Skip to content
Generative AI: Practical AI Skills for Career Growth cover image

Generative AI: Practical AI Skills for Career Growth
Oak Academy

Build real-world generative AI skills to improve productivity, output quality and professional performance.

Summary

Price
£999 inc VAT
Or £83.25/mo. for 12 months...
Study method
Online, On Demand 
Course format
544 Videos
Duration
62.8 hours · Self-paced
Qualification
No formal qualification
Certificates
  • Reed Courses Certificate of Completion - Free
Additional info
  • Tutor is available to students

Overview

Generative Artificial Intelligence is transforming how people create content, solve problems, and automate everyday tasks. From writing and design to research, coding assistance, and productivity workflows, generative AI tools are now widely used across many industries. This comprehensive Generative AI Bundle is designed to help you understand and use modern generative AI tools effectively, creatively, and confidently.

This programme provides a complete learning path that starts with the fundamentals of generative AI and progresses toward practical, real-world applications. Whether you are completely new to AI or already familiar with some tools, this bundle helps you build strong practical skills that can be applied immediately in personal projects, business tasks, and professional workflows.

You will learn how generative AI models work at a high level, how to communicate with them using clear and effective prompts, and how to use AI tools for content creation, idea generation, research, summarization, coding assistance, image generation, and workflow automation. The course focuses on getting real results from AI tools rather than theoretical complexity.

The training is built around hands-on exercises and practical examples. You will work on real use cases such as creating written content, generating visuals, improving productivity, automating repetitive tasks, brainstorming ideas, building AI-assisted workflows, and enhancing decision-making with AI support. These exercises help you gain confidence and fluency in using generative AI tools in everyday scenarios.

Throughout the programme, you will gain experience with popular generative AI platforms and tools, learning how to adapt them to different needs and use cases. You will also explore prompt design techniques, iterative refinement, and best practices for getting consistent and high-quality outputs from AI systems.

The course is structured so beginners can follow easily, while still providing enough depth for learners who want to become highly efficient AI users. Each module includes clear explanations, demonstrations, quizzes, and applied tasks that reinforce practical understanding.

By the end of this Generative AI Bundle, you will be able to use generative AI tools confidently, create high-quality outputs across multiple domains, improve productivity, and integrate AI into daily work and creative processes.

This is not just a collection of AI tool demonstrations — it is a complete Generative AI Productivity & Skills Programme, designed to make you effective, confident, and capable in using generative AI in real-world applications.

