 8.9 MB | ||
| 5. Logistic Regression.srt | 4.93 KB | ||
| 14. K Nearest Neighbors | |||
| 1. KNN Overview.mp4 | 12.89 MB | ||
| 1. KNN Overview.srt | 4.27 KB | ||
| 10. Feature scaling in KNN.mp4 | 49.39 MB | ||
| 10. Feature scaling in KNN.srt | 8.06 KB | ||
| 11. Curse of dimensionality.mp4 | 45.99 MB | ||
| 11. Curse of dimensionality.srt | 9.6 KB | ||
| 12. KNN use cases.mp4 | 28.92 MB | ||
| 12. KNN use cases.srt | 4.85 KB | ||
| 13. KNN pros and cons.mp4 | 30.45 MB | ||
| 13. KNN pros and cons.srt | 7.75 KB | ||
| 2. parametric vs non-parametric models.mp4 | 15.63 MB | ||
| 2. parametric vs non-parametric models.srt | 4.73 KB | ||
| 3. EDA on Iris Dataset.mp4 | 161.88 MB | ||
| 3. EDA on Iris Dataset.srt | 31.65 KB | ||
| 4. The KNN Intuition.mp4 | 8.09 MB | ||
| 4. The KNN Intuition.srt | 3.06 KB | ||
| 5. Implement the KNN algorithm from scratch.mp4 | 86.97 MB | ||
| 5. Implement the KNN algorithm from scratch.srt | 17.14 KB | ||
| 6. Compare the result with the sklearn library.mp4 | 24.57 MB | ||
| 6. Compare the result with the sklearn library.srt | 5.1 KB | ||
| 7. Hyperparameter tuning using the cross-validation.mp4 | 90.3 MB | ||
| 7. Hyperparameter tuning using the cross-validation.srt | 14.66 KB | ||
| 8. The decision boundary visualization.mp4 | 16.94 MB | ||
| 8. The decision boundary visualization.srt | 7.07 KB | ||
| 9. Manhattan vs Euclidean Distance.mp4 | 30.49 MB | ||
| 9. Manhattan vs Euclidean Distance.srt | 7.75 KB | ||
| 15. Decision Trees | |||
| 1. Decision Trees Section Overview.mp4 | 16.46 MB | ||
| 1. Decision Trees Section Overview.srt | 5.6 KB | ||
| 10. Visualizing the tree.mp4 | 68.17 MB | ||
| 10. Visualizing the tree.srt | 15 KB | ||
| 11. Plot the features importance.mp4 | 31.67 MB | ||
| 11. Plot the features importance.srt | 7.75 KB | ||
| 12. Decision Trees Hyper-parameters.mp4 | 81.27 MB | ||
| 12. Decision Trees Hyper-parameters.srt | 16.12 KB | ||
| 13. Pruning.mp4 | 112.97 MB | ||
| 13. Pruning.srt | 24.35 KB | ||
| 14. [Optional] Gain Ration.mp4 | 19.18 MB | ||
| 14. [Optional] Gain Ration.srt | 3.7 KB | ||
| 15. Decision Trees Pros and Cons.mp4 | 47.74 MB | ||
| 15. Decision Trees Pros and Cons.srt | 10.6 KB | ||
| 16. [Project] Predict whether income exceeds $50Kyr - Overview.mp4 | 15.11 MB | ||
| 16. [Project] Predict whether income exceeds $50Kyr - Overview.srt | 3.6 KB | ||
| 2. EDA on Adult Dataset.mp4 | 123.19 MB | ||
| 2. EDA on Adult Dataset.srt | 23.77 KB | ||
| 3. What is Entropy and Information Gain.mp4 | 136.08 MB | ||
| 3. What is Entropy and Information Gain.srt | 29.33 KB | ||
| 4. The Decision Tree ID3 algorithm from scratch Part 1.mp4 | 85.27 MB | ||
| 4. The Decision Tree ID3 algorithm from scratch Part 1.srt | 14.91 KB | ||
| 5. The Decision Tree ID3 algorithm from scratch Part 2.mp4 | 63.96 MB | ||