Certificates

Curriculum

This course contains

Format: 544 Videos

Duration: 62h and 48m

    • 1: What is Artificial Intelligence 07:18
    • 2: Artificial Narrow Intelligence (ANI) 05:35
    • 3: Types of Artificial Intelligence - Artificial General Intelligence (AGI) 03:39
    • 4: Types of Artificial Intelligence - Artificial Super Intelligence (ASI) 03:39
    • 5: Machine Learning 03:41
    • 6: Deep Learning 03:43
    • 7: Machine Learning vs. Deep Learning 03:35
    • 8: Machine Learning Study with a Real Example- Lesson 1 03:57
    • 9: Machine Learning Study with a Real Example- Lesson 2 04:34
    • 10: Large Language Models(LLM) 05:08
    • 11: Natural Language Processing(NLP) 06:11
    • 12: A WARNING BEFORE SWITCHING TO CHATGPT 02:35
    • 13: Revolutionary of the Era- OpenAI 10:58
    • 14: The Revolution of the Age- Creating a ChatGPT Account 01:50
    • 15: Let's Get to Know the ChatGPT Interface 09:38
    • 16: ChatGPT- Differences Between Versions 10:20
    • 17: Differences in the ChatGPT-4 Interface 05:34
    • 18: ChatGPT's Endpoints 05:00
    • 19: ChatGPT's Secret to More Accurate Answers- Prompt 02:22
    • 20: Prompt Engineering Power- Lesson 1 09:27
    • 21: Prompt Engineering Power- Lesson 2 05:00
    • 22: Prompt Engineering Power- Lesson 3 03:53
    • 23: Prompt Engineering Power- Lesson 4 08:28
    • 24: Summary of Prompt Engineering Fundamentals 08:58
    • 25: Prompt Engineering- Sample Prompts 13:34
    • 26: Best Questions in Prompt Engineering- Lesson 1 10:30
    • 27: Best Questions in Prompt Engineering- Lesson 2 10:13
    • 28: Best Questions in Prompt Engineering- Lesson 3 09:20
    • 29: Best Questions in Prompt Engineering- Lesson 4 08:49
    • 30: Best Questions in Prompt Engineering- Lesson 5 04:12
    • 31: Summary of the Best Questions in Prompt Engineering 02:53
    • 32: Reinforcing the topic through a scenario 11:33
    • 33: Drawing a Roadmap to the Prompt 02:48
    • 34: Drawing a Roadmap to the Prompt- Directed Writing Request 06:57
    • 35: Drawing a Roadmap to the Prompt- Clear Explanation Method 04:54
    • 36: Drawing a Roadmap to the Prompt Example-Based Learning 12:55
    • 37: Prompt Strengthening Efforts- RGC(Role, Goals, Context) 05:53
    • 38: Prompt Strengthening Efforts- Constrained Responses 02:49
    • 39: Prompt Strengthening Efforts- Adding Visual Appeal 03:04
    • 40: Prompt Updates Lesson 1 06:29
    • 41: Prompt Updates Lesson 2 09:57
    • 42: Prompt Updates Lesson 3 07:45
    • 43: Prompt Güncellemeleri Lesson 4 06:58
    • 44: Useful extensions with ChatGPT- ChatGPT-Google Extension 03:01
    • 45: Useful Extensions with ChatGPT- Email Writing 02:40
    • 46: ChatGPT ile faydali eklentiler- Youtube videolarinin özeti 04:36
    • 47: ChatGPT with useful extensions- Talk to ChatGPT 04:44
    • 48: Useful Extensions with ChatGPT- Quick Access to ChatGPT 01:46
    • 49: Useful Extensions with ChatGPT- Dive Into Websites 03:43
    • 50: Useful Extensions with ChatGPT- Get Prompt Assistance 05:42
    • 51: Using the ChatGPT API 04:20
    • 52: ChatGPT Capabilities- File Reading 05:25
    • 53: ChatGPT Capabilities- Visual Reading 05:25
    • 54: ChatGPT Capabilities- Visual Reading 03:48
    • 55: ChatGPT Capabilities- Enhancing Images with DALL-E 06:48
    • 56: ChatGPT Capabilities- Enhancing Images with DALL-E 07:23
    • 57: Capabilities of ChatGPT- Improving Visuals Through Ready-Made Prompts 06:27
    • 58: ChatGPT Abilities- Combining Images 03:20
    • 59: ChatGPT Abilities- A Helper Site for Visual Prompts 07:22
    • 60: ChatGPT Capabilities- GPTs 05:56
    • 61: ChatGPT Capabilities- Create Your Own GPT 06:16
    • 62: Useful GPTs- Lesson 1 06:06
    • 63: Useful GPTs- Lesson 2 06:36
    • 64: Useful GPTs- Lesson 3 05:39
    • 65: Big News- Introducing ChatGPT-4o 04:55
    • 66: How to Use ChatGPT-4o 05:53
    • 67: Chronological Development of ChatGPT 05:21
    • 68: What Are the Capabilities of ChatGPT-4o 04:33
    • 69: As an App- ChatGPT 03:19
    • 70: Voice Communication with ChatGPT-4o 04:51
    • 71: Instant Translation in 50+ Languages 03:04
    • 72: Interview Preparation with ChatGPT-4o 18:07
    • 73: Visual Commentary with ChatGPT-4o 04:20
    • 74: Visual Commentary with ChatGPT-4o- Lesson 2 04:57
    • 75: Important Announcement 00:57
    • 76: ChatGPT Latest Updates – Free Version Interface Review 03:39
    • 77: ChatGPT Latest Updates – Paid Version Interface Review 06:52
    • 78: Chatgpt 4o Model 03:41
    • 79: Chatgpt 4o with scheduled task & 4.5 model 04:04
    • 80: ChatGPT o1 & o3 & o3 mini model 05:47
    • 81: ChatGPT – Other Models 10:36
    • 82: ChatGPT Canvas 10:00
    • 83: ChatGPT – Search & Deep Research 08:38
    • 84: 79 ChatGPT SORA Introduction 05:49
    • 85: 80 ChatGPT Sora Interface 08:32
    • 86: 81 Preparing for Prompting with Sora 06:21
    • 87: 82 Prompting with Sora 12:30
    • 88: 83 Sora – Reenact Historical Events 17:54
    • 89: 84 Let’s Use Sora in Education 13:10
    • 90: 85 Product Promotion with Sora – Lesson 1 11:56
    • 91: 86 Product Promotion with Sora – Lesson 2 14:10
    • 92: 1 Important Announcement 03:39
    • 93: 2 DeepSeek Introduction - Lesson 1 08:44
    • 94: 3 DeepSeek Introduction - Lesson 2 05:10
    • 95: 4 Creating a DeepSeek Account 02:37
    • 96: 5 Let's get to know the DeepSeek Interface 07:46
    • 97: 6 Writing prompts with DeepSeek Lesson 1 11:22
    • 98: 7 Writing a Prompt with DeepSeek- DeepThink (R1) 05:23
    • 99: 8 Writing a Prompt with DeepSeek- Search Button 01:53
    • 100: 9 Uploading and Reading Files with DeepSeek 10:53
    • 101: 10 Visual Analysis with DeepSeek- Graph Interpretation 06:48
    • 102: 11 Other Visual Studies That Can Be Done with DeepSeek 05:25
    • 103: 17 Copilot Introduction Lesson 1 08:01
    • 104: 18 Copilot Introduction Lesson 2 04:59
    • 105: 19 Creating a Copilot Account 04:35
    • 106: 20 Copilot Interface Review 07:19
    • 107: 21 Copilot vs. ChatGPT Comparison 12:34
    • 108: 22 Copilot – DeepSeek Comparison 03:24
    • 109: 23 Copilot – Story Writing 07:09
    • 110: 24 Copilot – Think Deeper 06:45
    • 111: 25 Reading Files with Copilot 10:52
    • 112: 26 Visual Analysis with Copilot 08:01
    • 113: 27 Generating Visuals with Copilot 04:28
    • 114: 28 Using Copilot AI - First Task - Field Knowledge 07:45
    • 115: 29 Using Copilot AI - Examining Unique Values 06:29
    • 116: 30 Using Copilot AI – Examining Statistics of Variables 12:18
    • 117: 31 Gemini Introduction 03:26
    • 118: 32 Gemini Interface Review 10:02
    • 119: 33 Gemini Version General Reminder 01:04
    • 120: 34 Gemini – 2.0 Flash Version 07:10
    • 121: 35 Gemini – 2.0 Flash Thinking Experimental 08:26
    • 122: 36 Gemini – 2.0 Flash Experimental with Apps 07:52
    • 123: 37 Gemini – Deep Research 06:46
    • 124: 38 Gemini – Personalization (Experimental) 04:31
    • 125: 39 Operations on Gemini Output 06:25
    • 126: 40 Gemini Visual Analysis 09:44
    • 127: 41 Gemini File Reading 04:45
    • 128: 42 Generating Images with Gemini 09:45
    • 129: 43 Gemini Imagen 3 vs OpenAI DALL·E 06:25
    • 130: 44 Using Gemini AI - First Task - Field Knowledge 09:54
    • 131: 45 Gemini AI – Working with a Dataset Method 1 11:02
    • 132: 46 Gemini AI – Working with a Dataset Method 2 (Colab – Gemini Power) 13:11
    • 133: 49 Claude AI Introduction 07:49
    • 134: 50 Claude Interface Review 10:16
    • 135: 51 Claude 3.7 Sonnet & Normal Mode Lesson 1 07:42
    • 136: 52 Claude 3.7 Sonnet & Normal Mode – Lesson 2 10:03
    • 137: 53 Claude 3.5 Haiku & Extended Mode 07:13
    • 138: 54 Claude 3.5 Sonnet & Claude Opus 05:52
    • 139: 55 Processing Long Texts with Claude – Lesson 1 10:28
    • 140: 56 Processing Long Texts with Claude – Lesson 2 09:13
    • 141: 57 Visual Analysis with Claude 04:57
    • 142: 58 Grok Introduction 03:33
    • 143: 59 Grok Interface Review 11:47
    • 144: 60 Grok 3 Model – Lesson 1 06:16
    • 145: 61 Grok 3 Model – Lesson 2 04:12
    • 146: 62 Grok 2 Model Lesson 1 06:10
    • 147: 63 Grok 2 Model Lesson 2 05:11
    • 148: 64 Grok DeepResearch 12:13
    • 149: 65 Grok – Deeper Research 10:05
    • 150: 66 Grok – Think Mode 09:30
    • 151: 76 Grok – Visual Generation 06:46
    • 152: 77 Grok – File Reading 07:29
    • 153: 78 Grok Visual Analysis 02:43
    • 154: 1 - Installing Anaconda Distribution For Windows 10:35
    • 155: 2 - Installing Anaconda Distribution For Mac 06:17
    • 156: 3 -Anaconda for Linux 14:43
    • 157: 4 - Reviewing The Jupyter Notebook 12:54
    • 158: 5 - Reviewing The Jupyter Lab 11:37
    • 159: 1 Getting to know the dataset using ChatGPT 18:59
    • 160: 2 Getting started with Exploratory Data Analysis(EDA) using ChatGPT 09:01
    • 161: 3 Perform Univariate Analysis using ChatGPT Lesson 1 11:11
    • 162: 4 Perform Univariate Analysis using ChatGPT Lesson 2 09:13
    • 163: 5 Perform Bivariate Analysis using ChatGPT 08:07
    • 164: 6 Perform Multivariate Analysis using ChatGPT 10:27
    • 165: 7 Perform Correlation Analysis using ChatGPT 09:00
    • 166: 8 Prepare data for machine learning model using ChatGPT Lesson 1 03:52
    • 167: 9 Prepare data for machine learning model using ChatGPT Lesson 2 04:44
    • 168: 10 Create a machine learning model using the Linear Regression algorithm with Ch 19:51
    • 169: 11 Develop machine learning model using ChatGPT 07:14
    • 170: 12 Perform Feature Engineering using ChatGPT 03:07
    • 171: 13 Performing Hyperparameter Optimization using ChatGPT 14:42
    • 172: 1 Loading Dataset using ChatGPT 06:54
    • 173: 2 Perform initial analysis on Dataset using ChatGPT 09:59
    • 174: 3 Performing the first operation on the Dataset using ChatGPT 06:37
    • 175: 4 Tackling Missing values using ChatGPT Lesson 1 05:58