| 5. The Decision Tree ID3 algorithm from scratch Part 2.srt | 10.62 KB | ||
| 6. The Decision Tree ID3 algorithm from scratch Part 3.mp4 | 33.41 MB | ||
| 6. The Decision Tree ID3 algorithm from scratch Part 3.srt | 5.75 KB | ||
| 7. ID3 - Putting Everything Together.mp4 | 182.48 MB | ||
| 7. ID3 - Putting Everything Together.srt | 31.03 KB | ||
| 8. Evaluating our ID3 implementation.mp4 | 121.94 MB | ||
| 8. Evaluating our ID3 implementation.srt | 24.54 KB | ||
| 9. Compare with Sklearn implementation.mp4 | 65.58 MB | ||
| 9. Compare with Sklearn implementation.srt | 12.27 KB | ||
| 16. Ensemble Learning and Random Forests | |||
| 1. Ensemble Learning Section Overview.mp4 | 16.07 MB | ||
| 1. Ensemble Learning Section Overview.srt | 5.17 KB | ||
| 10. Random Forests Pros and Cons.mp4 | 19.69 MB | ||
| 10. Random Forests Pros and Cons.srt | 7.85 KB | ||
| 11. What is Boosting.mp4 | 35.44 MB | ||
| 11. What is Boosting.srt | 6.86 KB | ||
| 12. AdaBoost Part 1.mp4 | 25.53 MB | ||
| 12. AdaBoost Part 1.srt | 5.5 KB | ||
| 13. AdaBoost Part 2.mp4 | 85.94 MB | ||
| 13. AdaBoost Part 2.srt | 20.87 KB | ||
| 2. What is Ensemble Learning.mp4 | 91.97 MB | ||
| 2. What is Ensemble Learning.srt | 17.42 KB | ||
| 3. What is Bootstrap Sampling.mp4 | 55.88 MB | ||
| 3. What is Bootstrap Sampling.srt | 11.13 KB | ||
| 4. What is Bagging.mp4 | 29.48 MB | ||
| 4. What is Bagging.srt | 7.75 KB | ||
| 5. Out-of-Bag Error (OOB Error).mp4 | 42.03 MB | ||
| 5. Out-of-Bag Error (OOB Error).srt | 9.97 KB | ||
| 6. Implementing Random Forests from scratch Part 1.mp4 | 202.55 MB | ||
| 6. Implementing Random Forests from scratch Part 1.srt | 30.12 KB | ||
| 7. Implementing Random Forests from scratch Part 2.mp4 | 50.5 MB | ||
| 7. Implementing Random Forests from scratch Part 2.srt | 8.27 KB | ||
| 8. Compare with sklearn implementation.mp4 | 27.65 MB | ||
| 8. Compare with sklearn implementation.srt | 4.94 KB | ||
| 9. Random Forests Hyper-Parameters.mp4 | 39.67 MB | ||
| 9. Random Forests Hyper-Parameters.srt | 5.97 KB | ||
| 17. Support Vector Machines | |||
| 1. SVM Outline.mp4 | 35.31 MB | ||
| 1. SVM Outline.srt | 7.4 KB | ||
| 10. SMV - Project Overview.mp4 | 39.61 MB | ||
| 10. SMV - Project Overview.srt | 6.23 KB | ||
| 2. SVM intuition.mp4 | 48.86 MB | ||
| 2. SVM intuition.srt | 16.01 KB | ||
| 3. Hard vs Soft Margins.mp4 | 65.64 MB | ||
| 3. Hard vs Soft Margins.srt | 18.92 KB | ||
| 4. C hyper-parameter.mp4 | 21.06 MB | ||
| 4. C hyper-parameter.srt | 5.64 KB | ||
| 5. Kernel Trick.mp4 | 77.05 MB | ||
| 5. Kernel Trick.srt | 18.19 KB | ||
| 6. SVM - Kernel Types.mp4 | 126.38 MB | ||
| 6. SVM - Kernel Types.srt | 26.75 KB | ||
| 7. SVM with Linear Dataset (Iris).mp4 | 101.56 MB | ||
| 7. SVM with Linear Dataset (Iris).srt | 19.85 KB | ||