    • 176: 5 Tackling Missing values using ChatGPT Lesson 2 12:07
    • 177: 6 Tackling Missing values using ChatGPT Lesson 3 12:24
    • 178: 7 Tackling Missing values using ChatGPT Lesson 4 02:59
    • 179: 8 Performing Bivariate analysis with CatPLot using ChatGPT 13:41
    • 180: 9 Performing Bivariate analysis with KdePLot using ChatGPT 09:37
    • 181: 10 Examining the correlation of variables using ChatGPT Lesson 1 08:50
    • 182: 11 Examining the correlation of variables using ChatGPT Lesson 2 05:33
    • 183: 12 Perform a get_dummies operation using ChatGPT 04:04
    • 184: 13 Prepare for Logistic Regression modeling using ChatGPT 07:45
    • 185: 14 Create a Logistic Regression model using ChatGPT 05:07
    • 186: 15 Examining evaluation metrics on the Logistic Regression model using ChatGPT- 14:30
    • 187: 16 Examining evaluation metrics on the Logistic Regression model using ChatGPT L 05:11
    • 188: 17 Perform a GridSearchCv operation using ChatGPT 10:53
    • 189: 18 Model reconstruction with best parameters using ChatGPT 12:55
    • 190: 2 ChatGPT for Generative AI Introduction 04:22
    • 191: 3 Accessing the Dataset 01:36
    • 192: 4 First Task- Field Knowledge 11:12
    • 193: 5 Continuing with Field Knowledge 05:42
    • 194: 6 Loading the Dataset and Understanding Variables 07:55
    • 195: 7 Delving into the Details of Variables 05:36
    • 196: 8 Let's Perform the First Analysis 06:37
    • 197: 14 Using DeepSeek AI Let's Perform the First Analysis 06:17
    • 198: 9 Updating Variable Names 06:20
    • 199: 10 Examining Missing Values 06:08
    • 200: 11 Examining Unique Values 14:13
    • 201: 15 Using DeepSeek AI- Examining Unique Values 07:26
    • 202: 29 Using Copilot AI - Examining Unique Values 06:29
    • 203: 47 Using Gemini AI – Examining Unique Values 09:44
    • 204: 12 Examining Statistics of Variables Lesson 1 15:15
    • 205: 13 Examining Statistics of Variables Lesson 2 13:11
    • 206: 14 Examining Statistics of Variables Lesson 3 09:19
    • 207: 16 Using DeepSeek AI- Examining Statistics of Variables 13:28
    • 208: 30 Using Copilot AI – Examining Statistics of Variables 12:18
    • 209: 48 Using Gemini AI - Examining Statistics of Variables 08:46
    • 210: 15 Exploratory Data Analysis(EDA) 10:00
    • 211: 16 Categorical Variables(Analysis with Pie Chart) Lesson 1 10:40
    • 212: 17 Categorical Variables(Analysis with Pie Chart) Lesson 2 09:35
    • 213: 18 Categorical Variables(Analysis with Pie Chart) Lesson 3 06:52
    • 214: 19 Categorical Variables(Analysis with Pie Chart) Lesson 4 16:44
    • 215: 20 Categorical Variables(Analysis with Pie Chart) Lesson 5 11:19
    • 216: 21 Importance of Bivariate Analysis in Data Science 07:17
    • 217: 22 Numerical Variables vs Target Variable Lesson 1 06:43
    • 218: 23 Numerical Variables vs Target Variable Lesson 2 09:57
    • 219: 24 Numerical Variables vs Target Variable Lesson 3 08:24
    • 220: 25 Numerical Variables vs Target Variable Lesson 4 03:37
    • 221: 26 Categoric Variables vs Target Variable Lesson 1 03:38
    • 222: 27 Categoric Variables vs Target Variable Lesson 2 05:31
    • 223: 28 Categoric Variables vs Target Variable Lesson 3 05:21
    • 224: 29 Categoric Variables vs Target Variable Lesson 4 04:46
    • 225: 30 Categoric Variables vs Target Variable Lesson 5 05:51
    • 226: 31 Correlation Between Numerical and Categorical Variables and the Target Variab 11:42
    • 227: 32 Correlation Between Numerical and Categorical Variables and the Target Variab 07:57
    • 228: 33 Examining Numeric Variables Among Themselves Lesson 1 06:12
    • 229: 34 Examining Numeric Variables Among Themselves Lesson 2 06:56
    • 230: 35 Numerical Variables - Categorical Variables Lesson 1 18:13
    • 231: 36 Numerical Variables - Categorical Variables Lesson 2 06:01
    • 232: 37 Numerical Variables - Categorical Variables Lesson 3 05:22
    • 233: 38 Numerical Variables - Categorical Variables Lesson 4 05:18
    • 234: 39 Numerical Variables - Categorical Variables Lesson 5 05:48
    • 235: 40 Numerical Variables - Categorical Variables with Swarm Plot Lesson 1 12:33
    • 236: 41 Numerical Variables - Categorical Variables with Swarm Plot Lesson 2 07:19
    • 237: 42 Numerical Variables - Categorical Variables with Swarm Plot Lesson 3 07:01
    • 238: 43 Numerical Variables - Categorical Variables with Swarm Plot Lesson 4 04:35
    • 239: 44 Numerical Variables - Categorical Variables with Swarm Plot Lesson 5 04:31
    • 240: 45 Numerical Variables - Categorical Variables with Swarm Plot Lesson 6 08:48
    • 241: 46 Relationships between variables (Analysis with Heatmap) 08:08
    • 242: 47 Relationships between variables (Analysis with Heatmap) Lesson 2 14:44
    • 243: 48 Preparation for Modeling 05:23
    • 244: 49 Dropping Columns with Low Correlation 05:27
    • 245: 50 Struggling Outliers 09:50
    • 246: 51 Visualizing Outliers Lesson 06:31
    • 247: 52 Visualizing Outliers Lesson 2 04:34
    • 248: 53 Visualizing Outliers Lesson 3 03:52
    • 249: 54 Dealing with Outliers Lesson 1 09:25
    • 250: 55 Dealing with Outliers Lesson 2 13:50
    • 251: 56 Dealing with Outliers Lesson 3 06:02
    • 252: 57 Dealing with Outliers Lesson 4 06:39
    • 253: 58 Dealing with Outliers Lesson 5 10:03
    • 254: 59 Determining Distributions 11:24
    • 255: 60 Determining Distributions of Numeric Variables Lesson 1 06:26
    • 256: 61 Determining Distributions of Numeric Variables Lesson 2 04:06
    • 257: 62 Determining Distributions of Numeric Variables Lesson 3 04:30
    • 258: 63 Determining Distributions of Numeric Variables Lesson 4 08:20
    • 259: 64 Determining Distributions of Numeric Variables Lesson 5 06:54
    • 260: 65 Applying One Hot Encoding Method to Categorical Variables Lesson 1 05:19
    • 261: 66 Applying One Hot Encoding Method to Categorical Variables Lesson 2 02:31
    • 262: 67 Feature Scaling with the RobustScaler Method for Machine Learning Algorithms 03:44
    • 263: 68 Separating Data into Test and Training Set 04:00
    • 264: 69 Logistic Regression Algorithm Lesson 1 06:14
    • 265: 70 Logistic Regression Algorithm Lesson 2 11:16
    • 266: 71 Cross Validation 08:44
    • 267: 72 ROC Curve and Area Under Curve (AUC) 06:56
    • 268: 73 ROC Curve and Area Under Curve (AUC) Lesson 2 05:49
    • 269: 74 Hyperparameter Optimization (with GridSearchCV) 07:33
    • 270: 75 Hyperparameter Tuning for Logistic Regression Model 08:28
    • 271: 76 Decision Tree Algorithm Lesson 1 05:50
    • 272: 77 Decision Tree Algorithm Lesson 2 06:55
    • 273: 78 Support Vector Machine Algorithm Lesson 1 05:34
    • 274: 79 Support Vector Machine Algorithm Lesson 2 06:23
    • 275: 80 Random Forest Algorithm Lesson 1 06:10
    • 276: 81 Random Forest Algorithm Lesson 2 03:37
    • 277: 82 Random Forest Algorithm Lesson 3 05:04
    • 278: 83 Random Forest Algorithm Lesson 4 05:44
    • 279: 84 Project Conclusion 10:36
    • 280: 85 Suggestions and Closing 08:07
    • 281: CompTIA DataX Introduction 07:13
    • 282: Mathematics and Statistics 05:23
    • 283: T-Tests 04:53
    • 284: P-value 06:53
    • 285: Let's Practice- Paired Samples t-Test in Python 06:56
    • 286: Hypothesis Testing 05:17
    • 287: Chi squared 02:50
    • 288: Analysis of Variance (ANOVA) 07:25
    • 289: Let's Practice- ANOVA Test in Python 05:01
    • 290: Let's Practice- Chi-Square Test in Python 04:59
    • 291: Confidence Intervals 02:55
    • 292: Classification vs Regression 03:24
    • 293: Regression Error Metrics 09:49
    • 294: Let's Practice Regression Perfomance Metrics 12:04
    • 295: Classification Error Metrics 18:27
    • 296: Let's Practice Classification Perfomance Metrics 04:36
    • 297: Gini Index 04:46
    • 298: Entropy & Information Gain 04:10
    • 299: Let's Practice- Gini Index, Entropy, Information Gain 05:09
    • 300: ROC AUC 08:52
    • 301: AIC BIC 05:55
    • 302: Correlation Coefficients 06:28
    • 303: Let’s Practice- Correlation Coefficients 17:54
    • 304: Central Limit Theorem 05:10
    • 305: Law of Large Number 02:52
    • 306: Distributions 07:55
    • 307: Skewness 05:31
    • 308: Kurtosis 05:03
    • 309: Let’s Practice- Distributions, Skewness, Kurtosis Lesson 1 19:07
    • 310: Let’s Practice- Distributions, Skewness, Kurtosis Lesson 2 03:46
    • 311: Let’s Practice- Distributions, Skewness, Kurtosis Lesson 3 03:17
    • 312: Heteroskedasticity vs. Homoskedasticity 05:54
    • 313: Probability Density Function(PDF) 04:12
    • 314: Let’s Practice- Probability Density Function(PDF) 04:18
    • 315: Probability Mass Function(PMF) 03:47
    • 316: Cumulative Distribution Function(CDF) 04:03
    • 317: Let’s Practice- Cumulative Distribution Function(CMF) 06:31
    • 318: Probability 06:41
    • 319: Types of Missingness 06:54
    • 320: Let’s Practice- Types of Missingness 18:46
    • 321: Oversampling 04:59
    • 322: Stratification 03:29
    • 323: Linear Algebra Lesson 1 08:20
    • 324: Linear Algebra Lesson 2 04:32
    • 325: Linear Algebra Lesson 3 05:11
    • 326: Calculus 05:56
    • 327: Time Series Lesson 1 05:02
    • 328: Time Series Lesson 2 04:35
    • 329: Longitudinal Studies 03:42
    • 330: Survival Analysis 06:03
    • 331: Causal Inference 07:57
    • 332: Exploratory Data Analysis(EDA) Method or Process 05:49
    • 333: Univariate Analysis 09:17
    • 334: Let’s Practice- Univariate Analysis 17:23
    • 335: Multivariate Analysis 07:49
    • 336: Let’s Practice- Multivariate Analysis Lesson 1 08:26
    • 337: Let’s Practice- Multivariate Analysis Lesson 2 13:06
    • 338: Let’s Practice- Multivariate Analysis Lesson 3 07:00
    • 339: Let’s Practice- Multivariate Analysis Lesson 4 11:23
    • 340: Let’s Practice- Multivariate Analysis Lesson 5 08:03