| 8. SVM with Non-linear Dataset.mp4 | 111.55 MB | ||
| 8. SVM with Non-linear Dataset.srt | 18.38 KB | ||
| 9. SVM with Regression.mp4 | 25 MB | ||
| 9. SVM with Regression.srt | 8 KB | ||
| 18. K-means | |||
| 1. Unsupervised Machine Learning Intro.mp4 | 100.92 MB | ||
| 1. Unsupervised Machine Learning Intro.srt | 29.36 KB | ||
| 1.1 Unsupervised Learning.pdf | 636.55 KB | ||
| 2. Unsupervised Machine Learning Continued.mp4 | 83.13 MB | ||
| 2. Unsupervised Machine Learning Continued.srt | 29.19 KB | ||
| 3. Representing Clusters.mp4 | 109.62 MB | ||
| 3. Representing Clusters.srt | 28.32 KB | ||
| 19. PCA | |||
| 1. PCA Section Overview.mp4 | 31.77 MB | ||
| 1. PCA Section Overview.srt | 7.06 KB | ||
| 10. PCA - Feature Scaling and Screen Plot.mp4 | 68.2 MB | ||
| 10. PCA - Feature Scaling and Screen Plot.srt | 14.4 KB | ||
| 11. PCA - Supervised vs Unsupervised.mp4 | 35.79 MB | ||
| 11. PCA - Supervised vs Unsupervised.srt | 7.12 KB | ||
| 12. PCA - Visualization.mp4 | 68.02 MB | ||
| 12. PCA - Visualization.srt | 10.96 KB | ||
| 2. What is PCA.mp4 | 47.26 MB | ||
| 2. What is PCA.srt | 14.59 KB | ||
| 3. PCA Drawbacks.mp4 | 19.44 MB | ||
| 3. PCA Drawbacks.srt | 4.9 KB | ||
| 4. PCA Algorithm Steps (Mathematics).mp4 | 57.73 MB | ||
| 4. PCA Algorithm Steps (Mathematics).srt | 18.59 KB | ||
| 5. Covariance Matrix vs SVD.mp4 | 38.74 MB | ||
| 5. Covariance Matrix vs SVD.srt | 6.54 KB | ||
| 6. PCA - Main Applications.mp4 | 10.05 MB | ||
| 6. PCA - Main Applications.srt | 3.9 KB | ||
| 7. PCA - Image Compression.mp4 | 249.92 MB | ||
| 7. PCA - Image Compression.srt | 39.37 KB | ||
| 8. PCA Data Preprocessing.mp4 | 120.46 MB | ||
| 8. PCA Data Preprocessing.srt | 21.05 KB | ||
| 9. PCA - Biplot and the Screen Plot.mp4 | 135.6 MB | ||
| 9. PCA - Biplot and the Screen Plot.srt | 26.37 KB | ||
| 2. Data Science & Machine Learning Concepts | |||
| 1. Why We Use Python.mp4 | 13.51 MB | ||
| 1. Why We Use Python.srt | 4.91 KB | ||
| 2. What is Data Science.mp4 | 87.99 MB | ||
| 2. What is Data Science.srt | 21.23 KB | ||
| 3. What is Machine Learning.mp4 | 83.41 MB | ||
| 3. What is Machine Learning.srt | 22.93 KB | ||
| 4. Machine Learning Concepts & Algorithms.mp4 | 77.98 MB | ||
| 4. Machine Learning Concepts & Algorithms.srt | 23.56 KB | ||
| 5. What is Deep Learning.mp4 | 77.81 MB | ||
| 5. What is Deep Learning.srt | 15.83 KB | ||
| 6. Machine Learning vs Deep Learning.mp4 | 75.92 MB | ||
| 6. Machine Learning vs Deep Learning.srt | 17.9 KB | ||
| 20. Data Science Career | |||
| 1. Creating A Data Science Resume.mp4 | 37.08 MB | ||
| 1. Creating A Data Science Resume.srt | 10.52 KB | ||
| 2. Data Science Cover Letter.mp4 | 22.96 MB | ||
| 2. Data Science Cover Letter.srt | 5.99 KB | ||
| 3. How to Contact Recruiters.mp4 | 24.65 MB | ||
| 3. How to Contact Recruiters.srt | 7.34 KB | ||
| 4. Getting Started with Freelancing.mp4 | 30.24 MB | ||
| 4. Getting Started with Freelancing.srt | 7.08 KB | ||
| 5. Top Freelance Websites.mp4 | 29.55 MB | ||
| 5. Top Freelance Websites.srt | 8.37 KB | ||
| 6. Personal Branding.mp4 | 30.49 MB | ||
| 6. Personal Branding.srt | 6.42 KB | ||
| 7. Networking Do's and Don'ts.mp4 | 23.7 MB | ||
| 7. Networking Do's and Don'ts.srt | 6.28 KB | ||
| 8. Importance of a Website.mp4 | 15.37 MB | ||
| 8. Importance of a Website.srt | 4.79 KB | ||
| 3. Python For Data Science | |||
| 1. What is Programming.mp4 | 18.35 MB | ||
| 1. What is Programming.srt | 9.03 KB | ||
| 10. Python Conditional Statements.mp4 | 54.61 MB | ||
| 10. Python Conditional Statements.srt | 18.15 KB | ||
| 11. Python For Loops and While Loops.mp4 | 25.61 MB | ||
| 11. Python For Loops and While Loops.srt | 10.74 KB | ||
| 12. Python Lists.mp4 | 21.44 MB | ||
| 12. Python Lists.srt | 7.12 KB | ||
| 13. More about Lists.mp4 | 60.42 MB | ||
| 13. More about Lists.srt | 19.44 KB | ||
| 14. Python Tuples.mp4 | 54.53 MB | ||
| 14. Python Tuples.srt | 14.96 KB | ||
| 15. Python Dictionaries.mp4 | 104.18 MB | ||
| 15. Python Dictionaries.srt | 27.72 KB | ||
| 16. Python Sets.mp4 | 29.43 MB | ||
| 16. Python Sets.srt | 13.48 KB | ||
| 17. Compound Data Types & When to use each one.mp4 | 47.07 MB | ||
| 17. Compound Data Types & When to use each one.srt | 17.98 KB | ||
| 18. Python Functions.mp4 | 62.51 MB | ||
| 18. Python Functions.srt | 20.84 KB | ||
| 19. Object Oriented Programming in Python.mp4 | 70.25 MB | ||
| 19. Object Oriented Programming in Python.srt | 25.73 KB | ||
| 2. Why Python for Data Science.mp4 | 16.32 MB | ||
| 2. Why Python for Data Science.srt | 6.77 KB | ||
| 2.1 Importing Python Data.pdf | 61.55 KB | ||
| 2.2 Python Basics.pdf | 127.71 KB | ||
| 3. What is Jupyter.mp4 | 14.57 MB | ||
| 3. What is Jupyter.srt | 5.92 KB | ||
| 3.1 Jupyter Notebook.pdf | 307.15 KB | ||
| 4. What is Google Colab.mp4 | 8.26 MB | ||
| 4. What is Google Colab.srt | 4.89 KB | ||
| 5. Python Variables, Booleans and None.mp4 | 38.26 MB | ||
| 5. Python Variables, Booleans and None.srt | 15.22 KB | ||
| 6. Getting Started with Google Colab.mp4 | 35.09 MB | ||
| 6. Getting Started with Google Colab.srt | 12.39 KB | ||
| 7. Python Operators.mp4 | 86.76 MB | ||
| 7. Python Operators.srt | 31.44 KB | ||
| 8. Python Numbers & Booleans.mp4 | 25.61 MB | ||
| 8. Python Numbers & Booleans.srt | 9.6 KB | ||
| 9. Python Strings.mp4 | 56.27 MB | ||
| 9. Python Strings.srt | 16.14 KB | ||
| 4. Statistics for Data Science | |||
| 1. Intro To Statistics.mp4 | 21.24 MB | ||
| 1. Intro To Statistics.srt | 11.17 KB | ||
| 2. Descriptive Statistics.mp4 | 21.48 MB | ||
| 2. Descriptive Statistics.srt | 10.04 KB | ||
| 3. Measure of Variability.mp4 | 38.2 MB | ||