    • 341: Let’s Practice- Multivariate Analysis Lesson 6 12:37
    • 342: Identification of Object Behaviors and Attributes NEW 07:10
    • 343: Identification of Object Behaviors and Attributes 07:10
    • 344: Visualization Type(Charts & Graphs) 02:59
    • 345: Box and Whisker Plot 07:47
    • 346: Violin Plot 04:56
    • 347: Scatter Plot & Bar Chart 03:41
    • 348: Line Chart 02:30
    • 349: Histogram – Waterfall 03:28
    • 350: Heatmap & Correlation Plot 04:54
    • 351: Sankey Diagram & Quartile-Quartile(Q-Q) Plot 05:44
    • 352: Density Plot & Scatter Plot Matrix 05:31
    • 353: Feature Type Identification 09:06
    • 354: Let’s Practice- Feature Type Identification 03:36
    • 355: Common Issues Lesson 1 06:01
    • 356: Common Issues Lesson 2 08:10
    • 357: Let’s Practice- Multicollinearity 09:47
    • 358: Feature Engineering 05:21
    • 359: Data Transformation Lesson 1 05:11
    • 360: Let’s Practice- One-Hot Encoding 05:10
    • 361: Data Transformation Lesson 2 05:27
    • 362: Let’s Practice- Pivoting 10:47
    • 363: Geocoding 05:47
    • 364: Scaling 07:25
    • 365: Let’s Practice- Scaling(Robust Scaler) 02:41
    • 366: Standardization 06:40
    • 367: Let’s Practice- Scaling(Standard Scaler) 07:10
    • 368: Additional Data Sources 08:04
    • 369: Let’s Practice- Data Sets 16:03
    • 370: Design and Spesification 09:22
    • 371: Performance Evaluation 08:15
    • 372: Model Selection 05:57
    • 373: Let’s Practice- Hyperparameter Tuning 13:04
    • 374: Requirements Validation 06:14
    • 375: Performance Benchmarking 05:14
    • 376: Specification Testing Results 04:49
    • 377: Final Performance Measures 03:13
    • 378: Satisfy Business Requirements 03:49
    • 379: Effective Data Visualization and Reporting Techniques Lesson 1 05:57
    • 380: Effective Data Visualization and Reporting Techniques Lesson 2 04:41
    • 381: Data Visualization Best Practices and Pitfalls 05:18
    • 382: 101 Chart Accessibility 06:47
    • 383: 102 Data & Model Documantation 06:02
    • 384: 103 Loss Function 03:40
    • 385: 104 Bias-Variance Trade-Off 10:47
    • 386: 105 Variable Feature Selection 03:56
    • 387: 106 Let’s Practice- Multicollineartiy & VIF 06:47
    • 388: 107 Class Imbalance 03:50
    • 389: 108 Regularization 04:16
    • 390: 109 K-Fold Cross Validation 04:18
    • 391: 110 Let’s Practice- K-Fold Cross Validation With Python 06:33
    • 392: 111 The Curse of Dimensionality 07:21
    • 393: 112 - Installing Anaconda Distribution For Windows 10:35
    • 394: 112 Occam's Razor Law of Parsimory 04:24
    • 395: 113 - Installing Anaconda Distribution For Mac 06:17
    • 396: 113 In Sample vs. Out of Sample 04:21
    • 397: 114 -Anaconda for Linux 14:43
    • 398: 114 Interpolation vs. Extrapolation 04:19
    • 399: 115 Ensemble Models 07:13
    • 400: 116 Hyperparameter Tuning 06:24
    • 401: 117 Let’s Practice- Hyperparameter Tuning Lesson 1 10:15
    • 402: 118 Let’s Practice- Hyperparameter Tuning Lesson 2 13:07
    • 403: 119 Classifiers 04:49
    • 404: 120 Recommender Systems Lesson 1 04:58
    • 405: 121 Recommender Systems Lesson 2 04:24
    • 406: 122 Regressors 04:39
    • 407: 123 Embeddings 04:48
    • 408: 124 Post Hoc Model Explainability 05:21
    • 409: 125 Interpretable Model 06:08
    • 410: 126 Model Drift Causes 03:22
    • 411: 127 Data Leakage 03:52
    • 412: 128 Linear Regression Theory Lesson1 07:57
    • 413: 129 Linear Regression Theory Lesson2 04:43
    • 414: 130 Let’s Practice- Linear Regression NEW 19:55
    • 415: 130 Let’s Practice- Linear Regression 20:00
    • 416: 131 Logistic Regression Algorithm Theory 04:39
    • 417: 132 Let’s Practice- Logistic Regression 07:08
    • 418: 133 Linear Discriminant Analysis (LDA) 02:49
    • 419: 134 Quadratic Discriminant Analysis (QDA) 04:00
    • 420: 135 Association Rules 03:18
    • 421: 136 Naive Bayes 03:44
    • 422: 137 Decision Tree Algorithm Theory 09:31
    • 423: 138 Let’s Practice- Decision Tree 06:28
    • 424: 139 Random Forest Algorithm Theory 05:47
    • 425: 140 Let’s Practice- Random Forest 08:22
    • 426: 141 Boosting 03:57
    • 427: 142 Bootstrap Aggregation (Bagging) 03:36
    • 428: 143 Artificial Neural Network Architecture Lesson 1 04:40
    • 429: 144 Artificial Neural Network Architecture Lesson 2 04:38
    • 430: 145 Artificial Neural Network Architecture Lesson 3 05:44
    • 431: 146 Dropout 04:45
    • 432: 147 Batch Normalization 04:06
    • 433: 148 Early Stopping 04:08
    • 434: 149 Schedulers 04:11
    • 435: 150 Back Propagation 04:28
    • 436: 151 Shot-based Learning Techniques 03:52
    • 437: 152 Deep Learning Frameworks 11:52
    • 438: 153 Optimizers 05:21
    • 439: 154 Model Types 09:07
    • 440: 155 K-Means Clustering 04:10
    • 441: 156 Let’s Practice- K-Means Clustering Lesson 1 07:06
    • 442: 157 Let’s Practice- K-Means Clustering Lesson 2 06:50