| 3. Measure of Variability.srt | 18.19 KB | ||
| 4. Measure of Variability Continued.mp4 | 34.61 MB | ||
| 4. Measure of Variability Continued.srt | 13.32 KB | ||
| 5. Measures of Variable Relationship.mp4 | 23.57 MB | ||
| 5. Measures of Variable Relationship.srt | 10.72 KB | ||
| 6. Inferential Statistics.mp4 | 45.01 MB | ||
| 6. Inferential Statistics.srt | 22.1 KB | ||
| 7. Measure of Asymmetry.mp4 | 6.76 MB | ||
| 7. Measure of Asymmetry.srt | 2.75 KB | ||
| 8. Sampling Distribution.mp4 | 26.46 MB | ||
| 8. Sampling Distribution.srt | 10.25 KB | ||
| 5. Probability & Hypothesis Testing | |||
| 1. What is Exactly is Probability.mp4 | 27.17 MB | ||
| 1. What is Exactly is Probability.srt | 6.73 KB | ||
| 2. Expected Values.mp4 | 14.72 MB | ||
| 2. Expected Values.srt | 4.11 KB | ||
| 3. Relative Frequency.mp4 | 32.69 MB | ||
| 3. Relative Frequency.srt | 8.67 KB | ||
| 4. Hypothesis Testing Overview.mp4 | 60.59 MB | ||
| 4. Hypothesis Testing Overview.srt | 14.58 KB | ||
| 6. NumPy Data Analysis | |||
| 1. Intro NumPy Array Data Types.mp4 | 34.67 MB | ||
| 1. Intro NumPy Array Data Types.srt | 18.29 KB | ||
| 1.1 NumPy Basics.pdf | 77.1 KB | ||
| 2. NumPy Arrays.mp4 | 32.33 MB | ||
| 2. NumPy Arrays.srt | 11.09 KB | ||
| 3. NumPy Arrays Basics.mp4 | 39.98 MB | ||
| 3. NumPy Arrays Basics.srt | 16.84 KB | ||
| 4. NumPy Array Indexing.mp4 | 34.74 MB | ||
| 4. NumPy Array Indexing.srt | 14.02 KB | ||
| 5. NumPy Array Computations.mp4 | 16.96 MB | ||
| 5. NumPy Array Computations.srt | 8.6 KB | ||
| 6. Broadcasting.mp4 | 17.86 MB | ||
| 6. Broadcasting.srt | 6.36 KB | ||
| 7. Pandas Data Analysis | |||
| 1. Introduction to Pandas.mp4 | 46.83 MB | ||
| 1. Introduction to Pandas.srt | 22.3 KB | ||
| 1.1 Pandas.pdf | 110.18 KB | ||
| 1.2 Pandas Basics.pdf | 77.06 KB | ||
| 2. Introduction to Pandas Continued.mp4 | 71.1 MB | ||
| 2. Introduction to Pandas Continued.srt | 26.81 KB | ||
| 8. Python Data Visualization | |||
| 1. Data Visualization Overview.mp4 | 73.08 MB | ||
| 1. Data Visualization Overview.srt | 36.78 KB | ||
| 2. Different Data Visualization Libraries in Python.mp4 | 15.95 MB | ||
| 2. Different Data Visualization Libraries in Python.srt | 8.76 KB | ||
| 3. Python Data Visualization Implementation.mp4 | 27.43 MB | ||
| 3. Python Data Visualization Implementation.srt | 12.44 KB | ||
| 9. Machine Learning | |||
| 1. Introduction To Machine Learning.mp4 | 98.71 MB | ||
| 1. Introduction To Machine Learning.srt | 36.98 KB | ||
| 1.1 Supervised Learning.pdf | 836.74 KB |

Description
Learn Python for Data Science & Machine Learning from A-Z
In this practical, hands-on course you’ll learn how to program using Python for Data Science and Machine Learning. This includes data analysis, visualization, and how to make use of that data in a practical manner.
Our main objective is to give you the education not just to understand the ins and outs of the Python programming language for Data Science and Machine Learning, but also to learn exactly how to become a professional Data Scientist with Python and land your first job.
We’ll go over some of the best and most important Python libraries for data science such as NumPy, Pandas, and Matplotlib +
NumPy — A library that makes a variety of mathematical and statistical operations easier; it is also the basis for many features of the pandas library.
Pandas — A Python library created specifically to facilitate working with data, this is the bread and butter of a lot of Python data science work.
NumPy and Pandas are great for exploring and playing with data. Matplotlib is a data visualization library that makes graphs as you’d find in Excel or Google Sheets. Blending practical work with solid theoretical training, we take you from the basics of Python Programming for Data Science to mastery.
This Machine Learning with Python course dives into the basics of machine learning using Python. You’ll learn about supervised vs. unsupervised learning, look into how statistical modeling relates to machine learning, and do a comparison of each.
We understand that theory is important to build a solid foundation, we understand that theory alone isn’t going to get the job done so that’s why this course is packed with practical hands-on examples that you can follow step by step. Even if you already have some coding experience, or want to learn about the advanced features of the Python programming language, this course is for you!
Python coding experience is either required or recommended in job postings for data scientists, machine learning engineers, big data engineers, IT specialists, database developers, and much more. Adding Python coding language skills to your resume will help you in any one of these data specializations requiring mastery of statistical techniques.
Together we’re going to give you the foundational education that you need to know not just on how to write code in Python, analyze and visualize data and utilize machine learning algorithms but also how to get paid for your newly developed programming skills.
The course covers 5 main areas:
1: PYTHON FOR DS+ML COURSE INTRO
This intro section gives you a full introduction to the Python for Data Science and Machine Learning course, data science industry, and marketplace, job opportunities and salaries, and the various data science job roles.
Intro to Data Science + Machine Learning with Python
Data Science Industry and Marketplace
Data Science Job Opportunities
How To Get a Data Science Job
Machine Learning Concepts & Algorithms
2: PYTHON DATA ANALYSIS/VISUALIZATION
This section gives you a full introduction to the Data Analysis and Data Visualization with Python with hands-on step by step training.
Python Crash Course
NumPy Data Analysis
Pandas Data Analysis
Matplotlib
Seaborn
Plotly
3: MATHEMATICS FOR DATA SCIENCE
This section gives you a full introduction to the mathematics for data science such as statistics and probability.
Descriptive Statistics
Measure of Variability
Inferential Statistics
Probability
Hypothesis Testing
4: MACHINE LEARNING
This section gives you a full introduction to Machine Learning including Supervised & Unsupervised ML with hands-on step-by-step training.
Intro to Machine Learning
Data Preprocessing
Linear Regression
Logistic Regression
K-Nearest Neighbors
Decision Trees
Ensemble Learning
Support Vector Machines
K-Means Clustering
PCA
5: STARTING A DATA SCIENCE CAREER
This section gives you a full introduction to starting a career as a Data Scientist with hands-on step by step training.
Creating a Resume
Creating a Cover Letter
Personal Branding
Freelancing + Freelance websites
Importance of Having a Website
Networking
By the end of the course you’ll be a professional Data Scientist with Python and confidently apply for jobs and feel good knowing that you have the skills and knowledge to back it up.
Who this course is for:
Students who want to learn about Python for Data Science & Machine Learning
Requirements
Students should have basic computer skills
Students would benefit from having prior Python Experience but not necessary
Last Updated 1/2021
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