    • 443: 158 Let’s Practice- K-Means Clustering Lesson 3 06:51
    • 444: 159 Let’s Practice- K-Means Clustering Lesson 4 07:09
    • 445: 160 Hierarchical Clustering Algorithm Theory 04:40
    • 446: 161 Let’s Practice- Hierarchical Clustering Lesson 1 07:51
    • 447: 162 Let’s Practice- Hierarchical Clustering Lesson 2 05:54
    • 448: 163 Density-Based Spatial Clustering of Applications with Noise 04:48
    • 449: 164 Principal Component Analysis(PCA) Theory 08:48
    • 450: 165 Let’s Practice- Principal Component Analysis(PCA) Lesson 1 05:17
    • 451: 166 Let’s Practice- Principal Component Analysis(PCA) Lesson 2 01:56
    • 452: 167 Let’s Practice- Principal Component Analysis(PCA) Lesson 3 07:30
    • 453: 168 t-Distributed Stochastic Neighbor Embedding (t-SNE) 05:10
    • 454: 169 Uniform Manifold Approximation and Projection 05:40
    • 455: 170 K-Nearest Neighbors(KNN) 06:33
    • 456: 171 Let’s Practice- K-Nearest Neighbors(KNN) 08:53
    • 457: 172 Singular Value Decomposition 02:49
    • 458: 173 Compliance, Security, and Privacy 08:55
    • 459: 174 Measures, Metrics, and Key Performance Indicators (KPIs) 04:48
    • 460: 175 Requirements Gathering 04:43
    • 461: 176 Generated Data 06:20
    • 462: 177 Synthetic Data 07:36
    • 463: 178 Commercial Public Data 06:51
    • 464: 179 Infrastructure Requirements 03:32
    • 465: 180 Data Format- Lesson 1 04:45
    • 466: 181 Data Format- Lesson 2 03:38
    • 467: 182 Streaming 02:45
    • 468: 183 Batching 03:35
    • 469: 184 Pipeline Implementation 05:07
    • 470: 185 Orchestration Automation 05:52
    • 471: 186 Persistence 04:31
    • 472: 187 Refresh Cycles 04:20
    • 473: 188 Archiving 04:24
    • 474: 189 Data Lineage 05:41
    • 475: 190 Merging - Combining 05:27
    • 476: 191 Let’s Practice- Merging - Combining Lesson 1 12:38
    • 477: 192 Let’s Practice- Merging - Combining Lesson 2 10:45
    • 478: 193 Let’s Practice- Merging - Combining Lesson 3 05:38
    • 479: 194 Let’s Practice- Merging - Combining Lesson 4 09:44
    • 480: 195 Let’s Practice- Merging - Combining Lesson 5 07:35
    • 481: 196 Let’s Practice- Merging - Combining Lesson 6 11:42
    • 482: 197 Cleaning 06:40
    • 483: 198 Data Errors 04:59
    • 484: 199 Outliers 05:16
    • 485: 200 Let’s Practice- Outliers Lesson 1 08:34
    • 486: 201 Let’s Practice- Outliers Lesson 2 09:58
    • 487: 202 Let’s Practice- Outliers Lesson 3 10:53
    • 488: 203 Let’s Practice- Outliers Lesson 4 08:22
    • 489: 204 Let’s Practice- Outliers Lesson 5 07:50
    • 490: 205 Data Flattening 03:49
    • 491: 206 Imputation Types 05:22
    • 492: 207 Let’s Practice- Imputation Types 18:33
    • 493: 208 Ground Truth Labeling 05:45
    • 494: 209 Data Science Workflow Models 07:22
    • 495: 210 Version Control 07:27
    • 496: 211 Integrated Development Enviroment(IDE) 05:55
    • 497: 212 Dependency Licencing 04:09
    • 498: 213 Access via Application Programming Interface(API) 04:33
    • 499: 214 Process Documentation 04:18
    • 500: 215 Clean Code Methods 04:00
    • 501: 216 Unit Test Writing 03:53
    • 502: 217 Data Replication 03:58
    • 503: 218 Continuous Integration - Continuous Deployment (CI - CD) 04:38
    • 504: 219 Model Deployment 05:19
    • 505: 220 Container Orchestration 04:17
    • 506: 221 Virtualization 05:35
    • 507: 222 Code Isolation 05:19
    • 508: 223 Model Performance Monitoring 04:55
    • 509: 224 Model Validation 04:48
    • 510: 225 Containerization 03:27
    • 511: 226 Cloud Deployment 05:00
    • 512: 227 Cluster Deployment 04:53
    • 513: 228 Hybrid Deployment 03:41
    • 514: 229 Edge Deployment 04:29
    • 515: 230 One-Premises Deployment 04:43
    • 516: 231 Constrained Optimization 08:59
    • 517: 232 Unconstrained Optimization 04:33
    • 518: 233Tokenization -Bag of Words 04:27
    • 519: 234 Word Embeddings 04:32
    • 520: 235 Term Frequency-Inverse Document Frequency (TF-IDF) 02:44
    • 521: 236 Document Term Matrix 04:14
    • 522: 237 Edit Distance 04:29
    • 523: 238 Large Langulage Model 06:19
    • 524: 239 Text Preparation 05:47
    • 525: 240 Topic Modeling 05:31
    • 526: 241 Disambiguation 05:32
    • 527: 242 NLP Applications Lesson 1 05:36
    • 528: 243 NLP Applications Lesson 2 04:15
    • 529: 244 NLP Applications Lesson 3 08:26
    • 530: 245 Optical Character Recognition 03:13
    • 531: 246 Object - Semantic Segmentation 05:35
    • 532: 247 Tracking 05:42
    • 533: 248 Sensor Fusion 06:21
    • 534: 249 Data Augmentation 07:13
    • 535: Graphs Analysis - Graph Theory 06:47
    • 536: Heuristics 06:42
    • 537: Greedy Algorithms 04:56
    • 538: Reinforcement Learning 07:21
    • 539: Event Detection 05:26
    • 540: Fraud Detection 05:44
    • 541: Anomaly Detection 05:41
    • 542: Multimodal Machine Learning 06:29
    • 543: Optimization for Edge Computing 06:12
    • 544: Signal Processing 06:28

Description

You follow a sharp, practical learning sequence designed for maximum impact:

Foundations → Prompt Mastery → Real-World Applications → Advanced Workflows

This structure ensures you don’t just use AI — you control it.

1. Generative AI Foundations: Understand How to Get Results

You start by building a clear mental model of how generative AI tools work and how to interact with them effectively. This phase removes confusion and guesswork so you stop “hoping” for good outputs and start producing them consistently.

You learn:

  • How generative AI systems respond to inputs

  • Why some prompts work and others fail

  • How to think in terms of outputs, constraints, and intent

  • How to structure requests for clarity and accuracy

You are not memorizing theory.
You are learning how to communicate with AI effectively.

2. Prompt Engineering: Turn AI Into a Reliable Tool

Prompting is the core skill that separates average users from powerful users — and this programme treats it that way.

You will learn:

  • Prompt structures that consistently produce high-quality results

  • Instruction-based prompting

  • Role-based prompting

  • Iterative refinement techniques

  • Multi-step prompts

  • Output formatting and control

You won’t just type prompts.
You will design them.

3. Real-World Applications: Use AI Like a Professional

This is where the programme becomes practical and valuable.

You will apply generative AI to real tasks such as:

  • Content creation (articles, scripts, summaries, ideas)

  • Research and information synthesis

  • Productivity workflows

  • Business writing and communication

  • Creative generation (text, visuals, ideas)

  • AI-assisted problem solving

  • Coding assistance and technical support

  • Workflow acceleration and task automation

Everything is hands-on.
Every module produces something tangible.

4. Advanced Workflows: Work Faster, Smarter, Better

Once you are fluent with AI tools, you move into advanced usage that dramatically increases efficiency.

You will learn how to:

  • Chain prompts for complex outcomes

  • Reuse prompt templates

  • Build repeatable AI workflows

  • Adapt AI outputs for different goals

  • Combine AI tools into daily work processes

This is how AI becomes a multiplier, not just a novelty.

Real Practice = Real Capability

This programme is built around doing, not watching.

You will complete:

  • Prompt design exercises

  • Real-world task simulations

  • Productivity challenges

  • Applied AI workflows

  • Mini projects and use-case scenarios

By the end, you won’t be guessing what to type —
you’ll know exactly how to get the result you want.

A Programme Built for Real Advantage

This course is designed for people who want:

  • Faster work

  • Better output

  • Smarter workflows

  • Stronger professional skills

Whether you’re a student, freelancer, professional, creator, or entrepreneur — these skills translate directly into real value.

By the End of This Programme, You Will Be Able To:

  • Use generative AI tools confidently and efficiently

  • Design effective prompts for different goals

  • Create high-quality content and outputs consistently

  • Apply AI to real business and productivity tasks

  • Build repeatable AI workflows

  • Think strategically when using AI tools

  • Gain a practical skill set that improves your career value

This is not just an AI course.

This is your step-by-step path to mastering Generative AI for real-world results — fast, practical, and career-focused.

Who is this course for?

  • Beginners who want to start using generative AI confidently from scratch

  • Professionals who want to increase productivity and work faster with AI tools

  • Freelancers who want to improve output quality and deliver results more efficiently

  • Content creators, writers, marketers and designers who want to leverage AI in daily work

  • Students who want practical AI skills that provide real career value

  • Entrepreneurs and business owners who want to integrate AI into workflows and decision-making

  • Anyone who wants to move beyond “basic AI usage” and use generative AI professionally

Requirements

  • No prior AI experience required

  • No programming knowledge required

  • Basic computer skills are sufficient

  • Internet access and a willingness to practice are enough

  • A curiosity to learn and apply new tools in real scenarios

Career path

  • AI-powered productivity specialist

  • Content creator or writer using AI tools professionally

  • AI-assisted marketing or business operations roles

  • Freelance professional using AI to deliver higher-quality work faster

  • Entry-level AI or automation support roles

  • Professionals integrating AI into existing career paths (tech, business, creative fields)

Questions and answers

There are currently no Q&As for this course. Be the first to ask a question.

Reviews

Currently there are no reviews for this course. Be the first to leave a review.

FAQs

Zopa Bank Limited trading as DivideBuy is authorised by the Prudential Regulation Authority and regulated by the Financial Conduct Authority and the Prudential Regulation Authority, and entered on the Financial Services Register (800542). Zopa Bank Limited (10627575) is incorporated in England & Wales and has its registered office at: Zopa Bank, Level 12, 20 Water Street, Canary Wharf, London E14 5GX. VAT Number 281765280.